suggest_personas_for_query

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

With no annotations provided, the description carries the burden. It discloses that the tool uses keyword matching, context detection, and relevance scoring, does not perform analysis itself, and returns a JSON list with scores and rationale. This is reasonable transparency, though it doesn't explicitly state read-only status or edge-case behaviors.

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 somewhat long but well-structured with sections for purpose, philosophy, Args, Returns, and Example. Each sentence adds value; the example is pragmatic. It earns a slight deduction for redundancy (e.g., 'JSON list of suggested personas with relevance scores and rationale' appears in both Returns and Example).

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?

For a suggestion tool with an output schema, the description covers all needed aspects: purpose, usage workflow, parameter semantics, return format, and a concrete example. It is complete enough for an agent to select and invoke the tool correctly.

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?

Despite the input schema lacking descriptions (0% coverage), the description includes an 'Args' section that explains each parameter: query, max_suggestions (with default), and context_hint (with example). This fully compensates for the schema gaps, making the parameters clear.

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 'Suggest relevant personas for a given query', a specific verb+resource that distinguishes this from sibling tools like get_persona_content (which retrieves content) and suggest_mcps_for_query (which suggests MCPs). The purpose is unmistakable.

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 'MCP Design Philosophy' explains that this tool is for discovering which personas to consult and that the LLM should then call get_persona_content() for each recommendation. This gives clear workflow context, though it doesn't explicitly state when NOT to use it or contrast with alternatives.

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