OpenCollab MCP

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

Annotations already provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), indicating this is a safe, repeatable read operation. The description adds valuable context beyond annotations by detailing the analysis factors (issue body length, comments, labels, linked PRs, discussion depth) and the output (complexity rating), which helps the agent understand the tool's behavior and scope. No contradictions with annotations exist.

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 front-loaded with the core purpose in the first sentence, followed by specific analysis details in the second. Every sentence adds value without redundancy, and the structure is clear and efficient, making it easy for an agent to parse and understand quickly.

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 tool's moderate complexity (analysis of multiple factors), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is complete enough. It details the analysis factors and output, aligning well with the structured data to provide a holistic understanding without unnecessary repetition.

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 input schema has 0% description coverage, but the description compensates by clarifying that the tool analyzes a 'specific GitHub issue,' implying parameters like repository owner, repo name, and issue number. Although it does not explicitly list or explain each parameter, it provides enough semantic context to infer the required inputs. With 0% schema coverage, the description does well to add meaning, but could be more explicit about parameter roles.

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 specific action ('Estimate the complexity') and target resource ('a specific GitHub issue'), with explicit details about what factors are analyzed (issue body length, number of comments, labels, linked PRs, and discussion depth) and the output (complexity rating). It distinguishes itself from siblings like 'opencollab_find_issues' or 'opencollab_stale_issue_finder' by focusing on complexity analysis rather than discovery or filtering.

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 implies usage context by specifying it analyzes a 'specific GitHub issue,' suggesting it should be used when an issue is already identified. However, it does not explicitly state when to use this tool versus alternatives (e.g., compared to 'opencollab_issue_availability' for checking issue status or 'opencollab_find_issues' for discovering issues), nor does it provide exclusions or prerequisites. The guidance is present but limited to implied context.

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