LiLBrain

by MangoByteLabs

Overview Schema Source Related Servers Discussions Admin

Python

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lilbrain_function

Resolve codebase questions by looking up any function name to get signature, location, callers, callees, and language.

Instructions

Look up any function by name. Returns signature, location, callers, callees, language.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>"
    },
    "name": "lilbrain_function"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes	Function name (exact or partial)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 30, 2026

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the return contents, but it does not confirm the operation is read-only, how name matching behaves beyond the schema's 'exact or partial', or whether results are bounded in any way.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, dense sentence communicates the operation, the input, and all relevant output aspects. There is no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter lookup tool with no output schema, the description covers the essential input and return fields. It is slightly incomplete only because it does not compare itself with the many sibling lookup tools, leaving the agent to infer the boundary between function lookup and search/overview tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single 'name' parameter. The description adds little beyond the schema, only reinforcing that the lookup is by function name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb ('Look up'), a specific resource ('any function by name'), and enumerates the output fields (signature, location, callers, callees, language). It is distinguishable from the more specialized sibling tools like lilbrain_callers, though it does not explicitly name that distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used when you have a function name and want comprehensive function-level metadata. However, it does not explicitly state when to prefer lilbrain_function over lilbrain_callers, lilbrain_search, or lilbrain_file, nor does it provide exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.