a technique to standardise communication between AI purposes and exterior instruments or information sources. This standardisation helps to scale back the variety of integrations wanted (from N*M to N+M):
- You should use community-built MCP servers while you want widespread performance, saving time and avoiding the necessity to reinvent the wheel each time.
- You too can expose your individual instruments and assets, making them obtainable for others to make use of.
In my earlier article, we constructed the analytics toolbox (a group of instruments that may automate your day-to-day routine). We constructed an MCP server and used its capabilities with current shoppers like MCP Inspector or Claude Desktop.
Now, we need to use these instruments immediately in our AI purposes. To do this, let’s construct our personal MCP consumer. We are going to write pretty low-level code, which may even offer you a clearer image of how instruments like Claude Code work together with MCP below the hood.
Moreover, I want to implement the function that’s at present (July 2025) lacking from Claude Desktop: the flexibility for the LLM to robotically verify whether or not it has an appropriate immediate template for the duty at hand and use it. Proper now, it’s important to decide the template manually, which isn’t very handy.
As a bonus, I may even share a high-level implementation utilizing the smolagents framework, which is good for situations while you work solely with MCP instruments and don’t want a lot customisation.
MCP protocol overview
Right here’s a fast recap of the MCP to make sure we’re on the identical web page. MCP is a protocol developed by Anthropic to standardise the way in which LLMs work together with the surface world.
It follows a client-server structure and consists of three foremost elements:
- Host is the user-facing software.
- MCP consumer is a element inside the host that establishes a one-to-one reference to the server and communicates utilizing messages outlined by the MCP protocol.
- MCP server exposes capabilities equivalent to immediate templates, assets and instruments.
Since we’ve already applied the MCP server earlier than, this time we’ll deal with constructing the MCP consumer. We are going to begin with a comparatively easy implementation and later add the flexibility to dynamically choose immediate templates on the fly.
You could find the complete code on GitHub.
Constructing the MCP chatbot
Let’s start with the preliminary setup: we’ll load the Anthropic API key from a config file and modify Python’s asyncio occasion loop to help nested occasion loops.
# Load configuration and setting
with open('../../config.json') as f:
config = json.load(f)
os.environ["ANTHROPIC_API_KEY"] = config['ANTHROPIC_API_KEY']
nest_asyncio.apply()Let’s begin by constructing a skeleton of our program to get a transparent image of the appliance’s high-level structure.
async def foremost():
"""Fundamental entry level for the MCP ChatBot software."""
chatbot = MCP_ChatBot()
attempt:
await chatbot.connect_to_servers()
await chatbot.chat_loop()
lastly:
await chatbot.cleanup()
if __name__ == "__main__":
asyncio.run(foremost())We begin by creating an occasion of the MCP_ChatBot class. The chatbot begins by discovering obtainable MCP capabilities (iterating via all configured MCP servers, establishing connections and requesting their lists of capabilities).
As soon as connections are arrange, we’ll initialise an infinite loop the place the chatbot listens to the consumer queries, calls instruments when wanted and continues this cycle till the method is stopped manually.
Lastly, we’ll carry out a cleanup step to shut all open connections.
Let’s now stroll via every stage in additional element.
Initialising the ChatBot class
Let’s begin by creating the category and defining the __init__ methodology. The principle fields of the ChatBot class are:
exit_stackmanages the lifecycle of a number of async threads (connections to MCP servers), making certain that every one connections shall be closed appropriately, even when we face an error throughout execution. This logic is applied within thecleanupperform.anthropicis a consumer for Anthropic API used to ship messages to LLM.available_toolsandavailable_promptsare the lists of instruments and prompts uncovered by all MCP servers we’re related to.periodsis a mapping of instruments, prompts and assets to their respective MCP periods. This permits the chatbot to route requests to the proper MCP server when the LLM selects a selected instrument.
class MCP_ChatBot:
"""
MCP (Mannequin Context Protocol) ChatBot that connects to a number of MCP servers
and supplies a conversational interface utilizing Anthropic's Claude.
Helps instruments, prompts, and assets from related MCP servers.
"""
def __init__(self):
self.exit_stack = AsyncExitStack()
self.anthropic = Anthropic() # Consumer for Anthropic API
self.available_tools = [] # Instruments from all related servers
self.available_prompts = [] # Prompts from all related servers
self.periods = {} # Maps instrument/immediate/useful resource names to MCP periods
async def cleanup(self):
"""Clear up assets and shut all connections."""
await self.exit_stack.aclose()Connecting to servers
The primary activity for our chatbot is to provoke connections with all configured MCP servers and uncover what capabilities we are able to use.
