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 states what the tool does but lacks details on traits like output format (e.g., list of strings, JSON), whether it includes virtual environments, error handling, or performance implications. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place by clarifying the resource scope. It's appropriately sized for a simple tool with no parameters.

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 (0 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral context and usage guidelines. Without annotations or output schema, the agent might struggle with how to interpret results or handle edge cases, making it incomplete for optimal use.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by specifying the scope ('system Python and conda environments'), which isn't captured in the schema. This compensates well, but since there are no parameters, the baseline is high, and it doesn't fully address potential implicit parameters like filtering options.

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 verb 'List' and the resource 'all available Python environments', specifying both system Python and conda environments. It distinguishes from siblings like list_directory or list_installed_packages by focusing on environments rather than files or packages. However, it doesn't explicitly differentiate from run_python_code or run_python_file in terms of when to use each, keeping it from a perfect score.

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 doesn't mention prerequisites, such as needing conda installed for conda environments, or compare it to siblings like list_installed_packages for package listings. Without explicit when/when-not instructions, the agent must infer usage from the purpose alone.

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