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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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lilbrain_diff

Identify changed functions, compute blast radius, and assess risk level between any two Git refs to prioritize code review.

Instructions

Git-aware graph diff: changed functions, blast radius, risk level between any two refs.

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_diff"
  }
}

Input Schema

NameRequiredDescriptionDefault
baseNoBase git ref (default HEAD~1)
headNoHead git ref (default HEAD)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of explaining behavior. It communicates that this is an analytical diff operation and lists output dimensions, but it leaves implied that the operation is read-only and does not describe the shape of the result or any environment requirements.

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?

One concise sentence leads with the core operation, then lists the valuable output categories. There is no redundant wording or filler.

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?

The tool is simple: two optional, well-documented parameters and no required inputs. The description explains the main outcome at a useful level, though without an output schema, a bit more detail about the exact return format would make it fully complete.

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 coverage is 100%, with base and head already described and defaulted. The description's 'between any two refs' aligns with these parameters but does not add meaningful detail beyond the schema, so the baseline of 3 applies.

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 specific operation—git-aware graph diff—and names its key outputs: changed functions, blast radius, and risk level. This is clear enough to identify the tool's purpose, though it does not explicitly differentiate it from nearby siblings like lilbrain_impact or lilbrain_pr_review.

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

Usage Guidelines4/5

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

The phrase 'between any two refs' gives clear context for when the tool applies: comparing arbitrary git references. It does not provide exclusions or name alternative tools, so it falls just short of a 5.

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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