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 indicate idempotentHint=true and destructiveHint=false, covering safety aspects. The description adds behavioral context by instructing to include conversation for reproduction, which hints at expected usage but does not disclose side effects or response behavior. It neither contradicts nor enriches annotation info significantly, so it meets the baseline.
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 extremely concise—two short sentences that front-load the purpose and add a practical instruction. Every word earns its place, with no redundant phrasing or unnecessary details.
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?
With no output schema and only 3 parameters but 0% schema coverage, the description falls short. It does not explain what the tool returns, does not describe the message or context parameters, and lacks any mention of prerequisites or side effects. For a tool that is primarily for user feedback, this is insufficient for an agent to invoke it correctly without additional assumptions.
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 only mentions the 'conversation' parameter explicitly ('Include the conversation array'), while the required 'message' parameter and optional 'context' are not described. This leaves the agent guessing about the semantics of other parameters, especially since the schema itself provides no descriptions.
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 with a specific verb ('Report') and resource ('bug, missing feature, or send feedback'), making it distinct from sibling tools like billing or authentication. It also adds a key detail about including conversation data for reproduction, which helps differentiate its use case.
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 when to use the tool (when reporting issues or feedback) but does not explicitly state when not to use it or list alternatives. However, given the sibling set, the context is clear enough that no other tool would handle bug reports, so it is adequate.
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 clearly distinct purposes, especially the aws_billing_* group. The main ambiguity is between 'connect' and 'toolkit_info', both of which report connection status, and 'authenticate' overlaps slightly with connection management. However, descriptions are detailed enough to reduce misselection.
The aws_billing_* tools follow a consistent prefix pattern, but the rest are wildly inconsistent: authenticate, connect, marketplace, report_bug, show_version, toolkit_info. There's no uniform verb-noun convention, and mixed styles (snake_case vs plain words) make the set feel chaotic.
With 10 tools, the count is within the acceptable range. However, the server name suggests AWS Billing, yet only 4 tools are billing-related while the other 6 are generic MCP platform utilities. This scope mismatch makes the count feel inappropriate for the stated purpose.
For an AWS Billing server, the surface is missing critical operations: no budgets, alerts, cost allocation tags, or write actions (e.g., creating reports). The read-only cost, forecast, and anomalies cover only basic visibility. The presence of unrelated platform tools further dilutes billing completeness.