Skip to main content
Glama

report_outcome

Report solution success or failure to improve ranking in Hivemind MCP's debugging knowledge base.

Instructions

Report whether a solution worked or not. Helps improve solution rankings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
solution_idNoThe ID of the solution from search results.
outcomeYesDid the solution work?

Implementation Reference

  • The core implementation of the report_outcome tool. Sends HTTP POST to Hivemind backend (/report) with optional solution_id and required outcome (success/failure), returns confirmation.
    export async function reportOutcome(
      solutionId: number | undefined,
      outcome: "success" | "failure"
    ): Promise<OutcomeResult> {
      const response = await fetch(`${API_BASE}/report`, {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({ solution_id: solutionId, outcome }),
      });
    
      if (!response.ok) {
        throw new Error(`Report failed: ${response.statusText}`);
      }
    
      return response.json();
    }
  • MCP tool schema defining input parameters: optional solution_id (number), required outcome (success|failure). Used in ListTools response.
    {
      name: "report_outcome",
      description:
        "Report whether a solution worked or not. Helps improve solution rankings.",
      inputSchema: {
        type: "object",
        properties: {
          solution_id: {
            type: "number",
            description: "The ID of the solution from search results.",
          },
          outcome: {
            type: "string",
            enum: ["success", "failure"],
            description: "Did the solution work?",
          },
        },
        required: ["outcome"],
      },
    },
  • src/index.ts:366-374 (registration)
    MCP server registration: handles CallToolRequest for report_outcome by extracting args and invoking reportOutcome handler, returning JSON stringified result.
    case "report_outcome": {
      const result = await reportOutcome(
        args?.solution_id as number | undefined,
        args?.outcome as "success" | "failure"
      );
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • TypeScript interface defining the expected return type from the reportOutcome API call.
    interface OutcomeResult {
      success: boolean;
      message: string;
    }

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the transparency burden. It adds the behavioral consequence 'Helps improve solution rankings,' which discloses the effect of reporting. It does not mention auth, mutability, or the optional solution_id behavior, but the core action and outcome are clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler. The key action ('Report whether a solution worked') and the benefit ('improve solution rankings') are front-loaded, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter feedback tool, the description is adequate but omits clarification on the optional solution_id and what the tool returns or confirms. It also does not link the report explicitly to search results, though the schema partially covers this.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover 100% of parameters, including the outcome enum and solution_id explanation. The tool description adds no additional parameter semantics, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Report' and identifies the resource as 'a solution' with the outcome dimension. It clearly distinguishes from sibling tools like contribute_solution by focusing on feedback on existing solutions rather than adding new ones.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage after attempting a solution ("whether a solution worked or not") and the ranking improvement suggests it is for existing solutions. However, it does not explicitly state when to use this versus contribute_solution or mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.