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mcp-knowledge

by io.github.getgapup

dora_metrics_deep_dive

Read-onlyIdempotent

Analyzes DORA metrics (Deployment Frequency, Mean Time to Recovery, Change Failure Rate) with deep correlation to code review patterns. Designed for CTOs to identify bottlenecks in software delivery pipelines. Inputs include GitHub repository identifiers and optional time ranges. Outputs structured metrics with trend analysis and code review depth insights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoYesGitHub repository in format 'owner/repo'
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.
sinceNoStart date for analysis (ISO 8601)
untilNoEnd date for analysis (ISO 8601)
branchNoBranch name to analyze (default: main)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
metricsNo
sourcesNo
warningsNo

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds that it outputs structured metrics with trend analysis and code review insights, which gives some behavioral context but does not detail rate limits, authentication, or what happens on errors.

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 three sentences with clear, front-loaded information. No unnecessary words.

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 (DORA metrics), schema coverage 100%, output schema exists, and annotations are rich. The description covers purpose, inputs, and output format adequately. It could mention the async parameter behavior but that is covered in schema.

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 3. The description summarizes parameters (GitHub repo, optional time ranges) but does not add significant meaning beyond the 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 analyzes DORA metrics with deep correlation to code review patterns. It distinguishes itself from siblings like 'change_failure_root_cause_classifier' and 'code_review_depth_optimizer' by focusing on correlation with code review patterns.

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 states it is designed for CTOs to identify bottlenecks, providing clear context for use. However, it does not explicitly state when not to use or list 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.

Resources