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qencode

by com.qencode

fetch_qencode_doc

Read-onlyIdempotent

Read the full content of a Qencode knowledge-base resource by URI.

Works for every URI returned by `search_qencode_docs` — recipes, best
practices, storage, gotchas, error codes, and the schema digest. This
is the tool-based counterpart to the MCP `resources/read` operation,
provided because some MCP clients (notably Claude Desktop) don't expose
`resources/read` to the model directly.

Args:
    uri: a `qencode://...` URI from a `search_qencode_docs` hit.
        Examples:
          - qencode://recipe/hls_abr
          - qencode://docs/best-practices
          - qencode://docs/storage
          - qencode://docs/error-codes
          - qencode://schema/digest

Returns:
    A dict with `uri`, `mime_type`, and `content` (the full markdown or
    JSON, depending on the doc). On unknown URI, returns
    `{"error": "...", "available_uris": [...]}` listing the URIs you can
    try instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
uriNo
errorNo
titleNo
contentNo
mime_typeNo
available_urisNo

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds valuable detail: the exact return structure (`uri`, `mime_type`, `content`) and the error handling for unknown URIs, including the `available_uris` fallback. This goes beyond the annotations without contradicting them.

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 with a front-loaded main sentence, a brief context paragraph, and clearly labeled Args/Returns sections. Every sentence carries meaning — even the Claude Desktop note justifies the tool's existence. Nothing is redundant or filler.

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 single-parameter read tool with a strong annotation set and an output schema, the description covers all necessary ground: what it does, what the argument looks like, what the response contains, and error behavior. It is complete enough for an agent to select and invoke it correctly without further clarification.

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?

Although the schema itself has zero description coverage, the Args section fully explains the `uri` parameter: it must be a `qencode://...` URI from a `search_qencode_docs` hit, with five concrete examples. This is exactly the kind of semantic enrichment the schema lacks, and it directly guides correct invocation.

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 clearly states 'Read the full content of a Qencode knowledge-base resource by URI' — a specific verb and resource. It further distinguishes itself from siblings by noting it works for every URI returned by `search_qencode_docs` and that it's the tool-based counterpart to the MCP `resources/read` operation, removing any ambiguity.

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 explicitly ties usage to URIs from `search_qencode_docs`, provides multiple example URIs, and explains the rationale for its existence (clients that lack `resources/read`). It doesn't enumerate negative cases or alternative tools, but the context makes when-to-use clear.

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