Mikensey MCP Server

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

Annotations already indicate this is a read-only, non-destructive, idempotent operation with a closed-world dataset. The description adds valuable context beyond annotations by specifying the data source ('podcast conversations'), content types (e.g., 'specific numbers cited by industry leaders'), and return format ('Benchmark data with metric name, value, context, and source'), enhancing the agent's understanding without contradictions.

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 and front-loaded, starting with the core purpose, followed by key details, arguments, returns, and examples. Each sentence adds value without redundancy, such as specifying data content and usage scenarios, making it efficient and easy to parse for an agent.

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 complexity (simple query-based retrieval), rich annotations (covering safety and behavior), and lack of output schema, the description is mostly complete. It explains the purpose, data source, return format, and usage examples. However, it could improve by detailing potential limitations (e.g., data recency) or error cases, slightly reducing completeness.

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 input schema has 100% description coverage, with the query parameter documented as 'Filter benchmarks by keyword'. The description adds minimal value beyond this, repeating the filtering purpose and providing examples (e.g., 'mortgage', 'agent'), but does not clarify syntax or constraints. With high schema coverage, a baseline score of 3 is appropriate.

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: 'Look up real estate, mortgage, and proptech industry benchmarks and metrics from podcast conversations.' It specifies the verb ('look up'), resource ('benchmarks and metrics'), and source ('podcast conversations'), distinguishing it from siblings like mikensey_get_episode or mikensey_get_frameworks by focusing on quantitative data rather than episodes or advice.

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 provides clear context on when to use this tool: for accessing benchmark data from podcasts, with examples like 'attach rate' or 'cost'. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., mikensey_search might overlap), leaving some ambiguity in tool selection.

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