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

Goes well beyond the annotations: discloses the credit cost (one of 5 monthly credits per brand), the pipeline composition (Gemini + GPT-4o + normalization), the async latency (3-5 min), and that the immediate response is a placeholder_id. Annotations only cover the safety profile, so this added operational context is genuinely valuable.

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?

Front-loads the action, then constraints and result retrieval, with an Args block that restates parameters. The Args section partially duplicates the schema, but adds length/format constraints, so it mostly earns its place; slightly longer than strictly necessary.

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?

For a non-idempotent, open-world mutation tool with no output schema, the description supplies everything needed to call it correctly: prerequisites, cost, async nature, and the polling path to retrieve results. Nothing essential is missing.

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 description coverage is low (25%), and the description compensates by documenting brand_id, topic (2-200 chars), and topic_context (up to 500 chars) with constraints. The user_intent parameter is not mentioned in the description, though the schema itself carries a description for it, leaving a minor gap.

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?

States a specific verb and resource ('Trigger a topic snapshot research run') and explicitly carves out what it is not: full prompt research runs, which 'are intentionally NOT exposed through the MCP.' An agent can distinguish it from sibling read tools like get_latest_research without opening schemas.

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?

Explicit when-to-use ('Only use when the user explicitly asks to run new research on a topic'), a stated prerequisite ('call get_research_credits first'), a state requirement (brand must be active/tracking on), and named alternatives for retrieving results (get_research_runs, get_latest_research with report_type='topic_snapshot').

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