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qencode

by com.qencode
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search_qencode_docs

Read-onlyIdempotent

Search the Qencode knowledge base (recipes + reference docs).

Returns a ranked list of MCP resource URIs that match the query, each with
a short summary. Call this first whenever you're unsure which recipe
applies.

To read the full content of any URI returned here, call
`fetch_qencode_doc(uri)` next.

Args:
    query: free-text search — output type, codec, DRM provider, feature name,
        etc. (e.g. "hls widevine ezdrm", "thumbnail sprite", "stitching",
        "speech to text translation")
    limit: max number of hits to return. Default 8.

Returns:
    A dict with `hits`, each containing `uri`, `title`, `summary`, `score`.
    Pass `uri` to `fetch_qencode_doc` to read the full markdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsYes
queryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds meaningful context beyond that: results are ranked, each hit carries a short summary, and the URI must be handed to fetch_qencode_doc to get full content. It does not add much about limits or failure modes, but the annotation bar is already met.

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 core action and the follow-up step; the Args/Returns block is structured and readable. It is slightly long, and the Returns paragraph partially duplicates the output schema, but every sentence still conveys actionable detail.

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 two-parameter search tool with a rich output schema, the description covers purpose, when to call it, the routing to fetch_qencode_doc, param semantics, and result shape. Nothing an agent needs to invoke it correctly or act on the results 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 carries the full burden and does so: it explains that query is free-text covering output type, codec, DRM provider, or feature name, with four concrete example queries, and gives the semantics and default (8) of limit. This is much richer than the bare schema titles.

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 the Qencode knowledge base (recipes + reference docs)') and clarifies the unit of return (MCP resource URIs). It is clearly distinguishable from the sibling fetch_qencode_doc, which is named as the follow-up step.

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?

Explicitly prescribes when to use it ('Call this first whenever you're unsure which recipe applies') and names the alternative plus the transition condition ('To read the full content of any URI returned here, call fetch_qencode_doc(uri) next'). Routing is fully specified with no inference required.

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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