The checklist of MCP servers that our agent can hook up with is outlined within the server_config.json file. I’ve arrange connections with three MCP servers:
- analyst_toolkit is my implementation of the on a regular basis analytical instruments we mentioned within the earlier article,
- Filesystem permits the agent to work with recordsdata,
- Fetch helps LLMs retrieve the content material of webpages and convert it from HTML to markdown for higher readability.
{
"mcpServers": {
"analyst_toolkit": {
"command": "uv",
"args": [
"--directory",
"/path/to/github/mcp-analyst-toolkit/src/mcp_server",
"run",
"server.py"
],
"env": {
"GITHUB_TOKEN": "your_github_token"
}
},
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/marie/Desktop",
"/Users/marie/Documents/github"
]
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}First, we’ll learn the config file, parse it after which join to every listed server.
async def connect_to_servers(self):
"""Load server configuration and hook up with all configured MCP servers."""
attempt:
with open("server_config.json", "r") as file:
information = json.load(file)
servers = information.get("mcpServers", {})
for server_name, server_config in servers.gadgets():
await self.connect_to_server(server_name, server_config)
besides Exception as e:
print(f"Error loading server config: {e}")
traceback.print_exc()
elevateFor every server, we carry out a number of steps to determine the connection:
- On the transport degree, we launch the MCP server as a stdio course of and get streams for sending and receiving messages.
- On the session degree, we create a
ClientSessionincorporating the streams, after which we carry out the MCP handshake by callinginitializemethodology. - We registered each the session and transport objects within the context supervisor
exit_stackto make sure that all connections shall be closed correctly in the long run. - The final step is to register server capabilities. We wrapped this performance right into a separate perform, and we’ll focus on it shortly.
async def connect_to_server(self, server_name, server_config):
"""Hook up with a single MCP server and register its capabilities."""
attempt:
server_params = StdioServerParameters(**server_config)
stdio_transport = await self.exit_stack.enter_async_context(
stdio_client(server_params)
)
learn, write = stdio_transport
session = await self.exit_stack.enter_async_context(
ClientSession(learn, write)
)
await session.initialize()
await self._register_server_capabilities(session, server_name)
besides Exception as e:
print(f"Error connecting to {server_name}: {e}")
traceback.print_exc()Registering capabilities includes iterating over all of the instruments, prompts and assets retrieved from the session. Consequently, we replace the interior variables periods (mapping between assets and a specific session between the MCP consumer and server), available_prompts and available_tools.
async def _register_server_capabilities(self, session, server_name):
"""Register instruments, prompts and assets from a single server."""
capabilities = [
("tools", session.list_tools, self._register_tools),
("prompts", session.list_prompts, self._register_prompts),
("resources", session.list_resources, self._register_resources)
]
for capability_name, list_method, register_method in capabilities:
attempt:
response = await list_method()
await register_method(response, session)
besides Exception as e:
print(f"Server {server_name} does not help {capability_name}: {e}")
async def _register_tools(self, response, session):
"""Register instruments from server response."""
for instrument in response.instruments:
self.periods[tool.name] = session
self.available_tools.append({
"title": instrument.title,
"description": instrument.description,
"input_schema": instrument.inputSchema
})
async def _register_prompts(self, response, session):
"""Register prompts from server response."""
if response and response.prompts:
for immediate in response.prompts:
self.periods[prompt.name] = session
self.available_prompts.append({
"title": immediate.title,
"description": immediate.description,
"arguments": immediate.arguments
})
async def _register_resources(self, response, session):
"""Register assets from server response."""
if response and response.assets:
for useful resource in response.assets:
resource_uri = str(useful resource.uri)
self.periods[resource_uri] = sessionBy the tip of this stage, our MCP_ChatBot object has every thing it wants to begin interacting with customers:
- connections to all configured MCP servers are established,
- all prompts, assets and instruments are registered, together with descriptions wanted for LLM to grasp learn how to use these capabilities,
- mappings between these assets and their respective periods are saved, so we all know precisely the place to ship every request.
Chat loop
So, it’s time to begin our chat with customers by creating the chat_loop perform.
We are going to first share all of the obtainable instructions with the consumer:
- itemizing assets, instruments and prompts
- executing a instrument name
- viewing a useful resource
- utilizing a immediate template
- quitting the chat (it’s essential to have a transparent solution to exit the infinite loop).
