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lilbrain_diagram

Generate Mermaid or D2 architecture diagrams from codebase structure. Specify a target subsystem or function to map dependencies and call chains into visual diagrams.

Instructions

Auto-generate Mermaid or D2 architecture diagrams. Target: "architecture", subsystem name, or function name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNo"mermaid" (default) or "d2"
targetNo"architecture", a subsystem name, or a function name (default: architecture)
Install Server

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations available, the description carries the burden of behavioral disclosure. It does state the core behavior: automatically generating Mermaid or D2 architecture diagrams. But it does not clarify whether the output is raw diagram source text, a rendered artifact, or how invalid or missing target names are handled, so the behavior is only partially transparent.

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 description is short, front-loaded with the action and output formats, and every sentence adds useful information. The second sentence is formatted like parameter documentation rather than prose, which is slightly awkward but not wasteful.

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 simple tool with zero required parameters and a clear purpose, the description is minimally viable. However, with no output schema and no annotations, it leaves open what the return value looks like, how targets are located, and what error cases exist; given the low complexity this is a moderate gap rather than a severe one.

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% for both optional parameters, so the baseline is 3. The description mostly repeats the schema's target semantics rather than adding new meaning, and it offers no additional detail about format values or target resolution beyond the schema's enums and type descriptions.

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 clearly states a specific action ('Auto-generate') and resource (Mermaid or D2 architecture diagrams), and it describes the accepted target scopes: architecture, subsystem, or function. It is not tautological, but it does not explicitly differentiate itself from sibling tools such as lilbrain_architecture or lilbrain_subsystem by naming when to choose it over them.

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 target line gives implied usage guidance: to get a diagram, pick an architecture, subsystem, or function target. However, it never states when to prefer this tool over sibling tools or what conditions make another tool more appropriate, so usage context is only implied.

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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