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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

lilbrain_subsystem

Explore a subsystem to retrieve its files, top functions, and languages for quick codebase understanding.

Instructions

Deep dive into a subsystem: files, top functions, languages.

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_subsystem"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesSubsystem 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It does reveal that the tool returns files, top functions, and languages, which suggests a read-only introspection operation. However, it does not specify output format, what 'top functions' means, or confirm behavior beyond the vague 'deep dive' language.

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, front-loaded sentence with no filler. It states the action, the resource, and the expected output categories compactly, making it easy for an agent to parse quickly.

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 one-parameter tool, this is minimally viable, but there is no output schema, so the return value is only vaguely described. It also fails to disambiguate against similar sibling tools, leaving the agent to guess when this tool is the right choice among 23 alternatives.

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%: the only parameter, 'name', is described as 'Subsystem name'. The tool description adds no extra meaning to that parameter beyond establishing the subsystem context, so the baseline score of 3 is appropriate.

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 uses a specific verb/resource pair ('Deep dive into a subsystem') and enumerates the main content categories returned: files, top functions, languages. It is clear about what the tool does, though it does not explicitly differentiate itself from sibling tools like lilbrain_overview or lilbrain_architecture.

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?

There is no guidance on when to use this tool versus the many siblings. 'Deep dive' implies detailed subsystem inspection, but there are no prerequisites, exclusions, or alternative routing, so an agent must infer when this tool is preferred over lilbrain_overview, lilbrain_file, or lilbrain_architecture.

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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