attestify_research

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

Without annotations, the description carries full burden and adds significant behavioral context: metering cost ($0.023 USDC) and logging to an evidence ledger. It also discloses output structure. This is strong, though it omits potential side effects beyond cost/logging.

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 two sentences, front-loaded with the action and outcome, and each clause adds distinct value: what it does, what it returns, and the cost/logging side effects. No wasted words.

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 tool with 3 parameters and no output schema, the description covers the return format (structured briefing with executive summary, key findings, caveats) and a notable operational detail (cost/logging). It lacks explicit output schema, but the description sufficiently compensates, making it fairly complete.

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 100% per context signals, so the baseline is 3. The description adds no additional meaning about the query, url, or session_id parameters, leaving schema descriptions to carry the semantic load.

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 runs a deep research query through a specific agent (researcher-v2) and returns a structured briefing. It identifies the verb, resource, and output, making the purpose unmistakable even without sibling context.

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 phrase 'deep research query' implies use for comprehensive research, but there is no explicit statement of when to use vs alternatives or any exclusions. Lacking siblings, the guidance is only implied rather than formally specified.

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