ccg-mcp-tool

coordinate-review

Automatically selects and runs the minimum set of research-grounded skill gates for features, bugfixes, refactors, or dependency updates. Returns gate-by-gate findings, blocking issues, and a merge recommendation.

Instructions

Coordinated multi-gate review. Auto-selects the minimum set of research-grounded skill gates for a given task type (feature/bugfix/refactor/dependency-update) and runs them in a single structured pass. Produces gate-by-gate findings, blocking issues, and a final merge recommendation. Uses the AI Coding Agent Mitigator coordinator pattern.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>",
      "task_type": "<task_type>"
    },
    "name": "coordinate-review"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	The code, diff, or task description to review.
`task_type`	Yes	The type of change being made. Determines which minimum skill gates are applied: feature (8 gates), bugfix (7 gates), refactor (7 gates), dependency-update (7 gates).
`provider`	No	Provider to use (e.g., 'gemini', 'codex', 'claude'). Defaults to server config ('gemini').	gemini
`model`	No	Optional model override. Defaults to server config (provider default).

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.3.1May 3, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It clearly explains the tool runs a set of gates, produces findings and blocking issues, and gives a merge recommendation. The mention of the coordinator pattern adds behavioral context. It does not mention side effects, but as a review tool, read-only behavior is implied.

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, each contributing essential information. It front-loads the core purpose and uses no unnecessary words. Every sentence earns its place with specific details about function, task types, 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's complexity (multi-gate review, multiple parameters, no output schema), the description adequately explains what it does and what it produces. It covers inputs from the schema and gives a clear idea of outputs, though it could mention potential failure modes or prerequisites for completeness.

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 four parameters. The description adds limited extra meaning beyond what is in the schema, such as explaining that task_type determines gate count. Baseline 3 is appropriate as the description compensates slightly but not significantly.

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 performs a coordinated multi-gate review, auto-selecting skill gates based on task type and producing findings and a merge recommendation. This distinguishes it from all sibling tools (e.g., 'mitigate-mistakes', 'ask-ai') by its specific function.

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 for when to use the tool—specifically for task types feature, bugfix, refactor, or dependency-update. However, it does not explicitly state when not to use it or compare to alternatives, leaving usage decision somewhat implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server