mcp-pyright

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'returns diagnostics' but doesn't specify what these diagnostics include (e.g., errors, warnings), how they're formatted, or any limitations (e.g., performance, supported Python versions). The mention of Pyright adds some context, but behavioral traits like rate limits, authentication needs, or side effects are not addressed.

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 appropriately sized with two sentences that are front-loaded: the first sentence states the purpose, and the second adds implementation details (Pyright) and output. There's minimal waste, though it could be slightly more structured (e.g., separating purpose from behavior).

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 (type checking with multiple parameters), no annotations, and an output schema (which handles return values), the description is adequate but has gaps. It covers the basic purpose and tool used (Pyright), but lacks details on behavioral context, usage guidelines, and output specifics beyond 'diagnostics', making it minimally viable but incomplete for full understanding.

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 four parameters (code, file_path, python_path, type_checker_mode) with descriptions. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or usage examples. Baseline 3 is appropriate when schema does the heavy lifting.

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: 'Check types in a Python file or string of code' specifies the verb ('check types') and resource ('Python file or string of code'). It distinguishes from siblings like 'format_code' or 'get_completions' by focusing on type checking, but doesn't explicitly differentiate from all siblings (e.g., 'find_references' might also analyze code).

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 no guidance on when to use this tool versus alternatives. It mentions using Pyright for type checking, but doesn't indicate scenarios where this is preferred over other tools (e.g., 'get_completions' for code suggestions) or when not to use it. Usage is implied by the purpose but lacks explicit context or exclusions.

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