coderocket-mcp

list_reviews

List AI code reviews from CodeRocket, with optional repository filtering to view reviews for a specific project.

Instructions

List AI code reviews, optionally filtered by repository.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_reviews"
  }
}

Input Schema

Name	Required	Description	Default
`repository_id`	No	Optional repository ID to filter reviews

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.2Aug 7, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It merely states the action and an optional filter, but does not disclose whether the tool is read-only, whether results are paginated, what fields are returned, or any authorization requirements. For an agent, this is insufficient to anticipate behavior beyond the obvious.

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 a single, focused sentence that immediately states the tool's purpose. There is no redundant verbiage, and the key qualifier (optional repository filter) is included. It is perfectly sized for the scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description is the only source for return semantics. It does not explain what a 'review' object looks like, whether the list is paginated, or if it returns only the current user's reviews. For a listing tool, this leaves the agent guessing about the response structure and dependencies.

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% since repository_id has a clear description. The tool description's mention of 'optionally filtered by repository' adds no new meaning beyond the schema. Baseline 3 is appropriate because the schema already documents the parameter adequately.

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 'List AI code reviews, optionally filtered by repository' uses a specific verb ('List') and identifies the resource ('AI code reviews'), clearly distinguishing this from sibling tools like get_review (which likely fetches a single review) and list_repos (which lists repositories).

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 for listing all or repository-filtered reviews, but it does not explicitly state when to use this tool over alternatives such as get_review. No when-to-use or when-not-to-use guidance is provided, though the sibling context offers some implicit differentiation.

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

Deploy Server