After that, we’ll enter an infinite loop the place, primarily based on consumer enter, we’ll execute the suitable motion: whether or not it’s one of many instructions above or making a request to the LLM.
async def chat_loop(self):
"""Fundamental interactive chat loop with command processing."""
print("nMCP Chatbot Began!")
print("Instructions:")
print(" give up - Exit the chatbot")
print(" @intervals - Present obtainable changelog intervals")
print(" @ - View changelog for particular interval")
print(" /instruments - Listing obtainable instruments")
print(" /instrument - Execute a instrument with arguments")
print(" /prompts - Listing obtainable prompts")
print(" /immediate - Execute a immediate with arguments")
whereas True:
attempt:
question = enter("nQuery: ").strip()
if not question:
proceed
if question.decrease() == 'give up':
break
# Deal with useful resource requests (@command)
if question.startswith('@'):
interval = question[1:]
resource_uri = "changelog://intervals" if interval == "intervals" else f"changelog://{interval}"
await self.get_resource(resource_uri)
proceed
# Deal with slash instructions
if question.startswith('/'):
components = self._parse_command_arguments(question)
if not components:
proceed
command = components[0].decrease()
if command == '/instruments':
await self.list_tools()
elif command == '/instrument':
if len(components) < 2:
print("Utilization: /instrument ")
proceed
tool_name = components[1]
args = self._parse_prompt_arguments(components[2:])
await self.execute_tool(tool_name, args)
elif command == '/prompts':
await self.list_prompts()
elif command == '/immediate':
if len(components) < 2:
print("Utilization: /immediate ")
proceed
prompt_name = components[1]
args = self._parse_prompt_arguments(components[2:])
await self.execute_prompt(prompt_name, args)
else:
print(f"Unknown command: {command}")
proceed
# Course of common queries
await self.process_query(question)
besides Exception as e:
print(f"nError in chat loop: {e}")
traceback.print_exc() There are a bunch of helper features to parse arguments and return the lists of obtainable instruments and prompts we registered earlier. Because it’s pretty simple, I gained’t go into a lot element right here. You’ll be able to verify the complete code in case you are .
As an alternative, let’s dive deeper into how the interactions between the MCP consumer and server work in several situations.
When working with assets, we use the self.periods mapping to seek out the suitable session (with a fallback possibility if wanted) after which use that session to learn the useful resource.
async def get_resource(self, resource_uri):
"""Retrieve and show content material from an MCP useful resource."""
session = self.periods.get(resource_uri)
# Fallback: discover any session that handles this useful resource kind
if not session and resource_uri.startswith("changelog://"):
session = subsequent(
(sess for uri, sess in self.periods.gadgets()
if uri.startswith("changelog://")),
None
)
if not session:
print(f"Useful resource '{resource_uri}' not discovered.")
return
attempt:
outcome = await session.read_resource(uri=resource_uri)
if outcome and outcome.contents:
print(f"nResource: {resource_uri}")
print("Content material:")
print(outcome.contents[0].textual content)
else:
print("No content material obtainable.")
besides Exception as e:
print(f"Error studying useful resource: {e}")
traceback.print_exc()To execute a instrument, we comply with the same course of: begin by discovering the session after which use it to name the instrument, passing its title and arguments.
async def execute_tool(self, tool_name, args):
"""Execute an MCP instrument immediately with given arguments."""
session = self.periods.get(tool_name)
if not session:
print(f"Software '{tool_name}' not discovered.")
return
attempt:
outcome = await session.call_tool(tool_name, arguments=args)
print(f"nTool '{tool_name}' outcome:")
print(outcome.content material)
besides Exception as e:
print(f"Error executing instrument: {e}")
traceback.print_exc()No shock right here. The identical method works for executing the immediate.
async def execute_prompt(self, prompt_name, args):
"""Execute an MCP immediate with given arguments and course of the outcome."""
session = self.periods.get(prompt_name)
if not session:
print(f"Immediate '{prompt_name}' not discovered.")
return
attempt:
outcome = await session.get_prompt(prompt_name, arguments=args)
if outcome and outcome.messages:
prompt_content = outcome.messages[0].content material
textual content = self._extract_prompt_text(prompt_content)
print(f"nExecuting immediate '{prompt_name}'...")
await self.process_query(textual content)
besides Exception as e:
print(f"Error executing immediate: {e}")
traceback.print_exc()The one main use case we haven’t coated but is dealing with a common, free-form enter from a consumer (not one in all particular instructions).
