Skip to main content
Glama
Sign In
proof-of-commitment

proof-of-commitment

by piiiico
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

audit_dependencies

Batch-score npm or PyPI packages for supply chain risk, returning a prioritized report sorted by commitment score to flag publish-access concentration, unproven high-adoption packages, and abandonware.

Instructions

Batch-score multiple npm or PyPI packages for supply chain risk. Takes a list of package names and returns a risk table sorted by commitment score (lowest = highest risk first).

Risk flags:

  • CRITICAL: single npm publisher + >10M weekly downloads (publish-access concentration risk)

  • HIGH: new package (<1yr) + high downloads (unproven, rapid adoption = supply chain risk)

  • WARN: no release in 12+ months (potential abandonware)

  • WARN: dormant publishers with current scope access — contributors who stopped publishing but retain npm tokens (the Mastra incident vector, June 2026)

Perfect for auditing a full package.json or requirements.txt — paste your dependency list and get a prioritized risk report.

Examples: score all deps in a project, compare two similar packages, identify abandonware before it becomes a CVE.

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": {
      "packages": "<packages>"
    },
    "name": "audit_dependencies"
  }
}

Input Schema

NameRequiredDescriptionDefault
packagesYesList of package names to score. Up to 20 at once. Examples: ["langchain", "litellm", "openai", "axios"] or ["@anthropic-ai/sdk", "zod", "express"]
ecosystemNoPackage ecosystem. "auto" defaults to npm. Force "pypi" for Python packages.auto

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations at all, the description carries the full disclosure burden, and it does substantial work: it states the return is a risk table sorted by commitment score with lowest = highest risk, and it defines the four risk flags and their triggering conditions. That is real behavioral context an agent could not get from the schema. It still omits auth/permission requirements, rate limits, latency, and what happens when a package name is not found or spans both ecosystems.

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?

It is front-loaded: purpose, then return ordering, then a cleanly bulleted risk-flag glossary, then usage examples. Every block is scannable. Minor cost: the flag glossary and the "Mastra incident vector, June 2026" aside lean toward output explanation rather than invocation guidance, adding length an agent does not strictly need to call the tool.

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?

There is no output schema, so the description must explain returns — and it does via the risk table ordering and flag definitions, which is enough for an agent to interpret results. What remains thin is the boundary behavior: unknown packages, mixed-ecosystem lists, and the meaning of "commitment score" itself are not explained.

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 both `packages` (with maxItems 20 and examples) and `ecosystem` (enum with auto/npm/pypi semantics) are already fully documented. The description restates "takes a list of package names" and the batch nature but adds no format rules or edge-case meaning beyond the schema. Baseline 3 applies.

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?

"Batch-score multiple npm or PyPI packages for supply chain risk" gives a specific verb (batch-score), a specific resource (npm/PyPI packages), and the domain (supply chain risk). The word "batch" and the plural scope immediately separate it from the single-package siblings like lookup_npm_package and lookup_pypi_package.

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

Usage Guidelines4/5

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

It names concrete use situations — auditing a full package.json or requirements.txt, comparing two similar packages, spotting abandonware pre-CVE. That is clear when-to-use guidance. It stops short of stating when NOT to use it (e.g. for a single package lookup, use the lookup_* siblings) or any prerequisites, so it falls one step below the explicit when/when-not/alternatives bar.

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

Deploy Server

Other Tools