predict_citation

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

Explicitly states no LLM is used ('all heuristic'), lists the specific signals checked, and describes the output format (score, grade, breakdown, fixes). No annotations exist, so description carries full burden; it is mostly adequate.

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: first states purpose and signals, second clarifies method and output. No redundant text, front-loaded with key information.

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?

Despite no output schema, the description fully explains the output (0-100 score, grade, signal breakdown, ranked fixes) and the heuristic approach. All relevant aspects are covered.

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?

The single parameter 'url' is well-documented in the schema (100% coverage). The description adds a list of signals but does not elaborate on the parameter itself beyond what the schema provides.

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?

Clearly states it scores citation likelihood for a URL using public signals. Distinguishes from siblings like 'am_i_cited' (which checks if cited) and 'check_citations' (which verifies existing citations) by focusing on prediction via heuristics.

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?

Implied usage: use when you want to predict citation potential from web signals. No explicit when-not-to-use or alternatives mentioned, leaving the agent to infer from context.

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