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LiLBrain

LiLBrain

by MangoByteLabs
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Python
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lilbrain_hotspots

Identify code hotspots by ranking functions with the highest fan-in and fan-out, helping you focus on the most connected code that impacts understanding and maintenance.

Instructions

Most connected functions (highest fan-in + fan-out).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

NameRequiredDescriptionDefault
nNoNumber of hotspots (default 20)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It does disclose the core selection metric (fan-in + fan-out), which is the main behavioral trait. However, it does not mention output format, pagination, limitations, or whether the operation has side effects; for a read-only analysis tool this is a moderate gap.

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, focused sentence with no filler. It front-loads the key idea and every word contributes to understanding what the tool does.

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 simple tool with one optional parameter and no output schema, the description gives the essential answer: the tool returns the most connected functions by fan-in + fan-out. The schema handles the count and default. The main missing piece is routing relative to sibling tools, but that is captured under usage guidelines.

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 schema covers 100% of the single optional parameter n, including its default value, so the description does not need to add parameter details. The baseline of 3 applies because the description provides no extra semantic value beyond the schema.

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 clearly identifies the resource (functions) and the selection criterion (highest fan-in + fan-out), which distinguishes it from sibling analysis tools like lilbrain_complexity or lilbrain_callers. However, it lacks an explicit verb such as 'list' or 'show', so it reads as a noun phrase rather than a full action statement.

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 states what the tool computes but gives no guidance on when to use it instead of related siblings such as lilbrain_complexity, lilbrain_callers, or lilbrain_impact. No selection context, exclusions, or alternative routing is provided, leaving the agent to infer appropriate usage.

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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