Code Review MCP

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool runs an AI-powered code review but doesn't describe what that entails—e.g., whether it modifies code, requires authentication, has rate limits, or what the output looks like. This leaves significant gaps in understanding the tool's behavior, especially for a potentially complex operation like code review.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, with the core purpose stated first in a single sentence. The usage example is brief and relevant. However, it could be more structured by separating purpose from usage instructions, and some information is implied rather than explicit, but overall it avoids unnecessary verbosity.

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

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

Given the complexity of an AI code review tool, no annotations, no output schema, and low parameter coverage, the description is incomplete. It doesn't explain what the tool returns, how it interacts with GitHub, or any behavioral nuances. This makes it inadequate for an agent to fully understand and use the tool effectively in context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions parameters '[prId] [--flags]' but doesn't explain their meaning or usage. With 3 parameters in the schema and 0% schema description coverage, the description fails to compensate by providing details on what 'prId', 'options', or 'aiOutput' represent. This leaves parameters largely undocumented, hindering effective tool invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run an AI-powered code review for a GitHub PR.' This specifies the verb ('Run'), resource ('code review'), and context ('GitHub PR'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'security.run_semgrep' or 'analysis.run_static', which might offer overlapping functionality, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a usage example ('@code-review [prId] [--flags]'), which implies how to invoke the tool but offers no guidance on when to use it versus alternatives. There is no mention of when this tool is appropriate compared to siblings like 'security.run_semgrep' or 'analysis.run_static', nor any context on prerequisites or exclusions, leaving the agent with minimal usage direction.

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