graph-tool-call

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

No annotations are provided, so the description carries the full burden. It discloses that the tool sends HTTP requests and mentions optional authentication ('auth_token'), but lacks details on error handling, rate limits, or response formats. It adds some context (e.g., workflow dependency) but is incomplete for a mutation tool with no annotation coverage.

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 appropriately sized and front-loaded: the first sentence states the purpose, followed by key details and parameter explanations. It avoids redundancy, though the parameter list is somewhat verbose. Every sentence adds value, such as the workflow guidance.

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 tool's complexity (executing arbitrary OpenAPI tools), no annotations, and an output schema present, the description is fairly complete. It covers purpose, usage workflow, and parameter semantics, but lacks behavioral details like error handling. The output schema mitigates the need to explain return values, making this adequate though not exhaustive.

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 0%, so the description must compensate. It adds meaningful semantics for all parameters: 'tool_name' as 'Exact tool name (as returned by search_tools)', 'arguments' as 'JSON string of parameter values', 'base_url' as 'API base URL', and 'auth_token' as 'Bearer token for authentication'. This clarifies usage beyond the bare schema, though it doesn't cover all edge cases (e.g., JSON format specifics).

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's purpose: 'Execute an OpenAPI tool via HTTP' and 'Sends the actual HTTP request based on the tool's method and path from the OpenAPI spec.' It specifies the verb ('execute') and resource ('OpenAPI tool'), though it doesn't explicitly differentiate from siblings like 'get_tool_schema' or 'search_tools' beyond mentioning their use in a workflow.

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 provides explicit usage guidelines: 'Use after search_tools() + get_tool_schema() to call the API.' This clearly indicates when to use this tool versus alternatives (e.g., use those sibling tools first) and sets prerequisites, making it highly actionable for an AI agent.

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