Get Compatible AI Tools

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context about the data source (a computed IO-Compatibility Graph) and scale, but says nothing about result ordering, empty-result behavior, or rate limits. With annotations carrying the safety burden, this is adequate but thin.

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?

Front-loaded with the core purpose and clearly delimited from alternatives in the first sentence. The trailing statistic ('4,087,142+ real computed connections') and the 'not a category-similarity guess' defense lean promotional and only marginally aid selection, but the description is otherwise tight.

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?

With an output schema present, return values needn't be explained, and the description clarifies the non-obvious concept of 'compatible' (IO structural match). It could note behavior on zero matches or how results are ranked, but for a read-only lookup tool the coverage is sufficient.

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 coverage is 100%, so both parameters (slug and limit) are fully documented in the schema, setting the baseline at 3. The description adds no syntax, format, or edge-case detail about the slug or limit beyond what the schema already states.

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?

States a specific verb ('find') plus resource ('AI tools') and a precise scope ('that genuinely connect', IO-compatibility). It explicitly contrasts itself with category-similarity tools, which cleanly separates it from siblings like find_similar_by_philosophy or search_ai_tools. An agent can tell what this returns without opening the schema.

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?

Offers concrete example queries ('what tools work well with ChatGPT', 'what can I feed ChatGPT's output into') that clearly signal the intended use case. However, it never names alternatives or states when NOT to use it (e.g., use search_ai_tools for keyword lookup, find_similar_by_philosophy for thematic similarity), so routing is inferred rather than explicit.

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