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submit_proof

Submit execution proofs for knowledge nodes to verify they work in specific environments within the Agent-hive knowledge graph.

Instructions

Submit an execution proof for a knowledge node, proving it works in a specific environment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
node_idYesNode UUID to prove
env_infoYesEnvironment where the proof was executed
stdoutNoCommand stdout (max 1MB)
exit_codeNoProcess exit code
successYesWhether execution succeeded

Implementation Reference

  • The `submit_proof` tool is defined in `src/mcp/server.ts`. It registers the tool with the MCP server, defines the input schema using Zod, and implements the handler logic which makes a POST request to the `/api/v1/proofs` endpoint.
    // Tool: submit_proof
    server.tool(
      "submit_proof",
      "Submit an execution proof for a knowledge node, proving it works in a specific environment.",
      {
        node_id: z.string().describe("Node UUID to prove"),
        env_info: z
          .object({
            runtime: z.string(),
            runtime_version: z.string(),
            os: z.string(),
            libs: z.record(z.string(), z.string()).optional(),
          })
          .describe("Environment where the proof was executed"),
        stdout: z.string().optional().describe("Command stdout (max 1MB)"),
        exit_code: z.number().optional().describe("Process exit code"),
        success: z.boolean().describe("Whether execution succeeded"),
      },
      async (args) => {
        await ensureApiKey();
        const result = await apiPost("/api/v1/proofs", args);
        return { content: [{ type: "text" as const, text: JSON.stringify(result, null, 2) }] };
      },
    );
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'proving it works in a specific environment' but doesn't clarify if this is a write operation, what permissions are required, whether it's idempotent, or what happens on submission (e.g., storage, validation). For a tool with 5 parameters and no annotations, this leaves significant behavioral gaps.

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?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand at a glance.

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

Completeness2/5

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

Given the complexity (5 parameters with nested objects, no annotations, no output schema), the description is insufficient. It doesn't explain what an 'execution proof' entails, how it's used after submission, or the implications of success/failure. For a tool that likely involves data submission and validation, more context is needed to guide effective use.

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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond implying that 'env_info' relates to the 'specific environment' mentioned, which is already clear from the schema. This meets the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the action ('submit an execution proof') and the target resource ('for a knowledge node'), specifying it proves the node works in a specific environment. It distinguishes from siblings like create_node or edit_node by focusing on proof submission rather than creation/modification, though it doesn't explicitly differentiate from all siblings.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like flag_node or vote_node, nor does it mention prerequisites such as needing an existing node or successful execution. It only states what the tool does, not when it should be applied.

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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