Search X

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so safety and open-world reach are covered upstream. The description adds genuinely new behavioral facts beyond that: the call consumes a user-supplied external API key, is billed outside the free allowance, and returns a summary plus links. It does not state rate limits, result counts, or what 'recent' means in time window.

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?

Two sentences, purpose front-loaded, then the auth/billing caveat and the return shape — no filler. The dashboard URL and free-allowance clause make the middle slightly dense, but each clause carries operational weight for an agent deciding whether it can call the tool.

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?

With three parameters, full schema coverage, live annotations, and no output schema, the definition covers the essentials: what it searches, what credential it needs, and roughly what comes back. The remaining gap is operational detail — recency window, result volume, and failure mode when the key is missing — which an agent would want before relying on results.

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%, so query, model, and user_token are all documented in the schema itself, including examples of acceptable query forms. The description adds nothing parameter-level — it only hints at the API-key requirement, which maps loosely to user_token. Baseline 3 is appropriate when the schema carries the semantics.

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 first sentence names a specific verb and resource — 'Search X (Twitter) for recent posts and discussions' — plus the engine backing it (xAI's Grok), so the agent immediately knows this is a web/social search rather than a model query like ask_model or get_perspectives. It stops short of an explicit contrast with any sibling, so it lands at 4 rather than 5.

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?

It gives a clear context for use (recent posts and discussions on X) and a hard prerequisite: you need your own xAI API key, added at a specific dashboard URL, and it is not covered by the free allowance. It does not say when to prefer this over siblings such as ask_model or get_perspectives, so there are no explicit exclusions.

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