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by Lolaplex
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search_docs

Search technical docs for API specs, stack rules, model capabilities, and playbooks via header-aware BM25 ranking for AI coding agents.

Instructions

Search hard technical facts, API specs, stack rules, model capabilities, and playbooks using header-aware BM25 ranking.

Args: query: Keywords, function signatures, syntax, or technical questions (e.g. 'runes $state', 'HTTPException', 'coolify postgres port', 'cdp tab focus') category: Target category ('stacks', 'models', 'apis', 'platforms', 'custom', or specific docset name, or 'all' to search everything). top_k: Number of relevant sections to return (default 4).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
categoryNoall

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.44.1
    • addedInput schema / properties / category
      Added value: +{
+  "default": "all",
+  "title": "Category",
+  "type": "string"
+}
    • removedInput schema / properties / docset
      Removed value: -{
-  "default": "all",
-  "title": "Docset",
-  "type": "string"
-}
  2. First observedv0.42.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the ranking algorithm and that results are ranked sections, which is useful, but says nothing about permissions, result ordering guarantees, or failure behavior; the existence of an output schema removes the need to describe return shape.

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?

Front-loaded opening sentence followed by a compact Args block. The parenthetical query examples add real value rather than filler, and nothing is redundant, though the examples are slightly verbose.

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?

All three parameters are documented in the description, which is essential given 0% schema coverage, and the output schema covers return values. Guidance on selecting this tool over siblings is the only notable gap.

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?

Schema description coverage is 0%, so the description must compensate, and it does: it enumerates the accepted category values ('stacks', 'models', 'apis', 'platforms', 'custom', docset name, 'all'), explains top_k as 'number of relevant sections to return (default 4)', and gives concrete query examples. Format details like query syntax are left implicit.

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?

States a specific verb (Search) and the resource/content type (hard technical facts, API specs, stack rules, model capabilities, playbooks), and adds the ranking mechanism (header-aware BM25). It clearly reads as a retrieval tool distinct from write/list/delete siblings, though it never names those siblings explicitly.

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?

Usage is implied by the content scope ('hard technical facts, API specs...') and by the query examples, but there is no explicit guidance on when to prefer this over get_doc or get_model_playbook, nor any when-not conditions.

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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