gapup-mcp

mttr_breakdown_analyzer

Read-onlyIdempotent

As a CTO, analyze your team's incident response efficiency by breaking down Mean Time To Recovery (MTTR) into root causes: code defects, infrastructure failures, or process bottlenecks. This tool ingests GitHub issue and pull request data alongside Snyk vulnerability reports to provide a detailed breakdown of MTTR components, helping you identify systemic weaknesses in your incident resolution pipeline. Input your GitHub repository details and time range to receive a structured analysis of MTTR contributors with actionable insights.

Input Schema

TableJSON Schema

Name	Required	Description
`repo`	Yes	Full GitHub repository name (owner/repo)
`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.
`since`	Yes	Start date for analysis (ISO 8601)
`until`	Yes	End date for analysis (ISO 8601)
`snykToken`	No	Snyk API token for vulnerability data (optional)
`githubToken`	Yes	GitHub personal access token for API access

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`breakdown`	No
`topContributors`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description confirms it is a read-only analysis tool that ingests external data (GitHub, Snyk). It adds context about data sources and output format beyond annotations, e.g., 'structured analysis of MTTR contributors with actionable insights'. No behavioral contradictions.

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 concise sentences. It is front-loaded with purpose, then outlines data sources and output. No fluff or repetition. Every sentence adds value: target user and action, inputs, and output.

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 has 6 parameters (with 100% schema coverage) and an output schema (not shown), the description provides a good overview of the tool's functionality and data sources. However, it does not mention the 'async' parameter or how to use it, which is critical for handling slow operations. The output schema covers return values, so that is not a gap.

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%, so the schema already documents all parameters (repo, since, until, githubToken, snykToken, async). The description reinforces the purpose of repo and time range but does not add new meaning beyond the schema. The 'async' parameter is not mentioned in the description, but the schema adequately explains it.

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 'analyze your team's incident response efficiency by breaking down Mean Time To Recovery (MTTR) into root causes'. It specifies the verb (analyze/break down), resource (MTTR by root causes), and target user (CTO). This distinguishes it from sibling tools like 'dora_metrics_deep_dive' which focuses on broader DORA metrics, and 'incident_response_evidence_collector' which collects raw evidence.

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 scenarios ('As a CTO, analyze...') but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites beyond the required inputs. There is no mention of when not to use it, e.g., if you need real-time incident data or have only a single incident.

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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