execute_graphql

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

With no annotations provided, the description carries the full burden. It mentions that the tool 'returns actual data' and includes error details, but does not explicitly state whether it is read-only, any authorization requirements, or potential side effects. Additional behavioral details would improve transparency.

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 well-structured with sections for args, returns, workflow, and an important note. While slightly verbose, each sentence adds value and the critical information is front-loaded.

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 a single parameter and no output schema, the description covers purpose, usage prerequisites, workflow, and error handling. It is sufficient for an agent to use the tool effectively, though mentioning read-only nature would enhance 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?

The schema provides only a 'query' string with no description. The description adds meaning by specifying it must be a valid GraphQL query validated by validate_query, and explains return values. This compensates for the 0% schema coverage.

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 'Execute your GraphQL query and retrieve data from the Gen3 data commons,' specifying the verb (execute), resource (GraphQL query), and context (Gen3 data commons). It also distinguishes this tool from siblings by positioning it as the final execution step after validation.

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 explicitly recommends running validate_query first and outlines a workflow sequence, guiding when to use this tool. It does not specify when not to use it, but the context is clear enough for the agent to decide.

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