compute_graph_metrics

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

The description discloses that metrics are stored in the database if store_results is true, but does not explicitly state that this is a write operation or mention other behavioral traits like performance impact, rate limits, or whether the tool modifies existing data. With no annotations, more transparency would be beneficial.

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 three sentences long, each serving a purpose: stating what metrics are computed, describing the return value, and noting storage behavior. It is concise without unnecessary 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?

Given the lack of output schema, the description states 'Returns detailed metrics for all entities and graph-level statistics' which is adequate but vague. It does not specify the format or structure of the returned metrics. A more detailed explanation of the output would improve completeness.

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?

All four parameters are fully described in the input schema (100% coverage). The description does not add any additional meaning or context beyond what the schema already provides, so baseline score of 3 is appropriate.

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 computes comprehensive graph metrics including PageRank, betweenness centrality, degree centrality, and community detection. It specifies return of detailed metrics for all entities and graph-level statistics, and distinguishes from siblings like analyze_graph_pagerank or calculate_graph_centrality which likely compute single metrics.

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?

No guidance is provided on when to use this tool versus alternatives like analyze_graph_pagerank or calculate_graph_centrality. The description does not mention prerequisites, trade-offs, or scenarios where a simpler tool would be preferred.

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