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?
The annotations already indicate idempotent and non-destructive, and the description further instructs to include the conversation array for reproduction, which is a useful behavioral detail beyond the annotations. It doesn't disclose the response format or side effects, but given the simple nature and annotation coverage, this is adequate.
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?
One concise sentence, front-loaded with purpose and a clear instruction. No wasted words.
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 adequately covers the core purpose and one parameter, but it omits the 'context' parameter details and any post-report behavior. For a simple tool with no output schema, it is adequate but not complete.
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?
With 0% schema description coverage, the description carries the burden. It explains the 'conversation' parameter's purpose but doesn't mention 'message' or 'context' parameters, and it ambiguously calls 'conversation' an array while the schema defines it as a string. This could mislead an agent.
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 action ('Report') and the objects ('bug, missing feature, or send feedback'), which aligns with the tool name and distinguishes it from sibling tools dealing with authentication or marketplaces.
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 implies use for reporting issues or feedback, and the sibling tools are clearly unrelated, so an agent can infer when to use it. However, it doesn't explicitly list cases where this tool should not be used or alternative tools.
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.
Each tool has a clearly distinct function: authentication, the specific Bolsa Família query, connection status, marketplace operations, bug reporting, version display, and toolkit info. There is slight overlap between `connect` and `toolkit_info` regarding connection status, but descriptions clarify their different scopes.
Tool names mix languages and conventions: English verbs (`authenticate`, `connect`), snake_case Portuguese (`bolsa_familia_consultar`), single-word nouns (`marketplace`), and verb_noun in English (`report_bug`, `show_version`). This inconsistency makes the naming pattern unpredictable.
Seven tools is a reasonable number, but the scope is muddled: six tools are generic MCP.AI platform operations while only one is specific to the 'Bolsa Família' domain. This mismatch makes the count feel bloated for the apparent purpose of a benefits query server.
If the domain is Bolsa Família, the surface is severely incomplete (only a single consult operation, no eligibility or benefit details). If the domain is the mcp.ai platform, it is fairly complete (auth, marketplace, toolkit, feedback, version), but the server name suggests otherwise. Notable gaps exist under either interpretation.