suggest_tools

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns 'tool recommendations with priorities and reasons,' which adds some context about output behavior. However, it lacks details on permissions, rate limits, or error handling, leaving gaps for a 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 well-structured with a purpose statement, arg explanations, and return details, all in a compact format. Every sentence adds value, though the arg descriptions could be slightly more integrated into the flow rather than listed separately.

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 moderate complexity, no annotations, and the presence of an output schema (which handles return values), the description is fairly complete. It covers purpose, parameters, and output behavior adequately, though it could benefit from more behavioral context like error cases or usage examples.

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 provides clear semantics for all three parameters: intent_description explains its purpose, available_mcps notes it's optional and comma-separated, and discover_capabilities describes its boolean nature and effect. This adds significant value beyond the bare schema.

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 with a specific verb ('suggest') and resource ('MCP tools'), and it distinguishes itself from siblings by focusing on tool recommendation rather than analysis, enhancement, or validation. The phrase 'for a given intent' establishes its unique role in the toolset.

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 implies usage when determining which tools to use for an intent, but it doesn't explicitly state when to use this tool versus alternatives like analyze_intent or get_quality_standards. No exclusions or clear alternatives are provided, leaving usage context somewhat vague.

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