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 already cover non-destructive and not read-only behavior, and the description adds that the conversation array is needed for reproduction. However, it doesn't explain what happens after reporting, whether a ticket is created, retry effects, or whether data is sent externally, so the behavioral picture is incomplete.
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 concise and front-loaded: two sentences communicate the purpose and the key usage instruction without filler or redundancy.
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?
For a simple flat submission tool with one required parameter and no output schema, this description provides enough context for basic invocation. It would be stronger if it mentioned expected return behavior and conversation formatting, but the core use case is adequately covered.
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 description provides meaningful guidance by explaining that the conversation array is used for reproduction. However, schema description coverage is 0%, and the description doesn't clarify the 'context' parameter, and it calls conversation an 'array' while the schema declares it as a string, leaving serialization ambiguity.
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 uses a specific verb, 'Report', and concretely lists the accepted subject matter: bugs, missing features, and feedback. This makes the tool's purpose immediately clear and distinguishes it from siblings like show_version or receita_federal_cafir_consultar.
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 clearly indicates when to use the tool: when users need to report a bug, request a missing feature, or send feedback. It doesn't explicitly name exclusions or alternatives, but the context is clear enough for an agent to select it appropriately.
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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