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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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lilbrain_callers

Trace a function's dependencies by listing which code calls it and what it calls, enabling instant codebase navigation and comprehension.

Instructions

Call graph for a function: who calls it and what it calls.

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_callers"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesFunction name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does convey the read-only analytic nature of the tool and the two directions of the call graph, but it does not explain return format, whether the graph is direct or transitive, or any limitations.

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?

The description is a single compact sentence with no filler. It front-loads the core concept and efficiently conveys both callers and callees without wasted words.

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

Completeness3/5

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

For a simple one-parameter tool, the description is minimally adequate, but the absence of annotations, output schema, and usage guidance leaves important context to inference. With many closely related siblings, an agent would benefit from more direction about what this tool uniquely provides.

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?

The input schema already describes 'name' as 'Function name' with 100% coverage. The description adds only that the target is a function and that callers/callees are returned, which provides context but no additional parameter format or qualification requirements.

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?

The description states a clear analytic resource: a call graph for a function, including both callers ('who calls it') and callees ('what it calls'). It is understandable but doesn't explicitly differentiate from sibling tools like lilbrain_trace or lilbrain_dataflow.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool instead of the many related siblings like lilbrain_overview, lilbrain_impact, or lilbrain_trace. It implies a function-focused call graph use case but provides no exclusion criteria or alternative selection hints.

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

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