gapup-mcp

dependency_vulnerability_scan

Read-only

SCA (Software Composition Analysis) — scans a project dependency manifest and returns known vulnerabilities for each dependency. Supports: package.json (npm), requirements.txt (Python), go.mod (Go), Cargo.toml (Rust), composer.json (PHP), Gemfile.lock (Ruby), CycloneDX SBOM JSON. PRIMARY source: OSV.dev (keyless, free, covers npm/PyPI/Go/crates.io/Packagist/RubyGems + GHSA advisories federated). CVSS enrichment: NVD NIST (when OSV lacks score). Exploitation flag: CISA KEV (known-exploited-vulnerabilities catalog). Returns per-vuln CVE/GHSA IDs, severity, CVSS score, fixed version, and actionable upgrade recommendations. Relevant for EU NIS2 supply chain risk obligations, DORA, SOC 2 vendor assessments. Cache TTL 6h. Parallel OSV queries (concurrency=10). SLA <=30s p95.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Manifest type: "package_json"=npm, "requirements_txt"=pip, "go_mod"=Go modules, "cargo_toml"=Rust, "composer_json"=PHP, "gem_lock"=Ruby, "sbom_cyclonedx"=CycloneDX SBOM JSON.
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`severity_min`	No	Minimum severity to include in results (default: "medium").
`manifest_content`	Yes	Raw text content of the manifest file to scan (e.g. full contents of package.json, requirements.txt, etc.).
`include_transitive`	No	Include transitive/indirect dependencies in results (default: true).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`sources`	Yes
`summary`	Yes
`ecosystem`	Yes
`quality_score`	Yes
`recommendations`	Yes
`vulnerabilities`	Yes
`dependencies_parsed`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Goes well beyond annotations by detailing data sources (OSV.dev, NVD NIST, CISA KEV), caching (TTL 6h), concurrency (10), SLA (≤30s p95), and async polling behavior. Read-only and non-destructive nature is reinforced.

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?

Dense but well-organized; front-loaded with purpose and supported formats. Every sentence contributes, though slightly long. Efficient for its information density.

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?

Covers input, behavior, data sources, performance, and compliance relevance. Output schema is present, so return values are documented separately. Complete for a tool with rich annotations.

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 coverage is 100%, but description adds extra context: enum mappings, default values, and purpose of async parameter. Adds value beyond schema descriptions.

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?

The description clearly states it scans dependency manifests for known vulnerabilities, lists supported manifest types, and distinguishes from general CVE lookups or other vulnerability tools in the sibling list.

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?

Provides explicit usage cues: specific manifest formats, compliance standards, and performance characteristics. Lacks explicit when-not or alternative recommendations, but the context is sufficient for correct invocation.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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