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mcp-knowledge

by io.github.getgapup

ossf_scorecard_trend_analyzer

Read-onlyIdempotent

As a CTO, analyze OSSF Scorecard trends for your top 10-50 dependencies to identify security regressions or deteriorating project health. Input GitHub repository names (owner/repo), get structured trend data including score deltas, check failures, and risk flags. Uses OSSF Scorecard API and GitHub Archive for historical context. Ideal for proactive dependency management and risk assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
lookbackDaysNoNumber of days to analyze trends for
repositoriesYesList of GitHub repositories in owner/repo format
minScoreThresholdNoMinimum acceptable score to flag as risky

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
resultsNo
sourcesNo
warningsNo

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent behavior. The description adds context about data sources (OSSF Scorecard API, GitHub Archive) and output structure (score deltas, check failures, risk flags), enhancing transparency beyond structured fields.

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?

The description is concise at 4 sentences, front-loaded with purpose and audience, and efficiently covers key aspects without redundancy.

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 the tool's moderate complexity, existence of output schema, and good annotations, the description provides sufficient context including data sources and output components. Missing details like error handling are acceptable.

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% with clear descriptions. The description reiterates the input format (owner/repo) but adds no new meaning to parameters beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'analyze' and the resource 'OSSF Scorecard trends', specifying the goal of identifying security regressions or deteriorating project health. While the tool is unique among siblings, no explicit sibling differentiation is provided.

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

Usage Guidelines3/5

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

The description implies usage for proactive dependency management by a CTO for top 10-50 dependencies, but lacks explicit guidance on when not to use the tool or how it compares to alternatives.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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