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Fund-z

FundzWatch MCP Server

get_scored_leads

Retrieve AI-scored sales leads by filtering companies with recent business events based on buyer intent, buying stage, and industry criteria to identify high-potential prospects.

Instructions

Get AI-scored sales leads based on your ICP (Ideal Customer Profile). Returns companies with recent business events scored by AI for buyer intent, buying stage, and recommended outreach strategy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_scoreNoMinimum buyer intent score (0-100). Default: 0
max_resultsNoMax leads to return (1-50). Default: 25
buying_stagesNoFilter by buying stage: 'Active Evaluation', 'Decision', 'Research', 'Awareness'
industriesNoFilter by industry (e.g., ['SaaS', 'HealthTech', 'FinTech'])

Implementation Reference

  • The handler for `get_scored_leads` which performs an API request to `/signals` and formats the results.
    case "get_scored_leads": {
      const data = await apiRequest("POST", "/signals", {
        min_score: (args as any).min_score ?? 0,
        max_results: (args as any).max_results ?? 25,
        buying_stages: (args as any).buying_stages,
        industries: (args as any).industries,
      });
      const leads = data.signals || [];
      if (leads.length === 0) {
        return textResult("No scored leads found for your current ICP. Leads are generated daily by the AI scoring engine.");
      }
      const summary = leads
        .map(
          (lead: any, i: number) =>
            `${i + 1}. **${lead.company_name}** (Score: ${lead.score}/100)\n` +
            `   Stage: ${lead.buying_stage} | Priority: ${lead.priority}\n` +
            `   Pain Point: ${lead.pain_point}\n` +
            `   Outreach: ${lead.outreach_angle}\n` +
            `   Action: ${lead.recommended_action}\n` +
            (lead.domain ? `   Domain: ${lead.domain}\n` : "") +
            (lead.industries?.length ? `   Industries: ${lead.industries.join(", ")}\n` : "")
        )
        .join("\n");
      return textResult(`Found ${data.signals_found} scored leads (showing ${leads.length}):\n\n${summary}`);
    }
    
    case "get_events": {
      const data = await apiRequest("GET", "/events", {
        types: (args as any).types,
        days: (args as any).days,
        limit: (args as any).limit,
        industries: (args as any).industries,
  • src/index.ts:73-96 (registration)
    Definition and registration of the `get_scored_leads` tool.
    {
      name: "get_scored_leads",
      description:
        "Get AI-scored sales leads based on your ICP (Ideal Customer Profile). " +
        "Returns companies with recent business events scored by AI for buyer intent, " +
        "buying stage, and recommended outreach strategy.",
      inputSchema: {
        type: "object" as const,
        properties: {
          min_score: { type: "number", description: "Minimum buyer intent score (0-100). Default: 0" },
          max_results: { type: "number", description: "Max leads to return (1-50). Default: 25" },
          buying_stages: {
            type: "array",
            items: { type: "string" },
            description: "Filter by buying stage: 'Active Evaluation', 'Decision', 'Research', 'Awareness'",
          },
          industries: {
            type: "array",
            items: { type: "string" },
            description: "Filter by industry (e.g., ['SaaS', 'HealthTech', 'FinTech'])",
          },
        },
      },
    },
Behavior3/5

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

With no annotations provided, the description carries full disclosure burden. It successfully explains what the AI scoring evaluates (intent, stage, strategy) and that results include recent business events, but omits operational details like read-only safety, rate limits, or caching behavior.

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 well-constructed sentences with zero waste. First sentence establishes purpose and ICP context immediately; second sentence details return values. Perfectly front-loaded and appropriately sized.

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?

Without an output schema, the description adequately discloses return values (companies with scored dimensions). The 100% parameter coverage means the description doesn't need to elaborate on inputs. Minor deduction for not confirming read-only nature given lack of annotations.

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 coverage is 100% with clear descriptions for all four parameters. The description adds minimal semantic meaning beyond the schema other than the ICP framing, which contextualizes why one would filter by industries or buying stages. Baseline 3 is appropriate given the schema's completeness.

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?

Clearly identifies the resource (AI-scored sales leads) and action (Get), and distinguishes from siblings by specifying ICP-based matching and AI-generated dimensions (buyer intent, buying stage, outreach strategy). Falls short of 5 only because it does not explicitly contrast with sibling tools like get_events.

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?

Implies targeted use case via 'based on your ICP (Ideal Customer Profile),' hinting that an ICP must be configured first. However, lacks explicit when-to-use guidance relative to get_events or get_watchlist_events, and does not state prerequisites clearly.

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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