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Tavily Crawl Beta

GitHub Repo-SternenpmSchmiedeabzeichen

🎉 Einführung von tavily-crawl + tavily-map in v0.2.1! 🎉

MCP-Demo

Das Model Context Protocol (MCP) ist ein offener Standard, der KI-Systemen die nahtlose Interaktion mit verschiedenen Datenquellen und Tools ermöglicht und so sichere, bidirektionale Verbindungen ermöglicht.

Das von Anthropic entwickelte Model Context Protocol (MCP) ermöglicht KI-Assistenten wie Claude die nahtlose Integration mit den erweiterten Such- und Datenextraktionsfunktionen von Tavily. Diese Integration bietet KI-Modellen Echtzeitzugriff auf Webinformationen, einschließlich ausgefeilter Filteroptionen und domänenspezifischer Suchfunktionen.

Der Tavily MCP-Server bietet:

  • Such-, Extraktions-, Map- und Crawling-Tools

  • Echtzeit-Websuchfunktionen durch das Tavily-Suchtool

  • Intelligente Datenextraktion aus Webseiten über das Tool tavily-extract

  • Leistungsstarkes Web-Mapping-Tool, das eine strukturierte Karte der Website erstellt

  • Webcrawler, der Webseiten systematisch durchsucht

📚 Hilfreiche Ressourcen

  • Tutorial zum Kombinieren von Tavily MCP mit Neo4j MCP-Server

  • Tutorial zur Integration von Tavily MCP mit Cline in VS Code

Related MCP server: Tavily MCP Server

Voraussetzungen 🔧

Bevor Sie beginnen, stellen Sie sicher, dass Sie über Folgendes verfügen:

  • Tavily API-Schlüssel

    • Wenn Sie keinen Tavily-API-Schlüssel haben, können Sie sich hier für ein kostenloses Konto anmelden

  • Claude Desktop oder Cursor

  • Node.js (v20 oder höher)

    • Sie können Ihre Node.js-Installation überprüfen, indem Sie Folgendes ausführen:

      • node --version

  • Git installiert (nur erforderlich, wenn die Git-Installationsmethode verwendet wird)

    • Unter macOS: brew install git

    • Unter Linux:

      • Debian/Ubuntu: sudo apt install git

      • RedHat/CentOS: sudo yum install git

    • Unter Windows: Laden Sie Git für Windows herunter

Tavily MCP-Serverinstallation ⚡

Ausführen mit NPX

npx -y tavily-mcp@0.2.1

Installation über Smithery

So installieren Sie den Tavily MCP-Server für Claude Desktop automatisch über Smithery :

npx -y @smithery/cli install @tavily-ai/tavily-mcp --client claude

Obwohl Sie einen Server eigenständig starten können, ist er isoliert betrachtet nicht besonders hilfreich. Integrieren Sie ihn stattdessen in einen MCP-Client. Nachfolgend finden Sie ein Beispiel für die Konfiguration der Claude Desktop-App für den tavily-mcp-Server.

MCP-Clients konfigurieren ⚙️

In diesem Repository wird erklärt, wie VS Code , Cursor und Claude Desktop für die Arbeit mit dem tavily-mcp-Server konfiguriert werden.

VS Code konfigurieren 💻

Klicken Sie für die Ein-Klick-Installation auf eine der folgenden Installationsschaltflächen:

Mit NPX in VS Code installieren Installation mit NPX in VS Code Insiders

Manuelle Installation

Prüfen Sie zunächst, ob oben in diesem Abschnitt die entsprechenden Installationsschaltflächen vorhanden sind. Wenn Sie die manuelle Installation bevorzugen, gehen Sie folgendermaßen vor:

Fügen Sie den folgenden JSON-Block zu Ihrer Benutzereinstellungsdatei (JSON) in VS Code hinzu. Drücken Sie dazu Ctrl + Shift + P (oder Cmd + Shift + P unter macOS) und geben Sie Preferences: Open User Settings (JSON) ein.

{
  "mcp": {
    "inputs": [
      {
        "type": "promptString",
        "id": "tavily_api_key",
        "description": "Tavily API Key",
        "password": true
      }
    ],
    "servers": {
      "tavily": {
        "command": "npx",
        "args": ["-y", "tavily-mcp@0.2.1"],
        "env": {
          "TAVILY_API_KEY": "${input:tavily_api_key}"
        }
      }
    }
  }
}

Optional können Sie es zu einer Datei namens .vscode/mcp.json in Ihrem Arbeitsbereich hinzufügen:

{
  "inputs": [
    {
      "type": "promptString",
      "id": "tavily_api_key",
      "description": "Tavily API Key",
      "password": true
    }
  ],
  "servers": {
    "tavily": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "${input:tavily_api_key}"
      }
    }
  }
}

