MCP Python Interpreter

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 describes a read-only listing operation, which is straightforward, but lacks details on output format (e.g., list structure, package details), potential errors (e.g., if environment doesn't exist), or performance considerations. For a tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 highly concise and well-structured: the first sentence states the purpose clearly, and the 'Args' section efficiently explains the parameter without redundancy. Every sentence adds value, and there's no unnecessary information, making it easy for an agent to parse and understand quickly.

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 low complexity (one optional parameter, no annotations, no output schema), the description is minimally adequate. It covers the purpose and parameter semantics well, but lacks details on output (what the list includes, format) and error handling. For a listing tool, this might suffice, but without annotations or output schema, it leaves the agent guessing about the return values, resulting in a baseline score of 3.

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 description adds meaningful context for the single parameter 'environment' by explaining its semantics: it specifies the Python environment name, clarifies the default behavior ('default if custom path provided, otherwise system'), and implies it's optional. With schema description coverage at 0% (the schema only provides a title and type), the description fully compensates by detailing the parameter's role and default logic, earning a high score.

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 ('List') and resource ('installed packages') along with the target context ('for a specific Python environment'). It distinguishes from siblings like 'install_package' (which installs rather than lists) and 'list_python_environments' (which lists environments rather than packages). However, it doesn't explicitly differentiate from 'list_directory' or other listing tools, keeping it at 4 rather than 5.

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 implies usage context by specifying 'for a specific Python environment,' suggesting this tool should be used when targeting packages within an environment rather than system-wide. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'list_directory' (which might list files) or 'list_python_environments' (which lists environments themselves), nor does it mention prerequisites or exclusions, resulting in a score of 3.

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