rogue-blender-mcp

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search_api_docs

Read-only

Search the Blender Python API reference full-text to find relevant documentation sections. Get ranked hits with surrounding context and section paths.

Instructions

Full-text search over the bundled Blender Python API reference.

Returns a ranked list of hits. Each hit has:

path: file path relative to the bundled docs.
text: the matching paragraph plus context paragraphs on either side.
breadcrumb: the section path containing the hit (Section > Sub-section > ...).
index: the hit's position in the result list.
score: a relevance score; higher is better.

The query is tokenised on whitespace and matched case-insensitively. Every token must appear somewhere in the paragraph body, the file path, or an enclosing section title - in any order. Common English stop-words (the, a, how, to, ...) are dropped, so natural phrasings like "how to bake" work as expected. Regular expressions are not supported.

Use context to pull more surrounding paragraphs into each hit (symmetric, default 0). Use index with the position of a previous hit (same query) to get that hit alone with its text widened to its enclosing section.

Read-only; consults bundled RST files only.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`index`	No
`query`	Yes
`context`	No
`max_results`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 20, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description states 'Read-only; consults bundled RST files only' and discloses detailed matching semantics: whitespace tokenization, case-insensitivity, token placement requirements, stop-word removal, and rejection of regex. It also explains the meaning of index and context, giving the agent a clear model of how the tool behaves.

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 dense but every sentence serves a purpose: result structure, query semantics, parameter usage, and read-only status. It is well-organized with bulleted fields and clear separations between matching behavior and parameter guidance.

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

Completeness5/5

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

For a search tool with four parameters and an output schema, the description covers return field semantics, query syntax, stop words, regex unsupported, context behavior, index behavior, and read-only access. Nothing needed for safe and correct invocation is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must, and does, carry the full burden. It explains query tokenization and matching, defines context as symmetric surrounding paragraphs with default 0, and clarifies index as a way to retrieve a previous hit alone with widened text. Only max_results is left to its self-explanatory name, but it has a clear default.

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 opens with a specific verb and resource: 'Full-text search over the bundled Blender Python API reference.' It clearly distinguishes itself from siblings like search_manual_docs and get_python_api_docs by naming the API reference as its target rather than the manual or a doc retrieval operation.

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 description provides clear context on when to use the tool: any full-text search over the bundled Python API reference. It explains natural query phrasings, stop-word dropping, and the lack of regex support, but it does not explicitly say 'use search_manual_docs instead when searching the manual' or otherwise state exclusions.

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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