gapup-mcp

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

Name	Required	Description
`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.
`lookbackDays`	No	Number of days to analyze trends for
`repositories`	Yes	List of GitHub repositories in owner/repo format
`minScoreThreshold`	No	Minimum acceptable score to flag as risky

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`results`	No
`sources`	No
`warnings`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint. The description adds value by naming data sources (OSSF Scorecard API, GitHub Archive) and output structure (score deltas, check failures, risk flags). No contradictions, and it enriches the behavioral model beyond annotations.

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?

Three concise sentences that front-load the role and purpose, then specify input, output, and use case. Every sentence earns its place with zero 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?

The description covers purpose, input format, output structure, and use case. With an output schema present, it does not need to detail return values. Adequate for a tool with moderate complexity (4 parameters, trend analysis).

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%, so baseline is 3. The description offers a high-level explanation of output and input role but does not add detailed parameter meaning beyond what the schema already provides (e.g., lookbackDays, minScoreThreshold).

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 the tool analyzes OSSF Scorecard trends for dependencies, specifying a verb ('analyze'), resource ('OSSF Scorecard trends'), and scope ('top 10-50 dependencies'). It distinguishes itself from sibling tools, none of which perform similar scorecard trend analysis.

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 provides clear context: 'As a CTO' and 'proactive dependency management and risk assessment.' It specifies input format (GitHub repository names). However, it lacks explicit guidance on when not to use this tool or alternatives, missing a higher score.

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.

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