Cline konfigurieren 🤖

Die einfachste Möglichkeit, den Tavily MCP-Server in Cline einzurichten, ist über den Marktplatz mit nur einem Klick:

  1. Öffnen Sie Cline in VS Code

  2. Klicken Sie in der Seitenleiste auf das Cline-Symbol

  3. Navigieren Sie zur Registerkarte „MCP-Server“ (4 Quadrate).

  4. Suchen Sie nach „Tavily“ und klicken Sie auf „Installieren“.

  5. Geben Sie bei der entsprechenden Aufforderung Ihren Tavily API-Schlüssel ein

Alternativ können Sie den Tavily MCP-Server manuell in Cline einrichten:

  1. Öffnen Sie die Cline MCP-Einstellungsdatei:

Für macOS:

# Using Visual Studio Code
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

# Or using TextEdit
open -e ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

Für Windows:

code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

  1. Fügen Sie der Datei die Tavily-Serverkonfiguration hinzu:

    Ersetzen Sie your-api-key-here durch Ihren tatsächlichen Tavily-API-Schlüssel .

    {
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

  2. Speichern Sie die Datei und starten Sie Cline neu, falls es bereits ausgeführt wird.

  3. Wenn Sie Cline verwenden, haben Sie jetzt Zugriff auf die Tavily MCP-Tools. Sie können Cline bitten, die Tools „Tavily-Suche“ und „Tavily-Extrahieren“ direkt in Ihren Gesprächen zu verwenden.

Cursor konfigurieren 🖥️

Hinweis : Erfordert Cursor-Version 0.45.6 oder höher

So richten Sie den Tavily MCP-Server in Cursor ein:

  1. Cursoreinstellungen öffnen

  2. Navigieren Sie zu Funktionen > MCP-Server

  3. Klicken Sie auf die Schaltfläche „+ Neuen MCP-Server hinzufügen“

  4. Füllen Sie die folgenden Informationen aus:

    • Name : Geben Sie einen Spitznamen für den Server ein (z. B. „tavily-mcp“)

    • Typ : Wählen Sie als Typ „Befehl“ aus

    • Befehl : Geben Sie den Befehl zum Ausführen des Servers ein:

      env TAVILY_API_KEY=your-api-key npx -y tavily-mcp@0.2.1

      Wichtig : Ersetzen Sie your-api-key durch Ihren Tavily API-Schlüssel. Sie erhalten einen unter app.tavily.com/home

Nach dem Hinzufügen sollte der Server in der Liste der MCP-Server erscheinen. Möglicherweise müssen Sie die Schaltfläche „Aktualisieren“ oben rechts auf dem MCP-Server manuell drücken, um die Toolliste zu füllen.

Der Composer-Agent verwendet automatisch die Tavily MCP-Tools, wenn diese für Ihre Abfragen relevant sind. Es empfiehlt sich, die Verwendung der Tools explizit anzufordern, indem Sie beschreiben, was Sie tun möchten (z. B. „Verwende tavily-search, um im Internet nach den neuesten KI-Nachrichten zu suchen“). Auf dem Mac drücken Sie Befehl + L, um den Chat zu öffnen, wählen Sie die Composer-Option oben auf dem Bildschirm, wählen Sie neben der Schaltfläche „Senden“ den Agenten aus und senden Sie die Abfrage, wenn Sie bereit sind.

Beispiel für eine Cursorschnittstelle

Konfigurieren der Claude Desktop-App 🖥️

Für macOS:

# Create the config file if it doesn't exist
touch "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Opens the config file in TextEdit 
open -e "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# Alternative method using Visual Studio Code (requires VS Code to be installed)
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

Für Windows:

code %APPDATA%\Claude\claude_desktop_config.json

Fügen Sie die Tavily-Serverkonfiguration hinzu:

Ersetzen Sie your-api-key-here durch Ihren tatsächlichen Tavily-API-Schlüssel .

{
  "mcpServers": {
    "tavily-mcp": {
      "command": "npx",
      "args": ["-y", "tavily-mcp@0.2.1"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

2. Git-Installation

  1. Klonen Sie das Repository:

git clone https://github.com/tavily-ai/tavily-mcp.git
cd tavily-mcp

  1. Installieren Sie Abhängigkeiten:

npm install

  1. Erstellen Sie das Projekt:

npm run build

Konfigurieren der Claude Desktop-App ⚙️

Befolgen Sie die im Abschnitt „Konfigurieren der Claude Desktop-App“ oben beschriebenen Konfigurationsschritte und verwenden Sie dabei die folgende JSON-Konfiguration.

