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4 months ago
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In today’s rapidly evolving AI landscape, integrating multiple tooling systems has become both an opportunity and a challenge. As AI-assisted code generation becomes more common, software engineers increasingly find themselves debugging not their own logic — but the glue between AI models, APIs, and libraries moving at breakneck speed.
In this post, I’ll walk through a real-world example: integrating a remote MCP (Managed Control Plane) server with Slack, using LangChain as the LLM tool orchestration layer. The goal? Let AI post a message to a Slack channel through the MCP service — seamlessly.
But what unfolded instead was a deep dive into broken tool interfaces, async/sync mismatches, and versioning hell.
Build a pipeline that:
- Uses LangChain to invoke tools asynchronously
- Talks to a remote MCP server
- Sends structured Slack messages to a channel
- Is driven by an LLM agent (natural language → function call)
🧱 First Attempt: Async Confusion
langchain_tool = Tool(name=tool_def.name,
description=tool_def.description,
func=async_tool_func, # ❌ async function incorrectly passed to `func`
)
Problem: LangChain’s Tool class distinguishes between:
- func → sync implementations
- coroutine → async implementations
This usage leads to unpredictable runtime failures when agents try to execute the tool.
✅ Correction: Use coroutine=
langchain_tool = Tool(name=tool_def.name,
description=tool_def.description,
func=sync_not_implemented,
coroutine=async_tool_func,
)
This fixed the function binding, but triggered a new issue…
🧨 Schema Mismatch from LLM Output
When the LLM agent invoked the tool, it generated malformed arguments:
{"channel_id": "{\"channel_id\": \"<channel-id>\", \"text\": \"hello world\"}"
}
Expected:
{"channel_id": "<channel-id>",
"text": "hello world"
}
Result: Pydantic validation errors. The channel_id was parsed as a serialized dictionary, not a string.
Attempts to fix this via “sanitization” layers led to fragile logic and growing technical debt.
🔄 The Breakthrough: bind_tools to the Rescue
Eventually, the solution emerged: bind tools directly to the LLM, giving it better context of the expected input schema.
self.tools.append(Tool(name=tool_def.name,
description=tool_def.description,
func=sync_not_implemented,
coroutine=async_tool_func,
))
# ✅ Bind tools to the LLM itself
self.llm = base_llm.bind_tools(self.tools)
Now the LLM had direct awareness of tool schemas, and tool calls worked reliably — even across networked layers like MCP → Slack (after few more interface issues)
- Don’t trust the AI’s first draft — It may be syntactically valid, but semantically wrong.
- Use coroutine for async – Don’t shoehorn async into func=.
- Avoid band-aid fixes for schema errors — Fix the root cause by giving LLMs tool context.
- Use bind_tools – It enforces structure and reduces guesswork for the model.
Integrating LLMs with external systems isn’t about raw code anymore. It’s about handling ambiguity in tool schemas, rapidly changing APIs, and reconciling mismatches between AI-generated code and runtime behavior.
The real win isn’t just “getting Slack to post a message.” It’s building a system resilient enough to survive the next LangChain or tool schema update — without 10 failed iterations.
Follow me for more engineering deep dives into LLM tooling, AI integration patterns, and lessons from the bleeding edge.
