Exa MCP Server connects AI agents to Exa, a service for searching the web, fetching webpage content, and carrying out multi-step research. MCP, or Model Context Protocol, lets compatible AI clients call these capabilities as tools. The catalogue skills, MCP server, and plugin provide ways for coding agents to use Exa for web research.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add exa-labs/exa-mcp-server --skill exa-agentgit clone --depth 1 https://github.com/exa-labs/exa-mcp-serverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/exa-labs/exa-mcp-server/exa-agent)<a href="https://agentmods.dev/skills/exa-labs/exa-mcp-server/exa-agent"><img src="https://agentmods.dev/badge/skills/exa-labs/exa-mcp-server/exa-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/exa-labs/exa-mcp-server/exa-agent"><img src="https://agentmods.dev/badge/skills/exa-labs/exa-mcp-server/exa-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.02136 |
| Opus 5.5 | $0.00024 | $0.00854 |
| Sonnet 5.5 | $0.00012 | $0.00427 |
| Haiku 4.5 | $0.00006 | $0.00214 |
Grade A, and why
Exa Agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- exa-agent — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 enrichmentfinancial_datasets: ticker-based news for US public companiessimilarweb: website traffic estimates, rankings, and competitor discoverybaselayer: US business verification, officers, registrations, and KYBaffiliate: product catalog search, pricing, brands, and merchant linksparticle: podcast transcript search with speaker attribution and timestampsjinko: 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:
-
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.
-
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
outputSchemabefore 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +1 tokens per session 313819bd8113
- 1mo ago First seen · 261 lines · 58 tokens per session scan A 7bbff323b62d
Exa Agent is a skill published in the GitHub repository exa-labs/exa-mcp-server (5,168 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 2,136 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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