report_bug
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-readOnly (false), non-destructive, and idempotent, but the description adds the behavioral note that it requires reproduction details (conversation array). This adds value beyond annotations, which are relatively generic. However, it does not detail what happens after reporting (e.g., response, tracking) or any side effects. Given annotations cover the basic safety profile, a score of 3 is appropriate.
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 two sentences: one stating the purpose and one instructing on the conversation array. It is front-loaded with the main purpose and contains no fluff. Each sentence serves a distinct function (purpose and key guidance).
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 tool is relatively simple with 3 params and no output schema. The description conveys the main purpose and gives one essential guidance (include conversation for reproduction). However, it lacks details on what the 'message' parameter should contain, what 'context' is for, and what the user can expect after submission (e.g., no confirmation). Given the low complexity, a score of 3 is fair - it covers the core but misses some practical details.
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?
Schema description coverage is 0%, so the description must compensate. However, the description only mentions the 'conversation' parameter implicitly ('Include the conversation array') and doesn't explain the 'message' (required) or 'context' parameters. The meaning of 'message' is implied but not explicit, and 'context' is not described at all. This is insufficient given the lack of schema descriptions.
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: report a bug, missing feature, or send feedback. It is specific about the action and resource (reporting issues), and it differs from sibling tools like 'authenticate' or 'show_version' by being user feedback-oriented. However, it doesn't explicitly distinguish itself from all siblings, such as 'toolkit_info' which might also handle feedback, but the core purpose is clear.
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 gives a hint on when to use the tool (to report a bug, missing feature, or feedback) and implicitly says to include the conversation array for reproduction. However, it does not explicitly state when not to use it or mention alternatives. The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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