Skip to main content
Glama
BlackRaptorAI-Labs

VibeCTX

search

Query all cached library docs at once, grouped by library, to find relevant sections. Use when unsure which library covers a concept or to see which dependencies document it. Cache-only and offline.

Instructions

Search ALL cached library docs at once and get the best sections grouped by library — use this when you do not know which library owns a concept ("how do I stream a response to the client" could be Next.js, the AI SDK or Hono), or to find out which of your dependencies documents something. Use get_docs instead when you already know the library. Cache-only and offline by design: it never fetches, so it searches exactly the libraries already cached (the response says which, and how to cache the rest with warm_project). Each group opens with a Source line stating when that copy was fetched, whether it is fresh or stale, and whether the entry is curated or auto-resolved. For a library whose docs are an index of links rather than the documentation itself, this only searches that index — get_docs on the same library also follows its links into the real pages, which this tool does not do (the response names any index-only library it searched).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat you are looking for, in plain words — e.g. "server-sent events streaming"
librariesNoRestrict the search to these libraries, by name or alias (default: every cached library)
maxTokensNoApproximate response budget (default 4000, max 200000), shared across all libraries

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden and does so richly: cache-only/offline by design, never fetches, searches only already-cached libraries, response names which libraries were searched, and each group includes a Source line with fetch time, freshness, and curated vs auto-resolved provenance. It also discloses a real limitation (index-only libraries are searched only as an index, unlike get_docs).

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 with the primary use case and then layered with routing, behavioral constraints, and return-shape notes; every sentence carries information. It is dense and runs long as a single block, so it is efficient but not maximally scannable.

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?

With no output schema and no annotations, the description compensates by describing the response structure (grouped sections, Source lines, freshness/curation, index-only notices) and the offline/cache boundary. An agent has everything needed to call it correctly and interpret the result.

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%, so the schema already documents query, libraries, and maxTokens with defaults and limits. The description adds only oblique parameter context (that searching is confined to cached libraries, which relates to the libraries param) and no format or semantics beyond the schema, making the baseline 3 appropriate.

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?

States a specific verb and resource ('Search ALL cached library docs at once... grouped by library') and immediately distinguishes itself from get_docs. An agent can tell exactly what this tool returns versus a single-library lookup without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use ('when you do not know which library owns a concept') with a concrete example, explicit when-not ('Use get_docs instead when you already know the library'), and names warm_project as the path to broaden coverage. Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.