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@actalumen/mcp-server

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by ActaLumen

get_verification

Fetch the status of a verification job and retrieve the full report including per-criterion findings, citations, and pass/fail summary.

Instructions

Fetch verification job status and (when complete) the full report with per-criterion findings, citations, and pass/fail summary. Poll roughly every 5–10s after start_verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesJob ID returned by start_verification.

Implementation Reference

  • The handler function that executes the get_verification tool logic: calls client.get() with the jobId from the input schema.
    export const getVerification = defineTool({
      name: "get_verification",
      description:
        "Fetch verification job status and (when complete) the full report with per-criterion findings, citations, and pass/fail summary. Poll roughly every 5–10s after start_verification.",
      inputSchema: Input,
      handler: async ({ jobId }, { client }) => client.get(`/v1/reports/${encodeURIComponent(jobId)}`),
    });
  • Input schema for get_verification: requires a jobId string (returned by start_verification).
    const Input = z.object({
      jobId: z.string().describe("Job ID returned by start_verification."),
    });
  • Import and registration of getVerification in the tools array, making it available as a tool.
    import { getVerification } from "./get_verification.js";
    import { chatWithDocument } from "./chat_with_document.js";
    import { getUsage } from "./get_usage.js";
    
    export const tools: ToolDef[] = [
      uploadDocument,
      getDocument,
      listDocuments,
      listTemplates,
      startVerification,
      getVerification,
      chatWithDocument,
      getUsage,
    ];
  • The defineTool helper function used to define the get_verification tool, providing type inference and a standard shape.
    export function defineTool<Input extends z.ZodTypeAny>(t: {
      name: string;
      description: string;
      inputSchema: Input;
      handler: (input: z.infer<Input>, ctx: ToolContext) => Promise<unknown>;
    }): ToolDef {
      return t as unknown as ToolDef;
    }
Behavior3/5

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

Without annotations, the description carries the full burden of behavioral disclosure. It explains the output structure (status, per-criterion findings, citations, pass/fail summary) and mentions polling behavior. However, it does not specify error handling, valid status states, or whether the operation is read-only (though implied).

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 concise, with two sentences. The first sentence states the purpose and output, and the second provides a actionable usage hint. No extraneous information; every sentence is valuable and front-loaded.

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

Completeness4/5

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

Given the low complexity of the tool (1 parameter, no output schema), the description provides a fairly complete picture: it explains the outputs, ties to a sibling tool (start_verification), and offers polling guidance. It lacks details on error cases or rate limits, but covers essentials.

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 baseline is 3. The description adds the polling timing context but does not significantly enhance the parameter meaning beyond what the schema already provides ('Job ID returned by start_verification').

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 clearly states the tool's purpose: 'Fetch verification job status and (when complete) the full report'. It uses specific verbs and resources and effectively distinguishes itself from sibling tools like start_verification.

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

Usage Guidelines4/5

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

The description provides explicit usage context: 'Poll roughly every 5–10s after start_verification.' This indicates when to use it (after starting a verification) and suggests a polling frequency. However, it does not explicitly mention when not to use it or alternatives.

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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