On this case, we ship the preliminary request to the LLM first, then we parse the output, defining whether or not there are any instrument calls. If instrument calls are current, we execute them. In any other case, we exit the infinite loop and return the reply to the consumer.
async def process_query(self, question):
"""Course of a consumer question via Anthropic's Claude, dealing with instrument calls iteratively."""
messages = [{'role': 'user', 'content': query}]
whereas True:
response = self.anthropic.messages.create(
max_tokens=2024,
mannequin='claude-3-7-sonnet-20250219',
instruments=self.available_tools,
messages=messages
)
assistant_content = []
has_tool_use = False
for content material in response.content material:
if content material.kind == 'textual content':
print(content material.textual content)
assistant_content.append(content material)
elif content material.kind == 'tool_use':
has_tool_use = True
assistant_content.append(content material)
messages.append({'function': 'assistant', 'content material': assistant_content})
# Execute the instrument name
session = self.periods.get(content material.title)
if not session:
print(f"Software '{content material.title}' not discovered.")
break
outcome = await session.call_tool(content material.title, arguments=content material.enter)
messages.append({
"function": "consumer",
"content material": [{
"type": "tool_result",
"tool_use_id": content.id,
"content": result.content
}]
})
if not has_tool_use:
breakSo, we have now now totally coated how the MCP chatbot truly works below the hood. Now, it’s time to check it in motion. You’ll be able to run it from the command line interface with the next command.
python mcp_client_example_base.pyWhenever you run the chatbot, you’ll first see the next introduction message outlining potential choices:
MCP Chatbot Began!
Instructions:
give up - Exit the chatbot
@intervals - Present obtainable changelog intervals
@ - View changelog for particular interval
/instruments - Listing obtainable instruments
/instrument - Execute a instrument with arguments
/prompts - Listing obtainable prompts
/immediate - Execute a immediate with arguments From there, you possibly can check out totally different instructions, for instance,
- name the instrument to checklist the databases obtainable within the DB
- checklist all obtainable prompts
- use the immediate template, calling it like this
/immediate sql_query_prompt query=”What number of prospects did we have now in Might 2024?”.
Lastly, I can end your chat by typing give up.
Question: /instrument list_databases
[07/02/25 18:27:28] INFO Processing request of kind CallToolRequest server.py:619
Software 'list_databases' outcome:
[TextContent(type='text', text='INFORMATION_SCHEMAndatasetsndefaultnecommercenecommerce_dbninformation_schemansystemn', annotations=None, meta=None)]
Question: /prompts
Obtainable prompts:
- sql_query_prompt: Create a SQL question immediate
Arguments:
- query
Question: /immediate sql_query_prompt query="What number of prospects did we have now in Might 2024?"
[07/02/25 18:28:21] INFO Processing request of kind GetPromptRequest server.py:619
Executing immediate 'sql_query_prompt'...
I am going to create a SQL question to seek out the variety of prospects in Might 2024.
[07/02/25 18:28:25] INFO Processing request of kind CallToolRequest server.py:619
Based mostly on the question outcomes, here is the ultimate SQL question:
```sql
choose uniqExact(user_id) as customer_count
from ecommerce.periods
the place toStartOfMonth(action_date) = '2024-05-01'
format TabSeparatedWithNames
```
Question: /instrument execute_sql_query question="choose uniqExact(user_id) as customer_count from ecommerce.periods the place toStartOfMonth(action_date) = '2024-05-01' format TabSeparatedWithNames"
I am going to aid you execute this SQL question to get the distinctive buyer depend for Might 2024. Let me run this for you.
[07/02/25 18:30:09] INFO Processing request of kind CallToolRequest server.py:619
The question has been executed efficiently. The outcomes present that there have been 246,852 distinctive prospects (distinctive user_ids) in Might 2024 primarily based on the ecommerce.periods desk.
Question: give upAppears to be like fairly cool! Our primary model is working effectively! Now, it’s time to take it one step additional and make our chatbot smarter by instructing it to recommend related prompts on the fly primarily based on buyer enter.
Immediate recommendations
In follow, suggesting immediate templates that greatest match the consumer’s activity will be extremely useful. Proper now, customers of our chatbot have to both already find out about obtainable prompts or no less than be curious sufficient to discover them on their very own to learn from what we’ve constructed. By including a immediate recommendations function, we are able to do that discovery for our customers and make our chatbot considerably extra handy and user-friendly.
Let’s brainstorm methods so as to add this performance. I might method this function within the following manner:
Consider the relevance of the prompts utilizing the LLM. Iterate via all obtainable immediate templates and, for every one, assess whether or not the immediate is an effective match for the consumer’s question.
Counsel an identical immediate to the consumer. If we discovered the related immediate template, share it with the consumer and ask whether or not they want to execute it.
Merge the immediate template with the consumer enter. If the consumer accepts, mix the chosen immediate with the unique question. Since immediate templates have placeholders, we would want the LLM to fill them in. As soon as we’ve merged the immediate template with the consumer’s question, we’ll have an up to date message able to ship to the LLM.
We are going to add this logic to the process_query perform. Due to our modular design, it’s fairly simple so as to add this enhancement with out disrupting the remainder of the code.
