graph_statistics

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

With no annotations, the description carries the burden of behavioral disclosure. The verb 'Calculate' implies a read-only operation, but no details are given about output format, performance considerations on large graphs, or whether the graph is modified. This is minimal transparency beyond the purpose.

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 a single, concise sentence that front-loads the core action and includes relevant examples. Every word contributes to the purpose.

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?

The tool has no output schema, no annotations, and a minimal description. For an analysis tool of this scope, the lack of information about return values, input format, and any caveats makes the description incomplete.

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 input schema has one required parameter 'graph' of type string, but the description does not explain what format is expected (e.g., graph ID, name, object). Since schema description coverage is 0%, the description fails to compensate, leaving the parameter ambiguous.

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 provides a clear verb ('Calculate'), a specific subject ('graph statistics'), and concrete examples (density, diameter, degree distribution). This distinguishes it from sibling tools like pagerank or centrality measures, which focus on specific algorithms.

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 description does not specify when to use this tool versus alternatives. There are no explicit exclusions or references to other tools, leaving the agent to infer from the listed statistics. Sibling tools like 'get_info' or 'centrality_measures' may overlap, but no guidance is provided.

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