Ersetzen Sie your-api-key-here durch Ihren tatsächlichen Tavily-API-Schlüssel und /path/to/tavily-mcp durch den tatsächlichen Pfad, in den Sie das Repository auf Ihrem System geklont haben.

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": ["/path/to/tavily-mcp/build/index.js"],
      "env": {
        "TAVILY_API_KEY": "your-api-key-here"
      }
    }
  }
}

Verwendung in der Claude Desktop App 🎯

Sobald die Installation abgeschlossen und die Claude-Desktop-App konfiguriert ist, müssen Sie die Claude-Desktop-App vollständig schließen und erneut öffnen, um den Tavily-MCP-Server anzuzeigen. Unten links in der App sollte ein Hammersymbol angezeigt werden, das die verfügbaren MCP-Tools anzeigt. Klicken Sie auf das Hammersymbol, um weitere Details zu den Tools Tavily-Search und Tavily-Extract anzuzeigen.

Alternativtext

Claude hat nun vollständigen Zugriff auf den Tavily-MCP-Server, einschließlich der Tools Tavily-Search und Tavily-Extract. Wenn Sie die folgenden Beispiele in die Claude-Desktop-App einfügen, sollten Sie die Tavily-MCP-Server-Tools in Aktion sehen.

Tavily-Suchbeispiele

  1. Allgemeine Websuche :

Can you search for recent developments in quantum computing?

  1. Nachrichtensuche :

Search for news articles about AI startups from the last 7 days.

  1. Domänenspezifische Suche :

Search for climate change research on nature.com and sciencedirect.com

Beispiele für Tavily-Extrakte

  1. Artikelinhalt extrahieren :

Extract the main content from this article: https://example.com/article

✨ Suche und Extrahierung kombinieren ✨

Sie können die Tools „tavily-search“ und „tavily-extract“ auch kombinieren, um komplexere Aufgaben auszuführen.

Search for news articles about AI startups from the last 7 days and extract the main content from each article to generate a detailed report.

Fehlerbehebung 🛠️

Häufige Probleme

  1. Server nicht gefunden

    • Überprüfen Sie die npm-Installation, indem Sie npm --verison ausführen.

    • Überprüfen Sie die Claude Desktop-Konfigurationssyntax, indem Sie code ~/Library/Application\ Support/Claude/claude_desktop_config.json ausführen

    • Stellen Sie sicher, dass Node.js ordnungsgemäß installiert ist, indem Sie node --version ausführen.

  2. NPX-bezogene Probleme

  • Wenn Fehler im Zusammenhang mit npx auftreten, müssen Sie möglicherweise stattdessen den vollständigen Pfad zur ausführbaren npx-Datei verwenden.

  • Sie können diesen Pfad finden, indem Sie which npx in Ihrem Terminal ausführen und dann in Ihrer Konfiguration die Zeile "command": "npx" durch "command": "/full/path/to/npx" ersetzen.

  1. Probleme mit API-Schlüsseln

    • Bestätigen Sie, dass Ihr Tavily-API-Schlüssel gültig ist

    • Überprüfen Sie, ob der API-Schlüssel in der Konfiguration korrekt eingestellt ist

    • Stellen Sie sicher, dass der API-Schlüssel keine Leerzeichen oder Anführungszeichen enthält.

Danksagungen ✨

Available Tools

4 tools
tavily-crawlA

A powerful web crawler that initiates a structured web crawl starting from a specified base URL. The crawler expands from that point like a graph, following internal links across pages. You can control how deep and wide it goes, and guide it to focus on specific sections of the site.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe root URL to begin the crawl
max_depthNoMax depth of the crawl. Defines how far from the base URL the crawler can explore.
max_breadthNoMax number of links to follow per level of the tree (i.e., per page)
limitNoTotal number of links the crawler will process before stopping
instructionsNoNatural language instructions for the crawler. Instructions specify which types of pages the crawler should return.
select_pathsNoRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)
select_domainsNoRegex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)
allow_externalNoWhether to return external links in the final response
extract_depthNoAdvanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latencybasic
formatNoThe format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.markdown
include_faviconNoWhether to include the favicon URL for each result

