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

With no annotations, the description carries the burden, and it discloses case-insensitive matching on display name and the specific return contents (aggregate stats plus per-server list). It doesn't mention error cases or clarify GitHub username case-sensitivity, but overall it provides substantial behavioral context.

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?

Three sentences, front-loaded with the core purpose, followed by use-case questions and return summary. Every sentence carries meaningful information with no filler.

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 is comprehensive for a single-parameter read-only tool: it explains the purpose, use case, matching behavior, and return structure in detail. With no output schema, it names the aggregate stats fields. It could mention empty results, but that's a minor gap.

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 fully describes the creator parameter (case-insensitive display name or GitHub username, with a hint to use the `creator` field from search_servers). The description only reiterates case-insensitivity and doesn't add new parameter semantics; baseline 3 per 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 opens with a specific verb ('List') and clearly defines the resource ('all MCP servers by a single creator'), adding aggregate trust signals. This distinguishes it from siblings like search_servers (which likely lists all servers) or get_server (single server).

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 second sentence explicitly states the intended use cases ('evaluate a publisher holistically') with example questions, making it clear when to use this tool. However, it doesn't explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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