video-research-mcp

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

Beyond the annotations (readOnlyHint: false, openWorldHint: true), the description discloses critical behavioral traits: exact cost range ($2-5), duration (10-20 minutes), background execution model, query volume (~80-160), output format (cited markdown report), and the polling pattern. No contradictions 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 efficiently structured with clear sections. Key operational constraints (cost, time) are front-loaded after the core function. The 'Args' and 'Returns' sections use minimal words to add essential context beyond the schema. No filler content.

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 (async, costly, long-running) and the presence of output schema information in the description ('Returns: Dict with interaction_id...'), the definition is complete. It covers the full lifecycle: initiation, cost, duration, and status checking.

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?

With 100% schema coverage, the baseline is 3. The description adds valuable semantic context for the 'topic' parameter, advising users to 'include specific questions, scope, hypotheses,' which complements the schema's generic description and improves input quality.

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 ('Launch') and resource ('Gemini Deep Research Agent'), clearly defining the tool's function. It distinguishes itself from siblings like 'web_search' and 'research_deep' by specifying 'autonomous web-grounded research' and quantifying the scope (~80-160 queries).

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 explicitly references the sibling tool 'research_web_status' for polling, establishing the async workflow. While it doesn't explicitly name alternatives (e.g., 'use web_search for quick queries'), it provides clear cost ($2-5) and time (10-20 min) constraints that implicitly guide appropriate usage.

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