Miyabi MCP Bundle

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 retrieves data ('Get'), implying a read-only operation, but does not specify authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior beyond basic functionality.

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 a single, efficient sentence that front-loads the core action ('Get full issue details') and lists key components (body, labels, assignees, timeline). There is no wasted verbiage, and every word contributes to understanding the tool's purpose, making it highly concise and well-structured.

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?

The description is minimal but adequate for a simple read operation with one parameter and no output schema. It covers the basic purpose but lacks details on behavioral aspects (e.g., authentication, errors) and usage context. Without annotations or an output schema, it provides enough to infer functionality but falls short of being fully informative for an AI agent in complex scenarios.

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 100% description coverage, with one parameter ('issue_number') clearly documented. The description does not add any meaning beyond the schema, such as explaining what an issue number is or its format. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description.

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 verb ('Get') and resource ('full issue details'), specifying what information is retrieved (body, labels, assignees, timeline). It distinguishes itself from sibling tools like 'github_list_issues' (which lists issues) by focusing on detailed retrieval of a single issue. However, it does not explicitly contrast with 'github_update_issue' or 'github_create_issue', leaving some sibling differentiation implicit.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., needing an issue number), exclusions, or comparisons to siblings like 'github_list_issues' for overviews or 'github_get_pr' for pull requests. Usage is implied by the action but lacks explicit context.

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