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exa-search

Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

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Prix non confirmé★ 89 Stars GitHubRegistre mis à jour · 7 sept. 2026agent-skill

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Exa Search

Neural search for web content, code, companies, and people via the Exa MCP server.

When to Activate

User needs current web information or news
Searching for code examples, API docs, or technical references
Researching companies, competitors, or market players
Finding professional profiles or people in a domain
Running background research for any development task
User says "search for", "look up", "find", or "what's the latest on"

MCP Requirement

Exa MCP server must be configured. Add to ~/.codex.json:

"exa-web-search": {
  "command": "npx",
  "args": ["-y", "exa-mcp-server"],
  "env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}

Get an API key at exa.ai.

Core Tools

web_search_exa

General web search for current information, news, or facts.

web_search_exa(query: "latest AI developments 2026", numResults: 5)

Parameters:

Param	Type	Default	Notes
`query`	string	required	Search query
`numResults`	number	8	Number of results

web_search_advanced_exa

Filtered search with domain and date constraints.

web_search_advanced_exa(
  query: "React Server Components best practices",
  numResults: 5,
  includeDomains: ["github.com", "react.dev"],
  startPublishedDate: "2025-01-01"
)

Parameters:

Param	Type	Default	Notes
`query`	string	required	Search query
`numResults`	number	8	Number of results
`includeDomains`	string[]	none	Limit to specific domains
`excludeDomains`	string[]	none	Exclude specific domains
`startPublishedDate`	string	none	ISO date filter (start)
`endPublishedDate`	string	none	ISO date filter (end)

get_code_context_exa

Find code examples and documentation from GitHub, Stack Overflow, and docs sites.

get_code_context_exa(query: "Python asyncio patterns", tokensNum: 3000)

Parameters:

Param	Type	Default	Notes
`query`	string	required	Code or API search query
`tokensNum`	number	5000	Content tokens (1000-50000)

company_research_exa

Research companies for business intelligence and news.

company_research_exa(companyName: "OpenAI", numResults: 5)

Parameters:

Param	Type	Default	Notes
`companyName`	string	required	Company name
`numResults`	number	5	Number of results

people_search_exa

Find professional profiles and bios.

people_search_exa(query: "AI safety researchers at OpenAI", numResults: 5)

crawling_exa

Extract full page content from a URL.

crawling_exa(url: "https://example.com/article", tokensNum: 5000)

Parameters:

Param	Type	Default	Notes
`url`	string	required	URL to extract
`tokensNum`	number	5000	Content tokens

deep_researcher_start / deep_researcher_check

Start an AI research agent that runs asynchronously.

# Start research
deep_researcher_start(query: "comprehensive analysis of AI code editors in 2026")

# Check status (returns results when complete)
deep_researcher_check(researchId: "<id from start>")

Usage Patterns

Quick Lookup

web_search_exa(query: "Node.js 22 new features", numResults: 3)

Code Research

get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)

Company Due Diligence

company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)

Technical Deep Dive

# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")

Tips

Use web_search_exa for broad queries, web_search_advanced_exa for filtered results
Lower tokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive context
Combine company_research_exa with web_search_advanced_exa for thorough company analysis
Use crawling_exa to get full content from specific URLs found in search results
deep_researcher_start is best for comprehensive topics that benefit from AI synthesis

deep-research — Full research workflow using firecrawl + exa together
market-research — Business-oriented research with decision frameworks

Voir la source ↗

Métadonnées du fichier

name: exa-search
description: Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

Voir le texte original

---
name: exa-search
description: Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
---

# Exa Search

Neural search for web content, code, companies, and people via the Exa MCP server.

## When to Activate

- User needs current web information or news
- Searching for code examples, API docs, or technical references
- Researching companies, competitors, or market players
- Finding professional profiles or people in a domain
- Running background research for any development task
- User says "search for", "look up", "find", or "what's the latest on"

## MCP Requirement

Exa MCP server must be configured. Add to `~/.codex.json`:

```json
"exa-web-search": {
  "command": "npx",
  "args": ["-y", "exa-mcp-server"],
  "env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}
```

Get an API key at [exa.ai](https://exa.ai).

