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lilbrain_impact

Assess the blast radius of changing a function: identify affected callers, subsystems, and risk level to make informed refactoring decisions.

Instructions

Blast radius: if you change this function, what breaks? Affected callers, subsystems, risk level.

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_impact"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes	Function to analyze
`depth`	No	Max caller depth (default 5)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 30, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations provided, the description must carry the behavioral disclosure burden. It does reveal that the tool computes affected callers, subsystems, and risk level, which is meaningful. However, it does not disclose whether this is purely read-only, how depth affects results, or any limitations of the analysis.

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, well-constructed sentence that immediately communicates the tool's purpose. The 'Blast radius' framing is efficient and memorable, and the listed outputs are front-loaded without any wasted words.

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?

Given there is no output schema, the description usefully names the key return categories: affected callers, subsystems, and risk level. It does not describe return structure or detailed operational behavior, but for a selection-and-invocation context the description is largely sufficient, especially with the schema documenting the depth parameter.

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 covers 100% of parameter descriptions: 'Function to analyze' for name and 'Max caller depth (default 5)' for depth. The tool description does not add extra semantics beyond the schema, so the baseline score of 3 applies.

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

Purpose5/5

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

The description clearly identifies the tool's purpose: analyzing blast radius when changing a function. It explicitly names the affected outputs (callers, subsystems, risk level), which distinguishes it from sibling tools like lilbrain_callers that likely only show direct callers.

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 usage for change-impact analysis ('if you change this function, what breaks?'), but it does not explicitly state when to choose this over alternatives or mention exclusions. No sibling tool is named as a comparison, leaving some inference required.

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