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gomessoaresemmanuel-cpu

linkedin-prospection-mcp

Get Pipeline Status

get_pipeline_status
Read-only

Monitor LinkedIn prospection pipeline status: track lead counts, invitations, messages, acceptance rates, and identify next actions.

Instructions

Get the current state of your LinkedIn prospection pipeline: lead counts by status, invitations sent, DMs sent, acceptance rates, and next actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool `get_pipeline_status` is implemented here as an MCP tool handler in `src/index.ts`. It reads from `getDailyLog()` to report on pipeline status, lead counts, and next actions.
    server.registerTool(
      "get_pipeline_status",
      {
        title: "Get Pipeline Status",
        description:
          "Get the current state of your LinkedIn prospection pipeline: " +
          "lead counts by status, invitations sent, DMs sent, acceptance rates, and next actions.",
        inputSchema: {},
        annotations: { readOnlyHint: true, openWorldHint: false, destructiveHint: false },
      },
      async () => {
        const log = getDailyLog() as {
          date?: string;
          invitations_sent?: number;
          dms_sent?: number;
          leads_p1?: Array<{ name: string; status: string; priority: string; linkedin_url?: string }>;
          leads_p2?: Array<{ name: string; status: string; priority: string; linkedin_url?: string }>;
        };
    
        const allLeads = [...(log.leads_p1 || []), ...(log.leads_p2 || [])];
    
        const statusCounts: Record<string, number> = {};
        allLeads.forEach((l) => {
          const s = l.status || "unknown";
          statusCounts[s] = (statusCounts[s] || 0) + 1;
        });
    
        const lines = [
          `=== Pipeline Status (${log.date || "today"}) ===`,
          "",
          `Total leads tracked: ${allLeads.length}`,
          `Invitations sent: ${log.invitations_sent || 0}`,
          `DMs sent: ${log.dms_sent || 0}`,
          "",
          "--- Status Breakdown ---",
        ];
    
        Object.entries(statusCounts).forEach(([status, count]) => {
          lines.push(`  ${status}: ${count}`);
        });
    
        lines.push("", "--- Lead Details ---");
        allLeads.forEach((l) => {
          lines.push(`  [${l.priority}] ${l.name} — ${l.status}${l.linkedin_url ? ` — ${l.linkedin_url}` : ""}`);
        });
    
        // Session check
        lines.push("");
        if (fs.existsSync(SESSION_DIR)) {
          lines.push("LinkedIn session: ACTIVE");
        } else {
          lines.push("LinkedIn session: EXPIRED — run setup-session.js");
        }
    
        // Next actions
        lines.push("", "--- Next Actions ---");
        const pendingInvites = allLeads.filter((l) => l.status === "pending_invitation").length;
        const awaitingAccept = allLeads.filter((l) => l.status === "invitation_sent").length;
        const readyForDM = allLeads.filter((l) => l.status === "connection_accepted").length;
    
        if (pendingInvites > 0) lines.push(`  Send ${pendingInvites} pending invitations`);
        if (awaitingAccept > 0) lines.push(`  ${awaitingAccept} invitations awaiting acceptance`);
        if (readyForDM > 0) lines.push(`  Send DMs to ${readyForDM} accepted connections`);
        if (pendingInvites === 0 && awaitingAccept === 0 && readyForDM === 0) {
          lines.push("  Find new leads (run find_leads)");
        }
    
        return { content: [{ type: "text" as const, text: lines.join("\n") }] };
      },
    );
Behavior4/5

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

Annotations already establish read-only safety (readOnlyHint=true). Description adds valuable behavioral context by detailing exactly what data fields are returned (invitations sent, DMs sent, acceptance rates, next actions), helping the agent understand the return structure despite no output schema being present.

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?

Single sentence with efficient colon-delimited structure. Front-loaded with action verb, zero filler words. Every element (pipeline scope, specific metrics) earns its place.

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?

For a zero-parameter status tool without output schema, the description adequately compensates by listing specific return fields (lead counts, rates, actions). Would be perfect if it indicated whether data is real-time or cached, but sufficient for agent selection.

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?

Input schema has zero parameters. Per calibration rules, 0 params warrants baseline score of 4. Description appropriately doesn't attempt to invent parameter semantics where none exist.

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?

Description uses specific verb 'Get' with clear resource 'LinkedIn prospection pipeline' and distinguishes from action-oriented siblings (run_pipeline, manage_lead) by emphasizing it retrieves 'current state' and metrics rather than performing actions.

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?

Provides clear context by specifying it returns metrics (lead counts, acceptance rates, next actions), implicitly positioning it as the monitoring tool to use before/after running pipeline actions, though it doesn't explicitly name alternatives or when-not-to-use conditions.

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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