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gapup-mcp

by io.github.getgapup

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
NameRequiredDescriptionDefault
modeYesManifest 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.
asyncNoIf 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_minNoMinimum severity to include in results (default: "medium").
manifest_contentYesRaw text content of the manifest file to scan (e.g. full contents of package.json, requirements.txt, etc.).
include_transitiveNoInclude transitive/indirect dependencies in results (default: true).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
sourcesYes
summaryYes
ecosystemYes
quality_scoreYes
recommendationsYes
vulnerabilitiesYes
dependencies_parsedYes

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses data sources (OSV.dev, NVD/NIST, CISA KEV), authentication/cost posture (keyless, free), caching behavior (TTL 6h), concurrency, and performance SLA (<=30s p95). This gives the agent a strong understanding of tool behavior and side effects.

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?

The description is fairly dense but well-organized, front-loading the core purpose and then adding supported formats, data source details, outputs, compliance context, and performance characteristics. Each section earns its place, though the compliance sentence is somewhat tangential to direct invocation.

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?

The description covers supported inputs, data sources, return contents, caching, concurrency, SLA, and relevant use cases. With an output schema present, the description is fully complete for an agent to select and invoke the tool correctly, including edge considerations like async polling.

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%, and the schema already provides rich descriptions for all five parameters including enums and defaults. The description adds no parameter-specific detail beyond what the schema provides, so the baseline score of 3 is appropriate.

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 opens with a specific verb and resource: 'scans a project dependency manifest and returns known vulnerabilities for each dependency.' It clearly identifies the tool's SCA role and lists supported manifest formats, distinguishing it from sibling CVE lookup and vulnerability prioritization tools by focusing on dependency manifests.

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?

The description clearly indicates when to use the tool—when you have a project dependency manifest and need known vulnerabilities—and provides use-case context via compliance frameworks (NIS2, DORA, SOC 2). It does not explicitly mention alternatives or exclusions relative to sibling tools, so it falls one point short of full explicit guidance.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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