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 declare readOnlyHint=false and idempotentHint=true, covering side-effect and repeatability. The description adds the requirement to include a conversation array, which is useful behavioral context. It does not disclose any additional outcomes (e.g., where the report goes), and there is no contradiction with annotations.
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, front-loaded with the core purpose, and contains no filler. Every word adds value, 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 tool has only 3 parameters, no output schema, and simple behavior. The description covers the essential usage and the key parameter (conversation). It does not explain the 'context' parameter, which is a minor gap, but overall it is sufficiently complete for a reporting tool.
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. It explicitly mentions the 'conversation' parameter (as an array for reproduction) but does not explain 'context' or elaborate on the 'message' parameter beyond implying it is the report text. This is insufficient for full parameter understanding.
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 uses specific verbs (report, send) and identifies the resource (bug/feedback). This distinguishes it from sibling tools like authenticate or show_version, which are unrelated.
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 explicit instruction to 'Include the conversation array with recent messages for reproduction,' which gives some usage guidance. However, it does not mention when not to use this tool or contrast with alternatives, so 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.
Add one secure layer between your agents and this server.
Most tools have distinct purposes and detailed descriptions, but there are ambiguous boundaries around connection/auth status (`connect` vs `toolkit_info`) and the `marketplace` tool embeds sub-acts like `report_bug` and `list_tools` that overlap with separate tools. An agent can usually disambiguate, but not from names alone.
Names mix bare verbs (`authenticate`, `connect`), a bare noun (`marketplace`), verb_noun forms (`report_bug`, `show_version`), noun_info (`toolkit_info`), and one long Portuguese domain-specific identifier (`tribunal_tjpr_processo_consultar`). The overall snake_case is readable, but the English/Portuguese and verb/noun mix is not a coherent pattern.
Seven tools is within a reasonable count range, but six are generic mcp.ai platform utilities and only one is the actual TJPR/court domain tool. The server therefore feels inflated with infrastructure while its real domain surface remains minimal.
The direct domain coverage is essentially one generic `consultar` lookup. For a public court-process query service, a read-only tool is natural, but there are no search/browse/auxiliary operations or follow-up capabilities, leaving notable gaps if the tool is meant to support broader court research.