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 x15907982411/exa-mcp-server-for-operit --skill agentgit clone --depth 1 https://github.com/x15907982411/exa-mcp-server-for-operitWrote 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/x15907982411/exa-mcp-server-for-operit/agent)<a href="https://agentmods.dev/skills/x15907982411/exa-mcp-server-for-operit/agent"><img src="https://agentmods.dev/badge/skills/x15907982411/exa-mcp-server-for-operit/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/x15907982411/exa-mcp-server-for-operit/agent"><img src="https://agentmods.dev/badge/skills/x15907982411/exa-mcp-server-for-operit/agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00058 | $0.02136 |
| Opus 5 | $0.00029 | $0.01068 |
| Sonnet 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 9d 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.
This is a copy
100% identical to exa-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 9d ago First seen · 261 lines · 58 tokens per session scan A 7bbff323b62d
exa-agent is a skill published in the GitHub repository x15907982411/exa-mcp-server-for-operit (0 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 2,136 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to exa-agent, differing in 0 lines, and is treated as a copy.
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