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Why Syrin Exists

MCP servers break in production in ways that are hard to debug. The failures are silent, non-obvious, and often only surface under real agent traffic. Here are the patterns Syrin is built to catch.

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Failure 1: The LLM Picks the Wrong Tool

You have two tools: 
@mcp.tool()
def get_user(user_id: str) -> User:
    """Get user information."""

@mcp.tool()
def fetch_user(user_id: str) -> User:
    """Get user data."""
Both have similar names, identical schemas, and vague descriptions. The LLM picks one at random. Sometimes it works. Sometimes it does not. You cannot reproduce the failure because the choice depends on the model, the context window, and the prompt. What Syrin catches: E110: Tool Ambiguity — two tools match the same intent. Detected by syrin analyse before any tool executes.

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Failure 2: The Tool Returns Too Much Data

Your search_documents tool returns all matching results. In testing with 5 documents, it works fine. In production with 50,000 documents, the response is 12MB of JSON. The LLM context overflows, the agent loses track of what it was doing, and the session silently degrades. What Syrin catches: E301: Output Explosion — tool output exceeds the declared size limit. Detected by syrin test during sandboxed execution.

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Failure 3: The Tool Has Hidden Side Effects

Your generate_report tool is supposed to read data and return a summary. But the implementation also writes a cache file to disk. In a sandbox or CI environment, that write fails. In production, it corrupts state. Nobody notices until a user reports inconsistent results. What Syrin catches: E500: Side Effect Detected — tool attempted filesystem writes when its contract says side_effects: none. Detected by syrin test with behavioral observation.

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Failure 4: The Tool Description Is Missing

@mcp.tool()
def process(data: str) -> dict:
    return transform(data)
No description. No parameter documentation. The LLM has no idea what this tool does, when to use it, or what data should look like. It either ignores the tool entirely or passes garbage. What Syrin catches: E101: Missing Tool Description and E102: Underspecified Required Input. Detected by syrin analyse — static analysis, no execution needed.

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Failure 5: Tool Chain Breaks Silently

Tool A returns a user ID as a string ("123"). Tool B expects a user ID as a number (123). The LLM chains them together. Tool B receives "123", silently coerces it or throws an obscure error. The agent continues with broken state. What Syrin catches: E103: Type Mismatch — output type incompatible with downstream input type. Detected by syrin analyse through dependency analysis.

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Failure 6: The LLM Loops Forever

The LLM calls get_status, gets a partial result, calls get_status again with the same parameters, gets the same result. Repeat indefinitely. Your logs look “fine” — every call succeeds. But the agent is burning tokens in a loop and never making progress. What Syrin catches: W300: High Entropy Output and E107: Circular Dependency flag the structural conditions that lead to loops. syrin dev lets you watch the loop happen in real time and interrupt it.

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Why Logging Does Not Solve This

After a production failure, you have logs. They show:
  • Requests and responses
  • Error messages
  • Timestamps
What they do not show:
  • Why the LLM chose that tool over another one
  • Whether the tool’s contract matches its actual behavior
  • Whether the same input produces the same output every time
  • Where the execution diverged from what you expected
Syrin records execution as ordered, typed events — not log lines. Each event has a category, a sequence number, and a typed payload. You can replay the exact sequence of decisions that led to a failure. Syrin Analyse Warnings

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What Changes When Syrin Is Present

Without SyrinWith Syrin
Tool issues found in productionFound by syrin analyse before deployment
Side effects discovered by usersCaught by syrin test in sandbox
LLM behavior is a black boxsyrin dev shows every proposal and decision
Debugging means reading logsEvents are structured, typed, and replayable
CI checks code, not tool contractssyrin analyse --ci and syrin test --ci run on every PR

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When You Need Syrin

  • You are building an MCP server and want to catch contract issues before they reach users
  • You are integrating multiple LLM providers and want to compare how they interact with the same tools
  • You are running MCP tools in production and need CI-level validation
  • You are debugging why an agent behaves differently across runs

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