Let’s begin by implementing a perform to seek out essentially the most related immediate template. We are going to use the LLM to guage every immediate and assign it a relevance rating from 0 to five. After that, we’ll filter out any prompts with a rating of two or decrease and return solely essentially the most related one (the one with the very best relevance rating among the many remaining outcomes).
async def _find_matching_prompt(self, question):
"""Discover a matching immediate for the given question utilizing LLM analysis."""
if not self.available_prompts:
return None
# Use LLM to guage immediate relevance
prompt_scores = []
for immediate in self.available_prompts:
# Create analysis immediate for the LLM
evaluation_prompt = f"""
You're an knowledgeable at evaluating whether or not a immediate template is related for a consumer question.
Consumer Question: "{question}"
Immediate Template:
- Identify: {immediate['name']}
- Description: {immediate['description']}
Price the relevance of this immediate template for the consumer question on a scale of 0-5:
- 0: Fully irrelevant
- 1: Barely related
- 2: Considerably related
- 3: Reasonably related
- 4: Extremely related
- 5: Excellent match
Contemplate:
- Does the immediate template tackle the consumer's intent?
- Would utilizing this immediate template present a greater response than a generic question?
- Are the matters and context aligned?
Reply with solely a single quantity (0-5) and no different textual content.
"""
attempt:
response = self.anthropic.messages.create(
max_tokens=10,
mannequin='claude-3-7-sonnet-20250219',
messages=[{'role': 'user', 'content': evaluation_prompt}]
)
# Extract the rating from the response
score_text = response.content material[0].textual content.strip()
rating = int(score_text)
if rating >= 3: # Solely think about prompts with rating >= 3
prompt_scores.append((immediate, rating))
besides Exception as e:
print(f"Error evaluating immediate {immediate['name']}: {e}")
proceed
# Return the immediate with the very best rating
if prompt_scores:
best_prompt, best_score = max(prompt_scores, key=lambda x: x[1])
return best_prompt
return NoneThe subsequent perform we have to implement is one that mixes the chosen immediate template with the consumer enter. We are going to depend on the LLM to intelligently mix them, filling all placeholders as wanted.
async def _combine_prompt_with_query(self, prompt_name, user_query):
"""Use LLM to mix immediate template with consumer question."""
# First, get the immediate template content material
session = self.periods.get(prompt_name)
if not session:
print(f"Immediate '{prompt_name}' not discovered.")
return None
attempt:
# Discover the immediate definition to get its arguments
prompt_def = None
for immediate in self.available_prompts:
if immediate['name'] == prompt_name:
prompt_def = immediate
break
# Put together arguments for the immediate template
args = {}
if prompt_def and prompt_def.get('arguments'):
for arg in prompt_def['arguments']:
arg_name = arg.title if hasattr(arg, 'title') else arg.get('title', '')
if arg_name:
# Use placeholder format for arguments
args[arg_name] = '<' + str(arg_name) + '>'
# Get the immediate template with arguments
outcome = await session.get_prompt(prompt_name, arguments=args)
if not outcome or not outcome.messages:
print(f"Couldn't retrieve immediate template for '{prompt_name}'")
return None
prompt_content = outcome.messages[0].content material
prompt_text = self._extract_prompt_text(prompt_content)
# Create mixture immediate for the LLM
combination_prompt = f"""
You're an knowledgeable at combining immediate templates with consumer queries to create optimized prompts.
Authentic Consumer Question: "{user_query}"
Immediate Template:
{prompt_text}
Your activity:
1. Analyze the consumer's question and the immediate template
2. Mix them intelligently to create a single, coherent immediate
3. Make sure the consumer's particular query/request is addressed inside the context of the template
4. Preserve the construction and intent of the template whereas incorporating the consumer's question
Reply with solely the mixed immediate textual content, no explanations or extra textual content.
"""
response = self.anthropic.messages.create(
max_tokens=2048,
mannequin='claude-3-7-sonnet-20250219',
messages=[{'role': 'user', 'content': combination_prompt}]
)
return response.content material[0].textual content.strip()
besides Exception as e:
print(f"Error combining immediate with question: {e}")
return NoneThen, we’ll merely replace the process_query logic to verify for matching prompts, ask the consumer for affirmation and determine which message to ship to the LLM.
async def process_query(self, question):
"""Course of a consumer question via Anthropic's Claude, dealing with instrument calls iteratively."""
