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get_digest

Retrieve your top matches, pending introductions, and incoming requests in one call to see everything relevant to your networking needs.

Instructions

What matters to you right now? Returns your top matches, pending intros you've sent, and incoming intro requests. One call, everything relevant.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for get_digest tool that fetches digest data from the API and returns summary, matches, intros sent/waiting, and intro details in a formatted JSON response
    async () => {
      try {
        const d = await api(`/api/digest/${agentId}`);
        if (d.error) return { content: [{ type: "text" as const, text: d.error }], isError: true };
    
        return {
          content: [{
            type: "text" as const,
            text: JSON.stringify({
              summary: d.summary,
              networkSize: d.networkSize,
              hasCard: d.hasCard,
              matches: (d.matches || []).slice(0, 5).map((m: any) => ({
                person: m.agentA === agentId ? m.agentB : m.agentA,
                score: m.score,
                explanation: m.explanation,
              })),
              introsSent: (d.introsPending || []).length,
              introsWaiting: (d.introsReceived || []).length,
              introsDetail: (d.introsReceived || []).map((i: any) => ({
                introId: i.introId,
                from: i.requestedBy,
                message: i.message,
              })),
              note: !d.hasCard ? "No card published yet. Use publish_intent_card to join the network." : undefined,
            }, null, 2),
          }],
        };
      } catch (e: any) {
        return { content: [{ type: "text" as const, text: `Network error: ${e.message}` }], isError: true };
      }
    }
  • src/index.ts:157-160 (registration)
    Registration of get_digest tool with MCP server, including tool name, description, and empty schema object (no parameters required)
    server.tool(
      "get_digest",
      "What matters to you right now? Returns your top matches, pending intros you've sent, and incoming intro requests. One call, everything relevant.",
      {},
  • Helper function used by get_digest handler to make authenticated API calls with agent ID and public key headers
    async function api(path: string, opts?: RequestInit): Promise<any> {
      const res = await fetch(`${API}${path}`, {
        ...opts,
        headers: {
          "Content-Type": "application/json",
          "X-Agent-Id": agentId,
          "X-Public-Key": keys.publicKey,
          ...opts?.headers,
        },
      });
      return res.json();
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions returning data but doesn't disclose behavioral traits such as authentication requirements, rate limits, data freshness, or error handling. The phrase 'top matches' hints at ranking but lacks details on criteria or limits, leaving gaps in transparency.

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

Conciseness4/5

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

The description is concise and front-loaded, with two sentences that efficiently convey the tool's purpose and value proposition ('One call, everything relevant'). Every sentence earns its place, though the rhetorical question 'What matters to you right now?' adds slight fluff without critical information.

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 no annotations and no output schema, the description is incomplete for a tool that returns multiple data types. It lists output categories but lacks details on format, structure, or examples, which could hinder an agent's ability to interpret results. More context on the return data would improve completeness.

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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, focusing on the tool's output scope. This meets the baseline for zero parameters, as it doesn't introduce unnecessary complexity.

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 tool's purpose: 'Returns your top matches, pending intros you've sent, and incoming intro requests.' It specifies the verb 'returns' and lists the three resource types (matches, pending intros, incoming requests). However, it doesn't explicitly differentiate from sibling tools like 'search_matches' or 'respond_to_intro', which might offer overlapping functionality.

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 with 'What matters to you right now?' and 'One call, everything relevant,' suggesting it's for getting a comprehensive overview. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_matches' for specific queries or 'respond_to_intro' for handling requests, leaving some ambiguity.

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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