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 provide readOnlyHint false and destructiveHint false. The description adds the instruction to include conversation for reproduction, but does not discuss side effects, auth requirements, or what happens after submission. It also doesn't clarify the idempotentHint annotation.
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 sentence, front-loaded with purpose and a single instructional addition. Efficient.
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 tool with 3 parameters and no output schema, the description is incomplete: it doesn't explain the return behavior, the required message field, or the context field, and the conversation type mismatch creates confusion.
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 coverage is 0%, so the description must compensate. It explains the 'conversation' parameter (though calls it an array while schema types it as a string), but does not clarify the required 'message' parameter or the 'context' parameter. This is a significant gap.
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 lists the resource (bug, missing feature, feedback), clearly distinguishing it from sibling tools like authenticate or marketplace.
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?
It gives clear context for when to use (to report issues or feedback) but does not explicitly mention alternatives or exclusions. Since no sibling is similar, this is acceptable.
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.
Every tool has a clearly distinct purpose: authenticate for token login, cepim_consultar for CNPJ checks, connect for status, marketplace for discovery/running, report_bug, show_version, and toolkit_info. There is no functional overlap between tools.
Naming is inconsistent: verbs (authenticate, connect), nouns (marketplace), verb_noun (report_bug, show_version), and noun_noun (toolkit_info). The only domain tool, cepim_consultar, reverses the verb_noun pattern, breaking any coherent naming scheme.
Seven tools is moderate, but the server is named 'CEPIM' and only one tool relates to that domain. The other six are generic platform utilities, making the set feel padded and poorly scoped to the intended purpose.
For the CEPIM domain, only a single query operation exists with no additional workflows (e.g., bulk verification, historical checks). While the platform side is thorough, the CEPIM-specific surface is shallow and incomplete for a dedicated service.