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Gonzih

nexus-convergence-mcp

by Gonzih

get_evidence_ladder

Retrieve the complete audit trail for a query, showing each step from decomposition through model execution, consensus, verification, and conclusion with confidence scores and timestamps.

Instructions

Return the full Evidence Ladder for a query — immutable append-only audit trail showing every step: QUERY → DECOMPOSE → MODEL_EXECUTE → CONSENSUS → VERIFY → CONCLUDE. Each entry records: step, actor (model name or "system"), content, confidence, and timestamp.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idYesThe run/query ID returned by converge_query

Implementation Reference

  • Tool schema/definition for 'get_evidence_ladder' registered in ListToolsRequestSchema handler. Defines name, description, and inputSchema requiring query_id (string).
    {
      name: 'get_evidence_ladder',
      description:
        'Return the full Evidence Ladder for a query — immutable append-only audit trail showing every step: QUERY → DECOMPOSE → MODEL_EXECUTE → CONSENSUS → VERIFY → CONCLUDE. ' +
        'Each entry records: step, actor (model name or "system"), content, confidence, and timestamp.',
      inputSchema: {
        type: 'object' as const,
        required: ['query_id'],
        properties: {
          query_id: {
            type: 'string',
            description: 'The run/query ID returned by converge_query',
          },
        },
  • Handler for 'get_evidence_ladder' tool call. Validates input via Zod schema (query_id: string), calls getEvidenceLadder(params.query_id), and returns the result as JSON text.
    case 'get_evidence_ladder': {
      const schema = z.object({ query_id: z.string() });
      const params = schema.parse(args);
      const result = await getEvidenceLadder(params.query_id);
    
      return {
        content: [
          {
            type: 'text' as const,
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
  • The getEvidenceLadder function that performs an HTTP GET to ${EVIDENCE_SERVICE_URL}/evidence/${queryId} and returns a Promise<EvidenceLadder>. It delegates to a remote service at the EVIDENCE_SERVICE_URL (default http://localhost:3002).
    export async function getEvidenceLadder(queryId: string): Promise<EvidenceLadder> {
      return get<EvidenceLadder>(`${EVIDENCE_SERVICE_URL}/evidence/${queryId}`);
    }
  • EvidenceLadder TypeScript interface defining the response type: query_id, entry_count, and ladder array with step details (id, step, step_order, actor, content, confidence, created_at).
    export interface EvidenceLadder {
      query_id: string;
      entry_count: number;
      ladder: Array<{
        id: string;
        step: string;
        step_order: number;
        actor: string;
        content: string;
        confidence: number | null;
        created_at: string;
      }>;
    }
  • src/index.ts:35-40 (registration)
    Import of getEvidenceLadder from './lib/client.js' in the main server file.
    import {
      convergeQuery,
      getEvidenceLadder,
      checkCompliance,
      listModelDisagreements,
    } from './lib/client.js';
Behavior5/5

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

The description explicitly notes the tool is 'immutable append-only audit trail', indicating no side effects or destructive actions. It also details the exact steps recorded, providing full transparency about the content.

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 sentences: first introduces purpose with steps, second details fields. No wasted words. Front-loaded.

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

Completeness5/5

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

Despite no output schema, the description lists all fields (step, actor, content, confidence, timestamp), so return format is clear. Also covers immutability. For a simple single-param tool, this is complete.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by linking query_id to 'converge_query', clarifying its origin. This extra context justifies a 4.

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 returns the full Evidence Ladder for a query, listing the specific steps (QUERY → DECOMPOSE → ...). It uses a specific verb 'Return' and resource 'Evidence Ladder', and distinguishes from siblings by describing the audit trail nature.

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 implies usage when one needs the full audit trail for a query, but does not explicitly state when to use this vs alternatives like list_model_disagreements or check_compliance. However, the purpose is clear enough that an agent would infer appropriate context.

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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