Gitopia MCP Server

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

With no annotations provided, the description carries critical behavioral weight by disclosing 'Signs and broadcasts a transaction on-chain,' alerting the agent to write-operation semantics, gas costs, and signing requirements. It also clarifies the inline vs. general comment distinction.

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 efficiently structured with five high-value sentences: purpose statement, capability scope, behavioral disclosure, parameter mapping, and related tools. No redundancy or filler.

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 lack of annotations and output schema, the description adequately covers the essential behavioral and operational context. It could be improved by mentioning success indicators or common error conditions (e.g., PR not found).

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?

While the schema has 100% coverage, the description adds value by semantically grouping the optional parameters ('diff_hunk', 'path', 'position') as specific to 'inline comments' and explicitly contrasting required vs. optional fields.

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 explicitly states the tool 'leave[s] review comments on a pull request' and distinguishes between 'general comments and inline comments,' clearly differentiating it from the sibling tool comment_on_issue.

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?

It provides explicit when-to-use guidance ('Use this when you need to leave review comments') and references related tools ('See also: get_pull_request, list_pull_requests'), though it could explicitly contrast with comment_on_issue.

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