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LiLBrain

LiLBrain

by MangoByteLabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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lilbrain_architecture

Map codebase architecture to identify subsystems, cross-subsystem dependencies, and language mix. Use this overview to understand structural relationships and guide refactoring.

Instructions

Architecture: subsystems, cross-subsystem dependencies, language mix.

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_architecture"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

C2.6/5.0
Behavior1/5

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

No annotations are present, so the description must carry the behavioral burden. It only names a topic and does not disclose whether the tool returns a report, list, diagram, or performs any action. This is effectively transparent about nothing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The wording is very compact and front-loaded, with no filler words. It is more of a labeled fragment than a full sentence, but it is efficient and scannable for a zero-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema and no annotations, so the description should clarify what the agent will get. It only lists content areas and does not explain the return format, size, or how the information is presented, leaving important invocation context missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the input schema is empty, so there are no parameter semantics for the description to add. Applying the 0-parameter baseline, the description does not need to explain parameters.

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

Purpose3/5

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

The description identifies the broad domain ('Architecture') and lists the topics it covers: subsystems, cross-subsystem dependencies, and language mix. However, it lacks a main verb and does not clearly distinguish itself from sibling tools such as lilbrain_subsystem, lilbrain_overview, or lilbrain_dataflow.

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 gives no guidance on when to use this tool versus alternatives, and no exclusions are stated. It implies an architecture-focused purpose but never explains how it differs from the many related sibling tools.

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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