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, and it does so well: it explains the ranking mechanism, the curated snapshot source, precomputed embeddings, the one embedding call per query, and the exact output fields including the relevant score. It also addresses cost/auth expectations by stating free/no account/no payment.

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 two tight, information-dense sentences. Purpose and constraints are front-loaded, and later details (embedding mechanism, returned fields) are relevant and non-redundant. Every clause earns its place.

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?

The description gives a strong overview of what the tool returns and how ranking works, and an output schema exists to document return structure. The main gap is the unexplained 'max_results' parameter and the lack of explicit exclusions or alternative routing, though the simple parameter set and default value reduce the risk.

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 has 0% description coverage, so the description must compensate, but it only partially does. 'Matching a need' implies the meaning of 'q', yet 'max_results' is never explained; an agent must infer that it caps the number of returned matches. The description focuses on output fields rather than parameter behavior.

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 uses a specific verb ('Discover') and resource ('MCP servers'), and further clarifies the scope: matching a need, ranked by semantic similarity over a curated snapshot. This clearly distinguishes it from sibling tools like discover_semantic, which appears to be generic semantic discovery.

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 states the intended use case ('matching a need') and adds practical context: free, no account, no payment, and precomputed embeddings with one embedding call per query. However, it does not explicitly mention when not to use this tool or how it compares to a sibling alternative like discover_semantic.

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