gapup-mcp

oss_dependency_velocity_tracker

Read-onlyIdempotent

As a CTO, track the update velocity of your project's open-source dependencies to assess their impact on DORA metrics like deployment frequency and lead time. This tool fetches release history and version adoption data from npm registry and libraries.io, providing insights into dependency freshness, update frequency, and potential risks. Input a list of package names and optional version ranges to analyze. Outputs structured dependency velocity metrics and warnings about stale or rapidly changing packages.

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.
`packages`	Yes
`lookbackDays`	No

Output Schema

TableJSON Schema

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

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds value by specifying data sources (npm registry, libraries.io) and output type (structured metrics and warnings). It does not disclose potential issues like rate limits or error handling, but the added context is sufficient.

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 concise (3 sentences) and front-loaded with purpose. The opening 'As a CTO' adds role context but is slightly extraneous. Overall efficient with no repetition.

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 complexity (3 parameters, external data fetch, output schema exists), the description covers purpose, input, and output. It misses detailing the lookbackDays parameter, but the output schema handles return values. Nearly complete for a read-only, idempotent tool.

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 33% (only async described). The description explains the packages parameter ('list of package names and optional version ranges') but fails to mention lookbackDays. This partially compensates for low coverage but leaves a gap for the lookbackDays parameter.

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's purpose: tracking update velocity of open-source dependencies to assess impact on DORA metrics. It specifies the resource (dependency velocity), verb (track), and differentiates from siblings like dependency_vulnerability_scan by focusing on velocity rather than security.

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 provides context (CTO, DORA metrics) and mentions input requirements, but does not explicitly state when to use this tool versus alternatives. Usage is implied but not clearly delineated with when-not-to-use or comparisons to sibling tools.

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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