# Test if there is a matching immediate first
matching_prompt = await self._find_matching_prompt(question)
if matching_prompt:
print(f"Discovered matching immediate: {matching_prompt['name']}")
print(f"Description: {matching_prompt['description']}")
# Ask consumer in the event that they need to use the immediate template
use_prompt = enter("Would you want to make use of this immediate template? (y/n): ").strip().decrease()
if use_prompt == 'y' or use_prompt == 'sure':
print("Combining immediate template along with your question...")
# Use LLM to mix immediate template with consumer question
combined_prompt = await self._combine_prompt_with_query(matching_prompt['name'], question)
if combined_prompt:
print(f"Mixed immediate created. Processing...")
# Course of the mixed immediate as an alternative of the unique question
messages = [{'role': 'user', 'content': combined_prompt}]
else:
print("Failed to mix immediate template. Utilizing authentic question.")
messages = [{'role': 'user', 'content': query}]
else:
# Use authentic question if consumer does not need to use the immediate
messages = [{'role': 'user', 'content': query}]
else:
# Course of the unique question if no matching immediate discovered
messages = [{'role': 'user', 'content': query}]
# print(messages)
# Course of the ultimate question (both authentic or mixed)
whereas True:
response = self.anthropic.messages.create(
max_tokens=2024,
mannequin='claude-3-7-sonnet-20250219',
instruments=self.available_tools,
messages=messages
)
assistant_content = []
has_tool_use = False
for content material in response.content material:
if content material.kind == 'textual content':
print(content material.textual content)
assistant_content.append(content material)
elif content material.kind == 'tool_use':
has_tool_use = True
assistant_content.append(content material)
messages.append({'function': 'assistant', 'content material': assistant_content})
# Log instrument name info
print(f"n[TOOL CALL] Software: {content material.title}")
print(f"[TOOL CALL] Arguments: {json.dumps(content material.enter, indent=2)}")
# Execute the instrument name
session = self.periods.get(content material.title)
if not session:
print(f"Software '{content material.title}' not discovered.")
break
outcome = await session.call_tool(content material.title, arguments=content material.enter)
# Log instrument outcome
print(f"[TOOL RESULT] Software: {content material.title}")
print(f"[TOOL RESULT] Content material: {outcome.content material}")
messages.append({
"function": "consumer",
"content material": [{
"type": "tool_result",
"tool_use_id": content.id,
"content": result.content
}]
})
if not has_tool_use:
breakNow, let’s check our up to date model with a query about our information. Excitingly, the chatbot was capable of finding the precise immediate and use it to seek out the precise reply.
Question: What number of prospects did we have now in Might 2024?
Discovered matching immediate: sql_query_prompt
Description: Create a SQL question immediate
Would you want to make use of this immediate template? (y/n): y
Combining immediate template along with your question...
[07/05/25 14:38:58] INFO Processing request of kind GetPromptRequest server.py:619
Mixed immediate created. Processing...
I am going to write a question to depend distinctive prospects who had periods in Might 2024. Since it is a enterprise metric, I am going to exclude fraudulent periods.
[TOOL CALL] Software: execute_sql_query
[TOOL CALL] Arguments: {
"question": "/* Depend distinct customers with non-fraudulent periods in Might 2024n Utilizing uniqExact for exact consumer countn Filtering for Might 2024 utilizing toStartOfMonth and including date vary */nSELECT n uniqExactIf(s.user_id, s.is_fraud = 0) AS active_customers_countnFROM ecommerce.periods snWHERE toStartOfMonth(action_date) = toDate('2024-05-01')nFORMAT TabSeparatedWithNames"
}
[07/05/25 14:39:17] INFO Processing request of kind CallToolRequest server.py:619
[TOOL RESULT] Software: execute_sql_query
[TOOL RESULT] Content material: [TextContent(type='text', text='active_customers_countn245287n', annotations=None, meta=None)]
The question exhibits we had 245,287 distinctive prospects with reliable (non-fraudulent) periods in Might 2024. This is a breakdown of why I wrote the question this manner:
1. Used uniqExactIf() to get exact depend of distinctive customers whereas excluding fraudulent periods in a single step
2. Used toStartOfMonth() to make sure we seize all days in Might 2024
3. Specified the date format correctly with toDate('2024-05-01')
4. Used TabSeparatedWithNames format as required
5. Offered a significant column alias
Would you wish to see any variations of this evaluation, equivalent to together with fraudulent periods or breaking down the numbers by nation?It’s at all times a good suggestion to check unfavourable examples as effectively. On this case, the chatbot behaves as anticipated and doesn’t recommend an SQL-related immediate when given an unrelated query.
Question: How are you?
I ought to notice that I am an AI assistant centered on serving to you're employed with the obtainable instruments, which embody executing SQL queries, getting database/desk info, and accessing GitHub PR information. I haven't got a instrument particularly for responding to private questions.
