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MCP Python Refactoring

by slamer59

analyze_test_coverage

Analyze Python test coverage and get suggestions to reach a specified target coverage percentage.

Instructions

Analyze Python test coverage and suggest improvements

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
test_pathNoPath to test directory (optional)
source_pathYesPath to source code directory or file
target_coverageNoTarget coverage percentage (default: 80)
Behavior2/5

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

With no annotations, the description must fully disclose behavior, but it only states a generic analysis task. It does not clarify whether the tool runs tests, requires a coverage report, modifies files, or is read-only. The phrase 'suggest improvements' hints at output but lacks detail on side effects or requirements.

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

Conciseness3/5

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

The description is a single sentence, which is concise but arguably too minimal. It quickly states the purpose but omits critical context. It is front-loaded but could benefit from a second sentence clarifying behavior or usage without becoming verbose.

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

Completeness2/5

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

Given the lack of output schema and annotations, the description provides insufficient context. It does not explain the output format, what 'improvements' means, or how the tool interacts with the codebase. The agent has no way to assess prerequisites, side effects, or result structure.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema already documents all 3 parameters (source_path, test_path, target_coverage). The description adds no additional meaning beyond the word 'coverage', which is already inferred from the tool name. Baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the verb 'analyze' and resource 'Python test coverage', with the added behavior 'suggest improvements'. It is specific and distinguishable from sibling tools like find_package_issues or analyze_security_and_patterns, though it could be more explicit about the unique focus on coverage.

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

Usage Guidelines2/5

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 its siblings (e.g., find_package_issues or analyze_python_file). There are no prerequisites, context hints, or exclusions, leaving the agent to infer usage purely from the tool name and schema.

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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