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qencode

by com.qencode

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. (Some MCP clients also expose these URIs
via `resources/read`, but `fetch_qencode_doc` works in every client.)

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

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context about returning a ranked list of URIs with summaries, and the 'call this first' heuristic. It does not contradict annotations and provides useful workflow insight beyond the defined hints.

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 well-structured: a concise opening sentence, return format, usage trigger, parameter details, and return explanation. Every sentence adds value, with no fluff. The use of a bulleted Args section and explicit examples makes it efficient to parse.

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?

The description fully covers the tool's purpose, input parameters, return format, and next actions. It integrates with the existing MCP ecosystem (fetch_qencode_doc) and explains the workflow. The output schema is complemented by the explicit return structure ('A dict with hits...'), making it complete for an agent to invoke correctly.

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%, but the description fully compensates. It explains 'query' as free-text search with concrete examples ('hls widevine ezdrm', 'thumbnail sprite') and 'limit' with its default value (8). This adds substantial meaning beyond the raw schema.

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 states a specific verb ('Search') and resource ('Qencode knowledge base (recipes + reference docs)'). It clearly distinguishes itself from siblings by positioning it as the first step for recipe discovery, contrasting with fetch_qencode_doc for reading full content.

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 guidance is given: 'Call this first whenever you're unsure which recipe applies.' It also directs users to fetch_qencode_doc for reading full content and notes the alternative resources/read path, providing clear when-to-use and when-to-use-other-tool context.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools are clearly distinct (list_buckets vs list_objects, search vs fetch docs). Minor overlap exists between transcode_video and start_encode2_raw (both submit jobs) and between get_job_status and get_job_status_detailed, but the descriptions explicitly state when to use which, making misselection unlikely.

Naming Consistency4/5

Names overwhelmingly follow verb_noun (create_bucket, list_buckets, get_download_url, transcode_video). A few deviations like start_encode2_raw, wait_for_job, and download_url_to_bucket break the pure pattern, but the convention is still easily predictable.

Tool Count5/5

13 tools is well-scoped for a video encoding platform: bucket management, transcoding submission/status/wait, result retrieval, and docs search/read. Each tool serves a clear purpose without redundancy or bloat.

Completeness4/5

The set covers the main lifecycle: create bucket, ingest via copy, transcode (two entry points), poll status, fetch result, and generate download URLs. Missing cancel/delete operations for jobs and buckets are notable but not critical for core workflows, and the docs tools help fill knowledge gaps.

Resources