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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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lilbrain_complexity_velocity

Analyze recent git history to detect complexity changes and identify commits that modified complex code, enabling targeted code review.

Instructions

Track complexity changes over recent git history. Shows which commits touched complex code.

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_complexity_velocity"
  }
}

Input Schema

NameRequiredDescriptionDefault
n_commitsNoNumber of commits to analyze (default 10)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says the tool tracks and shows complexity-related commit information, but it does not explicitly state that the operation is read-only, whether a git repository is required, what complexity metric is used, or what the output looks like.

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?

Two short sentences with no filler. The core purpose is front-loaded, and the second sentence adds a concrete sense of the output without repeating the tool name or the schema.

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?

The tool is simple: one optional parameter, no output schema, and no annotations. The description is enough for an agent to understand when it might be useful and to invoke it with defaults, but it omits details about the return format, the meaning of 'complexity', and any prerequisites like a git repository.

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%, and the only parameter, n_commits, is already described as 'Number of commits to analyze (default 10)'. The tool description adds no additional parameter semantics, which matches the baseline for fully covered schemas.

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 action and resource: it tracks complexity changes and shows which commits touched complex code. It is not a tautology and the git-history scope makes its purpose understandable, though it does not explicitly contrast itself with siblings like lilbrain_complexity or lilbrain_hotspots.

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 intended use is implied: use this tool to see complexity changes across recent commits. However, there is no explicit when-to-use guidance and no mention of alternatives or when not to use it, so the agent must infer usage from the purpose statement.

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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