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Commit — Supply Chain Risk Scoring

by io.github.piiiico

audit_dependency_tree

Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level.

Unlike auditing a flat list of packages, this tool traverses the dependency graph — showing not just your direct dependencies but also what your dependencies depend on. Hidden CRITICAL packages (sole publisher + >10M weekly downloads) often lurk 1-2 levels deep.

Risk flags:

  • CRITICAL: single npm publisher + >10M weekly downloads — sole point of failure for a massive attack surface

  • HIGH: sole publisher + >1M/wk, OR new package (<1yr) with high adoption

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

depth=1 (default): root package + all direct dependencies depth=2: also traverses one more level for any CRITICAL/HIGH direct deps (reveals hidden exposure)

Examples:

  • audit_dependency_tree("express") — see all of Express's deps and their risk scores

  • audit_dependency_tree("langchain", 2) — reveal transitive CRITICAL deps 2 levels deep

  • audit_dependency_tree("@anthropic-ai/sdk") — audit Anthropic SDK full tree

Use this when someone asks:

  • "What am I really depending on?"

  • "Are my dependencies' dependencies safe?"

  • "Show me the full supply chain risk for package X"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow deep to traverse. 1 = direct deps only (fast). 2 = also traverse deps of CRITICAL/HIGH packages (slower, reveals hidden risk). Default: 1
packageYesnpm package name to map. Examples: "express", "langchain", "@anthropic-ai/sdk", "zod"

TDQS

A4.9/5.0
Behavior5/5

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

No annotations, so description carries full burden. It details traversal behavior (depth semantics), defines risk flag thresholds, and notes performance tradeoffs ('slower'). This is rich behavioral disclosure beyond a simple 'gets dependencies'.

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?

Though long, the description is well-structured with headers, bullets, and examples. Every section adds value: overview, risk flags, depth explanation, examples, and use-case triggers. No filler or tautology.

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?

The tool has no output schema, and the description does not explicitly describe the response format. While it thoroughly explains functionality and risk flags, an agent might not know the exact structure of the returned dependency tree. Minor gap, but overall comprehensive.

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

Parameters5/5

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

Despite 100% schema coverage, the description adds crucial semantics: explains depth=2 only traverses CRITICAL/HIGH direct deps, and provides multiple working examples with different package names. This goes far beyond the schema's 'minimum' and 'maximum'.

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?

States 'Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level.' Clearly specifies verb (map/identify), resource (npm dependency tree), and scope (full tree with risk flags), distinguishing it from flat-list audit and lookup tools.

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 contrasts with 'auditing a flat list of packages' and provides a 'Use this when' list with natural language queries. This gives clear guidance on when to prefer this tool over siblings.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools are clearly distinct by ecosystem and action, but the pair audit_github_repo and lookup_github_repo could be confused since both operate on GitHub repos (one audits dependencies, the other profiles the repo itself). The descriptions help, but the naming is similar enough to cause occasional misselection.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: 'audit_' for dependency auditing, 'lookup_' for single-entity profiles, plus 'get_api_key' and 'query_commitment'. The 'lookup_business_by_org' variant is a clear sub-pattern, not a deviation.

Tool Count5/5

12 tools is well-scoped for a multi-ecosystem supply chain risk scoring server. Each tool covers a distinct ecosystem or operational function (auditing, lookup, API key management, domain commitment), and none feel redundant.

Completeness4/5

Coverage is strong: flat audits for npm/PyPI/Cargo/Go, npm dependency tree traversal, GitHub repo audits, and single-package lookups across all major ecosystems. Minor gaps include no explicit tool for managing the monitoring/alerts feature mentioned in get_api_key, and no dependency tree traversal for non-npm ecosystems, but these are acceptable workarounds.

Resources