Use when: a solution is factually wrong, malicious, contains PII/secrets, or is spam. Returns: report count. Reports are queued for human review and never remove a solution. Do not use when: the fix did not work in your specific case or you merely disagree with the approach.
| Name | Required | Description | Default |
|---|---|---|---|
| solution_id | Yes | ID of the solution to report |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "description": "Submitted community report; may auto-remove at threshold.",
- "properties": {
- "error": {
- "description": "Present when the tool call failed.",
- "type": "string"
- },
- "field": {
- "description": "Input field related to the error, if any.",
- "type": "string"
- },
- "message": {
- "type": "string"
- },
- "removed": {
- "type": "boolean"
- },
- "report_count": {
- "type": "integer"
- },
- "solution_id": {
- "type": "integer"
- },
- "success": {
- "type": "boolean"
- }
- },
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (non-read-only, non-destructive), the description adds key behavioral context: reports are queued for human review span, never remove solutions, and return a report count. This is exactly the kind of side-effect information an agent needs.
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 compact and front-loaded: conditions for use, return value, behavioral side-effect, and exclusions. Every sentence contributes decision-relevant information with no waste.
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 one-parameter tool with no output schema, this covers when to use it, what it returns, and what it does not do. The only slight gap is that 'report count' is a little ambiguous about whether it is the total report count or a confirmation count, but this does not block correct invocation.
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 100%, and the single parameter is already described as 'ID of the solution to report.' The description adds no parameter-level meaning, so baseline 3 is appropriate.
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 pinpoints exactly when the tool applies: factually wrong, malicious, PII/secrets, or spam. The verb and resource are clear from the title and 'solution' references, and the boundary 'do not use when the fix did not work or you merely disagree' distinguishes this from feedback/edit tools.
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?
Explicit 'Use when' and 'Do not use when' conditions give an agent clear positive and negative guidance. However, it does not name a specific sibling tool as the alternative for the disagreement case, so it stops just short of full routing guidance.
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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