cos_graph_ranking

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds meaningful context: empty query = global PageRank, optional personalization, and the return format ('JSON envelope with nodes ranked'). This is sufficient beyond 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 compact and well-structured: a one-line definition, a 'Use for' section, structured Args, and a Returns line. No redundant words or repeated schema details.

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 moderate-complexity tool with 5 optional parameters and an existing output schema, the description covers purpose, all parameters, and return shape. The 'use for' section provides application context, making it complete for an agent to decide and invoke correctly.

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 schema provides zero description coverage (all params are just names with defaults), so the description carries full burden. It explains all five parameters: query (personalisation, empty=global), top (max nodes), kind (filter), damping (PageRank damping factor), iterations (power-iteration count). This adds rich meaning beyond the schema.

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 'PageRank — node importance, optionally personalised by query,' clearly identifying the tool as a node-importance ranking algorithm with personalization support. It distinguishes itself from sibling graph tools (e.g., cos_graph_centrality) by naming PageRank and listing concrete use cases.

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?

Three explicit use cases are given: knowledge condensation, query-personalised search ranking, and documentation sourcing. This provides clear context for when to use the tool, though it does not name alternatives or state when NOT to use it.

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