I will help you:
- Question a ClickHouse database
- Listing databases and describe tables
- Get details about GitHub Pull Requests
What would you wish to find out about these areas?Now that our chatbot is up and working, we’re able to wrap issues up.
BONUS: fast and simple MCP consumer with smolagents
We’ve checked out low-level code that allows constructing extremely customised MCP shoppers, however many use instances require solely primary performance. So, I made a decision to share with you a fast and simple implementation for situations while you want simply the instruments. We are going to use one in all my favorite agent frameworks — smolagents from HuggingFace (I’ve mentioned this framework intimately in my earlier article).
# wanted imports
from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel, VisitWebpageTool, ToolCallingAgent, ToolCollection
from mcp import StdioServerParameters
import json
import os
# setting OpenAI APIKey
with open('../../config.json') as f:
config = json.hundreds(f.learn())
os.environ["OPENAI_API_KEY"] = config['OPENAI_API_KEY']
# defining the LLM
mannequin = LiteLLMModel(
model_id="openai/gpt-4o-mini",
max_tokens=2048
)
# configuration for the MCP server
server_parameters = StdioServerParameters(
command="uv",
args=[
"--directory",
"/path/to/github/mcp-analyst-toolkit/src/mcp_server",
"run",
"server.py"
],
env={"GITHUB_TOKEN": "github_"},
)
# immediate
CLICKHOUSE_PROMPT_TEMPLATE = """
You're a senior information analyst with greater than 10 years of expertise writing advanced SQL queries, particularly optimized for ClickHouse to reply consumer questions.
## Database Schema
You're working with an e-commerce analytics database containing the next tables:
### Desk: ecommerce.customers
**Description:** Buyer info for the web store
**Major Key:** user_id
**Fields:**
- user_id (Int64) - Distinctive buyer identifier (e.g., 1000004, 3000004)
- nation (String) - Buyer's nation of residence (e.g., "Netherlands", "United Kingdom")
- is_active (Int8) - Buyer standing: 1 = lively, 0 = inactive
- age (Int32) - Buyer age in full years (e.g., 31, 72)
### Desk: ecommerce.periods
**Description:** Consumer session information and transaction information
**Major Key:** session_id
**Overseas Key:** user_id (references ecommerce.customers.user_id)
**Fields:**
- user_id (Int64) - Buyer identifier linking to customers desk (e.g., 1000004, 3000004)
- session_id (Int64) - Distinctive session identifier (e.g., 106, 1023)
- action_date (Date) - Session begin date (e.g., "2021-01-03", "2024-12-02")
- session_duration (Int32) - Session length in seconds (e.g., 125, 49)
- os (String) - Working system used (e.g., "Home windows", "Android", "iOS", "MacOS")
- browser (String) - Browser used (e.g., "Chrome", "Safari", "Firefox", "Edge")
- is_fraud (Int8) - Fraud indicator: 1 = fraudulent session, 0 = reliable
- income (Float64) - Buy quantity in USD (0.0 for non-purchase periods, >0 for purchases)
## ClickHouse-Particular Pointers
1. **Use ClickHouse-optimized features:**
- uniqExact() for exact distinctive counts
- uniqExactIf() for conditional distinctive counts
- quantile() features for percentiles
- Date features: toStartOfMonth(), toStartOfYear(), right this moment()
2. **Question formatting necessities:**
- All the time finish queries with "format TabSeparatedWithNames"
- Use significant column aliases
- Use correct JOIN syntax when combining tables
- Wrap date literals in quotes (e.g., '2024-01-01')
3. **Efficiency concerns:**
- Use applicable WHERE clauses to filter information
- Think about using HAVING for post-aggregation filtering
- Use LIMIT when discovering high/backside outcomes
4. **Knowledge interpretation:**
- income > 0 signifies a purchase order session
- income = 0 signifies a shopping session with out buy
- is_fraud = 1 periods ought to sometimes be excluded from enterprise metrics until particularly analyzing fraud
## Response Format
Present solely the SQL question as your reply. Embody temporary reasoning in feedback if the question logic is advanced.
