stepsecurity-mcp

Official

Overview Schema Source Related Servers Discussions Admin

check_pypi_package_exposure

Checks monitored GitHub repositories within an organization for usage of a specified PyPI package at given versions, identifying affected CI pipelines. Use with developer machine checks for full coverage.

Instructions

Org-wide (CI-side) 'Am I Affected?' check for a PyPI (Python) package. Searches all monitored repos — workflow runs, PRs, and default branches — for uses of the package at the given versions. Pass an empty versions array to match any version. IMPORTANT: this only covers CI. For a complete 'am I affected' answer, also call check_python_package_on_dev_machines in parallel to cover developer laptops. Use this (not check_npm_package_exposure) when the threat-center incident's ecosystem is 'pypi'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "owner": "<owner>",
      "packageName": "<packageName>"
    },
    "name": "check_pypi_package_exposure"
  }
}

Input Schema

Name	Required	Description
`owner`	Yes	GitHub organization
`packageName`	Yes	PyPI package name, e.g. 'xinference', 'requests'
`versions`	No	Specific versions to check. Omit or empty to match any version.
`seenOnlyInPrs`	No	Restrict results to PR-only sightings
`startTime`	No	Unix timestamp (seconds) — lower bound
`endTime`	No	Unix timestamp (seconds) — upper bound

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Apr 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations provided, but description clearly states limitation: 'this only covers CI' and lists scopes (workflow runs, PRs, default branches). Does not discuss rate limits or auth, but scope is fully disclosed.

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

Conciseness5/5

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

Three dense sentences with zero waste. Each sentence adds critical info: scope, version handling, sibling differentiation, and cross-tool guidance.

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

Completeness4/5

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

Given no output schema, description could mention return format, but purpose and behavior are well-covered. Agent can decide when and how to call correctly. Missing return info is minor.

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 coverage is 100%, so baseline is 3. Description adds value by explaining empty versions behavior, which matches schema. No additional semantics for other parameters beyond 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 it's an org-wide CI-side check for a PyPI package, with verb 'check' and resource 'package exposure'. Distinguishes from sibling like check_npm_package_exposure by specifying ecosystem 'pypi'. Explains version matching behavior with empty array.

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 says use this when ecosystem is 'pypi' and not npm. Provides critical guidance to also call check_python_package_on_dev_machines for complete coverage, since this only covers CI.

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