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list_context_documents

List imported video memories from YouTube or local files, excluding screen recordings. Returns document metadata; use read_context_document to retrieve transcripts.

Instructions

List the user's context documents — their imported/watched-video memory: YouTube videos and local video files they imported into Clipy so agents can read them. This is a SEPARATE library from the user's own screen recordings (use list_recordings/search_recordings for those), with its own ids; a recording id will not resolve here. Returns compact metadata only; call read_context_document for the transcript. Documents can be PARTIAL: frameCount: 0 with segments present is normal and usable — the transcript synced and frame extraction did not (or the video never needed frames). Check ingestStatus before reporting a document as incomplete, and never tell the user an import failed because frames are missing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoFilter to documents carrying this tag.
limitNoMax results (default 25).
queryNoKeywords to match against title and transcript text.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that return is compact metadata only, documents can be partial (frameCount: 0 with segments present), and that context document IDs are separate from recording IDs. While it lacks detail on pagination or ordering, it provides essential behavioral context for safe invocation.

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?

The description is well-structured and front-loaded with the core purpose. Each sentence adds value, using formatting (CAPS, backticks) for emphasis. It is slightly verbose but remains focused and efficient.

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?

Given no output schema, the description explains that returns are compact metadata, mentions important fields (frameCount, segments, ingestStatus), and covers edge cases (partial documents). It references sibling tools and provides complete guidance for using the tool effectively.

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% (all three parameters have descriptions in the schema), so the baseline is 3. The description adds no additional meaning beyond the schema; it does not elaborate on tag, limit, or query usage. Hence, no bonus.

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 identifies the tool as listing the user's context documents (imported/watched videos), explicitly distinguishes from sibling tools like list_recordings and search_recordings, and specifies the resource type (YouTube videos and local video files).

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?

The description provides explicit guidance: use list_recordings/search_recordings for screen recordings, call read_context_document for transcript, check ingestStatus for completeness, and warns against reporting failures due to missing frames. This clearly differentiates from alternatives and educates the agent on correct usage.

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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