## Examples
**Query:** What number of prospects made buy in December 2024?
**Reply:** choose uniqExact(user_id) as prospects from ecommerce.periods the place toStartOfMonth(action_date) = '2024-12-01' and income > 0 format TabSeparatedWithNames
**Query:** What was the fraud fee in 2023, expressed as a share?
**Reply:** choose 100 * uniqExactIf(user_id, is_fraud = 1) / uniqExact(user_id) as fraud_rate from ecommerce.periods the place toStartOfYear(action_date) = '2023-01-01' format TabSeparatedWithNames
**Query:** What was the share of customers utilizing Home windows yesterday?
**Reply:** choose 100 * uniqExactIf(user_id, os = 'Home windows') / uniqExact(user_id) as windows_share from ecommerce.periods the place action_date = right this moment() - 1 format TabSeparatedWithNames
**Query:** What was the income from Dutch customers aged 55 and older in December 2024?
**Reply:** choose sum(s.income) as total_revenue from ecommerce.periods as s interior be part of ecommerce.customers as u on s.user_id = u.user_id the place u.nation = 'Netherlands' and u.age >= 55 and toStartOfMonth(s.action_date) = '2024-12-01' format TabSeparatedWithNames
**Query:** What are the median and interquartile vary (IQR) of buy income for every nation?
**Reply:** choose nation, median(income) as median_revenue, quantile(0.25)(income) as q25_revenue, quantile(0.75)(income) as q75_revenue from ecommerce.periods as s interior be part of ecommerce.customers as u on u.user_id = s.user_id the place income > 0 group by nation format TabSeparatedWithNames
**Query:** What's the common variety of days between the primary session and the primary buy for customers who made no less than one buy?
**Reply:** choose avg(first_purchase - first_action_date) as avg_days_to_purchase from (choose user_id, min(action_date) as first_action_date, minIf(action_date, income > 0) as first_purchase, max(income) as max_revenue from ecommerce.periods group by user_id) the place max_revenue > 0 format TabSeparatedWithNames
**Query:** What's the variety of periods in December 2024, damaged down by working methods, together with the totals?
**Reply:** choose os, uniqExact(session_id) as session_count from ecommerce.periods the place toStartOfMonth(action_date) = '2024-12-01' group by os with totals format TabSeparatedWithNames
**Query:** Do we have now prospects who used a number of browsers throughout 2024? If that's the case, please calculate the variety of prospects for every mixture of browsers.
**Reply:** choose browsers, depend(*) as customer_count from (choose user_id, arrayStringConcat(arraySort(groupArray(distinct browser)), ', ') as browsers from ecommerce.periods the place toStartOfYear(action_date) = '2024-01-01' group by user_id) group by browsers order by customer_count desc format TabSeparatedWithNames
**Query:** Which browser has the very best share of fraud customers?
**Reply:** choose browser, 100 * uniqExactIf(user_id, is_fraud = 1) / uniqExact(user_id) as fraud_rate from ecommerce.periods group by browser order by fraud_rate desc restrict 1 format TabSeparatedWithNames
**Query:** Which nation had the very best variety of first-time customers in 2024?
**Reply:** choose nation, depend(distinct user_id) as new_users from (choose user_id, min(action_date) as first_date from ecommerce.periods group by user_id having toStartOfYear(first_date) = '2024-01-01') as t interior be part of ecommerce.customers as u on t.user_id = u.user_id group by nation order by new_users desc restrict 1 format TabSeparatedWithNames
---
**Your Activity:** Utilizing all of the offered info above, write a ClickHouse SQL question to reply the next buyer query:
{query}
"""
with ToolCollection.from_mcp(server_parameters, trust_remote_code=True) as tool_collection:
agent = ToolCallingAgent(instruments=[*tool_collection.tools], mannequin=mannequin)
immediate = CLICKHOUSE_PROMPT_TEMPLATE.format(
query = 'What number of prospects did we have now in Might 2024?'
)
response = agent.run(immediate) Consequently, we obtained the proper reply.
In case you don’t want a lot customisation or integration with prompts and assets, this implementation is certainly the way in which to go.
Abstract
On this article, we constructed a chatbot that integrates with MCP servers and leverages all the advantages of standardisation to entry instruments, prompts, and assets seamlessly.
We began with a primary implementation able to itemizing and accessing MCP capabilities. Then, we enhanced our chatbot with a wise function that implies related immediate templates to customers primarily based on their enter. This makes our product extra intuitive and user-friendly, particularly for customers unfamiliar with the whole library of obtainable prompts.
To implement our chatbot, we used comparatively low-level code, providing you with a greater understanding of how the MCP protocol works below the hood and what occurs while you use AI instruments like Claude Desktop or Cursor.
As a bonus, we additionally mentioned the smolagents implementation that permits you to rapidly deploy an MCP consumer built-in with instruments.
Thanks for studying. I hope this text was insightful. Bear in mind Einstein’s recommendation: “The essential factor is to not cease questioning. Curiosity has its personal cause for current.” Might your curiosity lead you to your subsequent nice perception.
Reference
This text is impressed by the “MCP: Construct Wealthy-Context AI Apps with Anthropic” quick course from DeepLearning.AI.
