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 readOnlyHint=false, destructiveHint=false, and idempotentHint=true, covering some behavioral aspects. The description adds the instruction to include the conversation array, which is more about usage than behavior. It does not disclose what happens after reporting (e.g., where the data goes, persistence), but since annotations already reveal it is a non-read, non-destructive operation, the description does not need to repeat those. It adds minimal extra context, so a 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 exceptionally concise—two sentences, each earning its place. The first states the purpose, and the second gives a critical usage instruction. There is no redundancy or filler, making it easy for an agent to parse 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?
The tool is simple with no output schema, but the description still lacks information about expected outcomes or side effects. It does not mention what happens after reporting, any confirmation, or error conditions. Annotations provide some behavioral context but not completeness. The instruction to include the conversation array is useful, but for a tool that likely sends data externally, more context about the action's consequences would be needed.
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. The description explicitly mentions the 'conversation' parameter ('Include the conversation array with recent messages'), which adds meaning beyond the schema's default value. However, it does not explain 'message' or 'context' beyond the obvious. Given the low coverage and only partial elucidation, the description falls short of adequately documenting all parameters.
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: to report a bug, missing feature, or send feedback. The verb 'Report' and resources ('bug, missing feature, feedback') are specific enough to distinguish it from sibling tools like 'authenticate' or 'sefaz_mg_simulacao_ipva_consultar'. However, it does not explicitly contrast with alternatives, so it falls short of a 5.
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 an operational guideline: 'Include the conversation array with recent messages for reproduction.' This tells the agent how to use the tool effectively but does not specify when to use this tool versus alternatives or under what circumstances it should be preferred. The usage context is implied rather than explicit.
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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