Generate enriched lead lists using Exa Agent. Finds companies matching an ICP, enriches with signals/news/scores, and outputs CSV. Use when generating leads, building prospect lists, finding companies to sell to, doing outbound research, or ICP-based company discovery. Triggers on "leads", "lead gen

Lead Generation with Exa Agent Generate enriched lead lists using the Exa Agent API. An Agent run is an asynchronous, multi step web research task: you describe the list you want plus an output schema, and Exa handles query decomposition, searching, verification, enrichment, and structured output internally. You do NOT need to orchestrate parallel searches, subagents, or manual deduplication. For very large or continuously maintained lead lists with per item verification, consider Exa Websets instead: https://docs.exa.ai/websets/api/overview Prerequisites This skill requires the Exa MCP server with the Agent tool enabled. Use the agent tools URL selection alias to enable agent run . If the Agent tools are not available, tell the user: You need the Exa MCP server installed with the Agent tools and your API key. Instructions: https://docs.exa.ai/reference/exa mcp Then stop. Tool Restriction Use agent run , plus Write and Bash (for CSV output). Do NOT use generic web search for the lead list itself. Workflow Step 1: Understand the ICP When the user says something like "Make a list of 200 leads for [company]", first establish the Ideal Customer Profile. If the user already described the ICP, confirm it. If not, run one small Agent run to research it: Present the ICP to the user and confirm: Is the ICP description accurate? Any companies to exclude (competitors, existing customers)? How many leads do they want? (default 200) Any specific enrichment columns they care about? Step 2: Create the Lead Gen Run Design an outputSchema with a bounded companies array. Keep schemas small, flat, and explicit; always bound arrays with maxItems . Core fields to always include: company name (string) website (string) product description (string, "in 12 words or less") icp fit score (integer, 1 10) icp fit reasoning (string, "compelling one liner in 20 words or less") Add enrichment fields tailored to the campaign (funding stage, headcount range, headquarters, hiring signals, etc.). Give string fields a length hint in their description to keep CSV output clean. Use the run inputs for the pieces the old manual pipeline handled by hand: query — describe the list: the ICP, geography, stage, and how many companies you want outputSchema — the exact structure back, with maxItems bounding the companies array systemPrompt — scoring rules, source preferences, dedup/exclusion emphasis input.exclusion — companies to avoid (competitors, existing customers, results from earlier runs) effort — "low" by default; "auto" , "high" , or "xhigh" for large or hard lists Example: agent run returns the completed result when possible. If it returns status: "running" with an agent run ... ID, save the ID and continue with agent run using only runId . Step 3: Wait and Read Output 1. If the run is still running, call agent run with its runId until outputReady is true or the run reaches a terminal status ( failed or cancelled ). 2. Read the companies from output.structured , citations from output.grounding , and the run cost from costDollars in the agent run result. Do not paste the full raw output into the conversation — go straight to CSV. Step 4: Write the CSV Write output.structured.companies to {target company} leads {YYYY MM DD}.csv , sorted by icp fit score descending. Join any array fields with " ". Use Python's csv.writer (handles quoting/escaping) via Bash, or Write directly for small lists. Print a summary: Step 5: Expanding the List If the user wants more leads than one run returned: Create a new follow up run with previousRunId set to the completed run's ID, asking for additional companies Put the company records already collected into input.exclusion so the new run avoids them Append the new results to the CSV and re deduplicate by normalized company name (strip "Inc"/"Ltd"/etc., case insensitive) For lists in the many hundreds, run a few runs sequentially this way rather than one giant run, and confirm scope with the user first: "This will require ~{N} Agent runs. Proceed?" Handling Failures If a run ends failed , read the error from the agent run result, adjust the query or schema, and retry once with different wording If a client cancellation is needed, abort the in progress agent run call If results are consistently below the requested count, narrow the ICP into 2 3 sub vertical runs instead of one broad run MCP Configuration Requires an Exa API key. Get yours at https://dashboard.exa.ai/api keys References Exa Agent guide: https://docs.exa.ai/reference/agent api guide Exa MCP setup: https://docs.exa.ai/reference/exa mcp Websets (verified list building at scale): https://docs.exa.ai/websets/api/overview Full docs for LLMs: https://docs.exa.ai/llms.txt

lead-generation