## Core Tools

### web_search_exa
General web search for current information, news, or facts.

```
web_search_exa(query: "latest AI developments 2026", numResults: 5)
```

**Parameters:**

| Param | Type | Default | Notes |
|-------|------|---------|-------|
| `query` | string | required | Search query |
| `numResults` | number | 8 | Number of results |

### web_search_advanced_exa
Filtered search with domain and date constraints.

```
web_search_advanced_exa(
  query: "React Server Components best practices",
  numResults: 5,
  includeDomains: ["github.com", "react.dev"],
  startPublishedDate: "2025-01-01"
)
```

**Parameters:**

| Param | Type | Default | Notes |
|-------|------|---------|-------|
| `query` | string | required | Search query |
| `numResults` | number | 8 | Number of results |
| `includeDomains` | string[] | none | Limit to specific domains |
| `excludeDomains` | string[] | none | Exclude specific domains |
| `startPublishedDate` | string | none | ISO date filter (start) |
| `endPublishedDate` | string | none | ISO date filter (end) |

### get_code_context_exa
Find code examples and documentation from GitHub, Stack Overflow, and docs sites.

```
get_code_context_exa(query: "Python asyncio patterns", tokensNum: 3000)
```

**Parameters:**

| Param | Type | Default | Notes |
|-------|------|---------|-------|
| `query` | string | required | Code or API search query |
| `tokensNum` | number | 5000 | Content tokens (1000-50000) |

### company_research_exa
Research companies for business intelligence and news.

```
company_research_exa(companyName: "OpenAI", numResults: 5)
```

**Parameters:**

| Param | Type | Default | Notes |
|-------|------|---------|-------|
| `companyName` | string | required | Company name |
| `numResults` | number | 5 | Number of results |

### people_search_exa
Find professional profiles and bios.

```
people_search_exa(query: "AI safety researchers at OpenAI", numResults: 5)
```

### crawling_exa
Extract full page content from a URL.

```
crawling_exa(url: "https://example.com/article", tokensNum: 5000)
```

**Parameters:**

| Param | Type | Default | Notes |
|-------|------|---------|-------|
| `url` | string | required | URL to extract |
| `tokensNum` | number | 5000 | Content tokens |

### deep_researcher_start / deep_researcher_check
Start an AI research agent that runs asynchronously.

```
# Start research
deep_researcher_start(query: "comprehensive analysis of AI code editors in 2026")

# Check status (returns results when complete)
deep_researcher_check(researchId: "<id from start>")
```

## Usage Patterns

### Quick Lookup
```
web_search_exa(query: "Node.js 22 new features", numResults: 3)
```

### Code Research
```
get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)
```

### Company Due Diligence
```
company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)
```

### Technical Deep Dive
```
# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")
```

## Tips

- Use `web_search_exa` for broad queries, `web_search_advanced_exa` for filtered results
- Lower `tokensNum` (1000-2000) for focused code snippets, higher (5000+) for comprehensive context
- Combine `company_research_exa` with `web_search_advanced_exa` for thorough company analysis
- Use `crawling_exa` to get full content from specific URLs found in search results
- `deep_researcher_start` is best for comprehensive topics that benefit from AI synthesis

## Related Skills

- `deep-research` — Full research workflow using firecrawl + exa together
- `market-research` — Business-oriented research with decision frameworks

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
No explicit limitations or safety considerations are documented (e.g., API rate limits, cost implications, or potential for biased results).
The skill assumes the Exa MCP server is already configured; no troubleshooting or fallback guidance is provided if the server is unavailable.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 89 GitHub stars
Stars/forks activity: 89 stars, 2 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: mturac/everything-openai-codex
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 24 août 2026
Registre mis à jour: 7 sept. 2026
Chemin des instructions: .agents/skills/exa-search/SKILL.md @ b5057da5f42e

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

63/100

Prometteur

Confiance

57/100

Do not auto-install

Audit

72/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
No explicit limitations or safety considerations are documented (e.g., API rate limits, cost implications, or potential for biased results).
The skill assumes the Exa MCP server is already configured; no troubleshooting or fallback guidance is provided if the server is unavailable.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 89 GitHub stars
Stars/forks activity: 89 stars, 2 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

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Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use exa-search in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 72/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "mturac-exa-search (exa-search)",
      "install_command": "npx skills add mturac/everything-openai-codex --skill exa-search",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "mturac-exa-search",
      "task": "Use exa-search in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/mturac-exa-search",
    "api": "https://www.openagentskill.com/api/agent/skills/mturac-exa-search",
    "audit": "https://www.openagentskill.com/skills/mturac-exa-search/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=mturac-exa-search&task=Use%20exa-search%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20exa-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20exa-search%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/mturac-exa-search/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/mturac-exa-search"
  }
}

Pour le créateur

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: mturac
Source: mturac/everything-openai-codex
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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Vue d’ensemble

Exa Search

When to Activate

MCP Requirement

Core Tools

web_search_exa

web_search_advanced_exa

get_code_context_exa

company_research_exa

people_search_exa

crawling_exa

deep_researcher_start / deep_researcher_check

Usage Patterns

Quick Lookup

Code Research

Company Due Diligence

Technical Deep Dive

Tips

Related Skills

Examiner la source

Prix et coûts d’utilisation

Commencer par une petite tâche

Source et conseils d’utilisation

Accès agent

Skills associés

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