extract_entities

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool extracts entities but doesn't describe how (e.g., via NLP, accuracy, rate limits), what the output looks like (since no output schema exists), or any constraints (e.g., video length limits, processing time). This leaves significant gaps for an AI agent to understand the tool's behavior beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes essential information, earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of entity extraction (which involves NLP processing) and the lack of annotations and output schema, the description is incomplete. It doesn't explain the output format, accuracy, limitations, or how it integrates with other tools (e.g., needing 'get_transcript' first). For a tool with no structured behavioral data, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the single parameter 'video_id' documented as a YouTube video ID. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation rules. However, with high schema coverage and only one parameter, the baseline score of 3 is appropriate as the schema adequately covers parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: extracting structured entities from video transcripts. It specifies the resource (video transcripts) and the action (extracting entities), and lists the types of entities (companies, indices, people, sectors, etc.). However, it does not explicitly differentiate this tool from sibling tools like 'segment_topics' or 'search_transcripts', which might also process transcripts, so it doesn't fully distinguish from alternatives.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a transcript first), exclusions, or compare it to siblings like 'segment_topics' or 'search_transcripts' that might handle similar data. Usage is implied by the purpose but lacks explicit context or alternatives.

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