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

Annotations already indicate read-only and idempotent behavior, but the description adds substantial behavioral detail: case-insensitivity, substring matching with word order/distance rules, ё/е equivalence, hit structure (source, t_ms, t_wall, matched text, nearest frame), speaker filtering that excludes OCR, audio-only behavior, and the absence of embeddings. No contradiction with annotations.

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 long but meticulously structured: a dense summary, a 'when not to use' callout, then a bulleted list of concrete examples and edge cases. Every bullet adds distinct value—search semantics, OCR benefits, stem suggestions, speaker use, anti-examples—so no sentence is wasted.

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 tool's complexity, the description is remarkably complete. It covers result contents, alternative tools, failure modes (no hits, audio-only, OCR absence), and practical workflows (jumping to frames, grepping logs). The presence of an output schema means return values do not need exhaustive listing, but the description still provides enough to use hits effectively.

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?

Schema coverage is 0%, so the description carries the full burden. It thoroughly explains the query parameter (every word as substring, any order/distance), the speaker parameter (narrows to one voice's transcript hits, excludes OCR), and job_id is contextualized by examples. This fully compensates for the lack of schema descriptions.

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 opens with a specific verb and resource: 'Case-insensitive word search across BOTH transcript segments and frame OCR text.' It clearly distinguishes itself from sibling tools like get_transcript and get_frames by defining its matching semantics and output scope. Multiple examples reinforce the tool's unique role.

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 includes an explicit 'When NOT to use' section naming alternatives (get_transcript for fuzzy/semantic questions, get_frames for visual layout issues) and states that regex is not supported. Anti-examples and 'no hits' advice further clarify when to use the tool versus reaching for a sibling.

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