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It explains the crawler's graph-like expansion and control over depth/breadth, but omits behavioral details such as asynchronicity, rate limits, or side effects. It provides adequate but not comprehensive transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loads the core purpose, and contains no redundant information. Every sentence contributes meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 100% schema coverage and no output schema, the description is somewhat light for a complex 11-parameter tool. It does not mention the output format or any operational constraints (e.g., timeouts, error handling), leaving some gaps in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by describing the crawler's graph expansion and ability to focus on sections, which enhances understanding of how parameters like max_depth and max_breadth work together.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a web crawler that starts from a base URL and expands like a graph, distinguishing it from sibling tools like extract, map, and search. It specifies the core action (initiates a structured crawl) and the resource (URL).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for structured web crawling but does not explicitly state when to use it versus alternatives (e.g., tavily-search). It lacks explicit when-not or alternative suggestions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

tavily-extractC

A powerful web content extraction tool that retrieves and processes raw content from specified URLs, ideal for data collection, content analysis, and research tasks.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlsYesList of URLs to extract content from
extract_depthNoDepth of extraction - 'basic' or 'advanced', if usrls are linkedin use 'advanced' or if explicitly told to use advancedbasic
include_imagesNoInclude a list of images extracted from the urls in the response
formatNoThe format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.markdown
include_faviconNoWhether to include the favicon URL for each result
queryNoUser intent query for reranking extracted chunks based on relevance

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'retrieves and processes raw content' but doesn't disclose critical behavioral traits: whether it requires authentication, rate limits, error handling, pagination, or what the response structure looks like. The description adds minimal context beyond the basic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two concise sentences. The first sentence states the core functionality, and the second provides use cases. There's no wasted text, though it could be slightly more front-loaded with sibling differentiation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or behavioral constraints. For a web extraction tool with multiple configuration options and no structured output documentation, the description should provide more context about the extraction results and limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions general purpose but no parameter semantics. Baseline 3 is appropriate when schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'retrieves and processes raw content from specified URLs' with specific verbs and resource. It mentions use cases like 'data collection, content analysis, and research tasks' which helps understanding. However, it doesn't explicitly differentiate from sibling tools like tavily-crawl or tavily-search, which likely have overlapping web-related functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings (tavily-crawl, tavily-map, tavily-search). It mentions the tool is 'ideal for data collection, content analysis, and research tasks' but doesn't specify contexts where alternatives might be better. There's no explicit when/when-not guidance or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

tavily-mapB

A powerful web mapping tool that creates a structured map of website URLs, allowing you to discover and analyze site structure, content organization, and navigation paths. Perfect for site audits, content discovery, and understanding website architecture.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe root URL to begin the mapping
max_depthNoMax depth of the mapping. Defines how far from the base URL the crawler can explore
max_breadthNoMax number of links to follow per level of the tree (i.e., per page)
limitNoTotal number of links the crawler will process before stopping
instructionsNoNatural language instructions for the crawler
select_pathsNoRegex patterns to select only URLs with specific path patterns (e.g., /docs/.*, /api/v1.*)
select_domainsNoRegex patterns to restrict crawling to specific domains or subdomains (e.g., ^docs\.example\.com$)
allow_externalNoWhether to return external links in the final response

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must cover behavioral traits. It mentions 'crawler' but does not disclose how it handles JavaScript, rate limits, robot.txt, or data retention. The description is insufficient for an agent to understand side effects or constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, consisting of two sentences that efficiently convey the tool's value. However, it could be structured to front-load the core action more clearly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, no output schema, and no annotations, the description should explain the output structure (e.g., tree vs. list) and how the map is presented. It omits these critical details, making it incomplete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema, simply restating the overall purpose without elaborating on parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it creates a structured map of website URLs for discovering site structure, content organization, and navigation paths. It distinguishes from siblings (crawl, extract, search) by focusing on mapping and analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context ('Perfect for site audits, content discovery, and understanding website architecture') but lacks explicit guidance on when not to use or how it compares to siblings, leaving the agent to infer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updatesv1.0.0
    • First observedtavily-crawl
    • First observedtavily-extract
    • First observedtavily-map
    • First observedtavily-search
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: crawling (tavily-crawl) focuses on structured exploration from a base URL, extraction (tavily-extract) retrieves raw content from specific URLs, mapping (tavily-map) analyzes site structure, and search (tavily-search) provides real-time web results. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tool names follow a consistent 'tavily-' prefix with a descriptive action suffix (crawl, extract, map, search), using a uniform hyphenated style. This predictable pattern enhances readability and reduces confusion, with no deviations in naming conventions.

Tool Count5/5

With 4 tools, the server is well-scoped for its web-related domain, covering key operations like crawling, extraction, mapping, and search without bloat. Each tool earns its place by addressing a distinct aspect of web interaction, making the count appropriate and manageable.

Completeness5/5

The tool set provides complete coverage for web-based tasks, including discovery (crawl, map), content retrieval (extract, search), and analysis. There are no obvious gaps; agents can perform end-to-end workflows from finding sites to extracting and analyzing content without dead ends.

Maintenance

ActivityActive
ResponsivenessUnresponsive

Resources

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