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get_installed_package_version

Check the installed version of a Python package in the server's environment to ensure documentation and source code match your installation.

Instructions

Check what version of a package is installed in the MCP server's Python environment.

Returns the installed version and the Python executable path so users can verify which environment is being checked. Always call this before get_function_source or get_doc_section when the user has not specified a version, to ensure the documentation and source code match their actual installation.

If the package is not installed, the response includes guidance on pointing mcp.json to the right Python environment.

Args: package: Package name as returned by list_packages().

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYes
Behavior4/5

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

Describes return values (version and Python path) and behavior when package is not installed (guidance on mcp.json). No annotations exist, so description carries full burden. Could mention that it is read-only and has no side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Reasonably concise; covers purpose, usage, return, and error handling in a few sentences. Could be slightly tighter by removing redundant phrasing like 'so users can verify which environment is being checked'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one parameter, no output schema, and no annotations, the description is highly complete: explains what it does, what it returns, when to use it, and what happens in error cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, but description adds meaning: 'Package name as returned by list_packages()'. This links the parameter to the sibling tool list_packages, providing helpful context beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the verb 'Check what version' and the resource 'package installed in the Python environment'. Distinguishes from siblings by specifying when to call this tool before get_function_source or get_doc_section.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use this tool: 'Always call this before get_function_source or get_doc_section when the user has not specified a version'. Provides context about environment verification and error handling.

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

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