/exa-agent

name: Exa Agent
description: "Use Exa Agent for multi-step web research, list-building, enrichment, structured output, run continuation, and coverage validation. Exa Agent can access additional data providers: fiber, financial_datasets, similarweb, baselayer, affiliate, particle, and jinko."

Exa Agent Research

You are operating Exa Agent through MCP. Exa Agent is a tool that allows you to run multi-step web research, list-building, enrichment, structured output, run continuation, and coverage validation.

Required tools

• `agent_run`

Exa Connect providers

When a run needs premium partner data alongside Exa web search, pass `dataSources` to `agent_run`.

Use only the currently usable self-serve providers:

• `fiber`: B2B company, people, jobs, and contact enrichment
• `financial_datasets`: ticker-based news for US public companies
• `similarweb`: website traffic estimates, rankings, and competitor discovery
• `baselayer`: US business verification, officers, registrations, and KYB
• `affiliate`: product catalog search, pricing, brands, and merchant links
• `particle`: podcast transcript search with speaker attribution and timestamps
• `jinko`: travel destination discovery ranked by fare

Do not suggest request-only providers unless the user explicitly says their Exa account already has them enabled.

Decision tree

Choose the work surface before acting:

1. Known input rows plus repeated same-shape enrichment at scale

• Write a deterministic script using Exa APIs directly.
• Use bounded concurrency, exponential backoff, checkpoints, and a stable output file.
• Read the output file and synthesize from it.
• Do not burn context manually looping over hundreds of identical tool calls.

2. Open-ended universe definition, list-building, people/company discovery, multi-hop research, structured research, or follow-up over previous work

• Use Exa Agent.
• Define the objective and `outputSchema` before creating the run.

Before creating a run

Always write down:

• Objective: what the run is meant to answer.
• Universe: what entities qualify.
• Segments: geographies, industries, personas, dates, asset classes, or other partitions.
• Coverage target: desired count, maximum count, and what "good enough" means.
• Output fields: columns needed in the final answer.
• Evidence requirements: URLs, source titles, dates, and confidence.
• Exclusions: prior results or disallowed entities.

If the user uses relative time like "recent", "last 6 months", or "post-IPO", calculate exact dates from today's date first.

Schema rules

Use `outputSchema` for list-building, enrichment, finance/company research, and repeatable workflows.

Rules:

• Use a top-level object.
• Put list rows in a named array field.
• Add `maxItems` to arrays when possible.
• Include source/evidence fields, not just conclusions.
• Include stable identifiers: company name, website/domain, person LinkedIn URL, ticker, CIK, etc.
• Include confidence or rationale fields for fuzzy judgments.
• Keep required fields limited to what must exist.
• Use `format: "uri"`, `format: "email"`, or `format: "phone"` when needed.

Example company-list schema:

{
  "type": "object",
  "properties": {
    "companies": {
      "type": "array",
      "maxItems": 50,
      "items": {
        "type": "object",
        "properties": {
          "company_name": { "type": "string" },
          "website": { "type": "string", "format": "uri" },
          "segment": { "type": "string" },
          "why_it_qualifies": { "type": "string" },
          "evidence_url": { "type": "string", "format": "uri" },
          "confidence": { "type": "string", "enum": ["low", "medium", "high"] }
        },
        "required": ["company_name", "website", "why_it_qualifies", "evidence_url"]
      }
    },
    "coverage_notes": { "type": "string" },
    "known_gaps": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["companies", "coverage_notes"]
}

Example with Exa Connect:

{
  "tool": "agent_run",
  "arguments": {
    "query": "Find 10 fast-growing B2B SaaS companies and return estimated monthly website visits from Similarweb.",
    "dataSources": [
      { "provider": "similarweb" }
    ],
    "outputSchema": {
      "type": "object",
      "properties": {
        "companies": {
          "type": "array",
          "maxItems": 10,
          "items": {
            "type": "object",
            "properties": {
              "name": { "type": "string" },
              "domain": { "type": "string" },
              "monthlyVisits": {
                "type": "number",
                "description": "Estimated monthly visits from Similarweb"
              }
            },
            "required": ["name", "domain", "monthlyVisits"]
          }
        }
      },
      "required": ["companies"]
    }
  }
}

Exa Agent workflow

1. Run the agent

• Call `agent_run`.
• Omit `effort` to use the tool's `low` default. Choose `auto` or a higher effort only when the user asks for more depth or the task clearly requires it.
• Include `outputSchema` for structured work.
• Use `input.data` for known rows.
• Use `input.exclusion` for entities already returned or disallowed.
• Add `dataSources` only when one of the self-serve Exa Connect providers is clearly useful.
• Name the provider-specific data you want in both the query and the schema so Agent uses the provider instead of falling back to web search.
• Save the returned `id` when a later continuation may use `previousRunId`.
• If the response has `status: "running"`, call `agent_run` again with that `runId` until `outputReady` is true. This continuation is available for retained runs that outlive one MCP call.
• Zero Data Retention (ZDR) teams: new runs always stream, and output is only available on that live stream (not via `runId` resumption). The MCP call window is ~750 seconds; if a ZDR run cannot finish in on