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gomessoaresemmanuel-cpu

linkedin-prospection-mcp

Run Prospection Pipeline

run_pipeline

Automate LinkedIn prospection by finding leads, sending invitations, checking acceptances, and sending direct messages. Skip lead search or simulate actions as needed.

Instructions

Trigger the full daily prospection pipeline: find leads → send invitations → check acceptances → send DMs. Uses the daily-orchestrator.js script. Can skip lead search with skip_leads=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skip_leadsNoSkip lead search (invitations + DMs only)
dry_runNoSimulate without sending

Implementation Reference

  • The implementation of the run_pipeline tool handler which executes the daily-orchestrator.js script.
    server.registerTool(
      "run_pipeline",
      {
        title: "Run Prospection Pipeline",
        description:
          "Trigger the full daily prospection pipeline: find leads → send invitations → check acceptances → send DMs. " +
          "Uses the daily-orchestrator.js script. Can skip lead search with skip_leads=true.",
        inputSchema: {
          skip_leads: z.boolean().default(true).optional().describe("Skip lead search (invitations + DMs only)"),
          dry_run: z.boolean().default(false).optional().describe("Simulate without sending"),
        },
        annotations: { readOnlyHint: false, openWorldHint: true, destructiveHint: false },
      },
      async ({ skip_leads, dry_run }) => {
        if (!fs.existsSync(SESSION_DIR)) {
          return {
            isError: true,
            content: [{ type: "text" as const, text: "LinkedIn session expired. Run setup-session.js to re-login." }],
          };
        }
    
        const args: string[] = [];
        if (skip_leads) args.push("--skip-leads");
        if (dry_run) args.push("--dry-run");
    
        const result = await runScript("daily-orchestrator.js", args, 600_000);
    
        return {
          content: [
            {
              type: "text" as const,
              text: [
                result.code === 0 ? "Pipeline completed successfully!" : `Pipeline finished with code ${result.code}`,
                "",
                result.stdout.slice(-3000),
                result.stderr ? `\nErrors:\n${result.stderr.slice(-500)}` : "",
              ].join("\n"),
            },
          ],
        };
      },
    );
Behavior4/5

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

Annotations indicate readOnly=false and destructive=false; the description adds valuable behavioral context beyond these binaries by disclosing the exact sequence of side effects (sending invitations and DMs) and noting the implementation script (daily-orchestrator.js), which helps the agent understand the operational scope.

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?

Three sentences efficiently structured: the first front-loads the purpose with clear flow notation, the second provides implementation context (script name), and the third notes the skip parameter. The script reference could be considered extraneous for an AI agent but does not significantly detract from clarity.

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 100% schema coverage and presence of annotations, the description adequately covers the pipeline's behavioral complexity by detailing the 4-stage process. While it could benefit from mentioning idempotency or error handling behavior for a pipeline tool, it sufficiently covers the core operation without needing to describe return values (no output schema).

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?

With 100% schema description coverage, the schema fully documents both parameters (skip_leads and dry_run). The description mentions skip_leads explicitly but adds no semantic information beyond what the schema already provides, meeting the baseline for high-coverage schemas.

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 uses the specific verb 'Trigger' with the resource 'full daily prospection pipeline' and distinguishes from siblings by enumerating the complete 4-step workflow (find leads → send invitations → check acceptances → send DMs), clearly positioning it as the orchestration tool versus single-operation siblings like find_leads.

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 by emphasizing 'full' pipeline and listing the complete sequence, suggesting when to use this versus partial operations. However, it lacks explicit guidance stating when to choose this over individual sibling tools (e.g., 'Use this instead of find_leads when you need the complete workflow').

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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