sharplens-mcp

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool returns a list of symbols with details (kind, arguments, source location) and implies it's a read-only search operation without side effects. However, it doesn't mention performance aspects like timeouts or limitations on large codebases, leaving some gaps.

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 with the core purpose, followed by usage examples and a 'Use for' section. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI 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 moderate complexity (search operation with filtering), no annotations, and no output schema, the description does a good job by explaining the output format and use cases. However, it could be more complete by detailing error handling or result limitations, slightly reducing the score from perfect.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by providing usage examples that illustrate parameter application, but it doesn't explain semantics like how 'attributeName' matches partial names or case sensitivity. Baseline 3 is appropriate given the high 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 clearly states the tool's purpose with a specific verb ('Find') and resource ('all types and members decorated with a specific attribute'), and it distinguishes itself from siblings like 'find_callers' or 'find_references' by focusing on attribute usage. The opening sentence is direct and unambiguous.

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 explicitly provides usage examples with parameters and a 'Use for:' section listing specific scenarios (e.g., 'finding all API endpoints, authorization points, serialization config, test fixtures'), which clearly indicates when to use this tool versus alternatives. It effectively guides the agent on applicable contexts.

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