DataForSEO
DataForSEO MCP Server is a TypeScript implementation of the Model Context Protocol (MCP) for DataForSEO APIs.
It acts as a bridge between AI agents like Claude and DataForSEO’s powerful SEO data services.
This server enables AI to interact with selected DataForSEO APIs and retrieve valuable SEO insights through a standardized interface.
Features List
- 🔍 SERP API: Real-time Search Engine Results Page data for Google, Bing, and Yahoo
- 🔑 KEYWORDS_DATA API: Comprehensive keyword research and clickstream data
- 🕸️ ONPAGE API: Customizable website crawling for on-page SEO performance metrics
- 🧪 DATAFORSEO_LABS API: Advanced data on keywords, SERPs, and domains
Use Cases
- AI-powered SEO analysis: Integrate AI agents with DataForSEO to perform in-depth SEO audits and provide actionable recommendations.
- Automated keyword research: Use AI to conduct extensive keyword analysis and identify high-potential terms for content strategies.
- Competitive intelligence: Leverage AI to analyze competitor websites and SERPs, uncovering valuable insights for strategic planning.
- On-page optimization: Employ AI agents to crawl websites and suggest targeted improvements for better search engine performance.
How to Use It
- Clone the repository:
git clone https://github.com/dataforseo/mcp-server-typescript
cd mcp-server-typescript- Install dependencies:
npm install- Set up environment variables:
export DATAFORSEO_USERNAME=your_username
export DATAFORSEO_PASSWORD=your_passwordOptionally, specify which modules to enable:
export ENABLED_MODULES="SERP,KEYWORDS_DATA,ONPAGE,DATAFORSEO_LABS"- Build the project:
npm run build- Run the server:
node build/index.jsAvailable Modules:
- SERP: Real-time SERP data
- KEYWORDS_DATA: Keyword research and clickstream data
- ONPAGE: Website crawling and on-page SEO metrics
- DATAFORSEO_LABS: Advanced SEO data and analytics
To add new tools or modules, follow the structure outlined in the repository’s README. Create a new tool class extending BaseTool and implement the required methods. For a new module, create a directory under src/modules/, implement the module class, and register it in the configuration files.
FAQs
Q: How does the DataForSEO MCP Server handle rate limiting?
A: The server respects DataForSEO’s rate limits by default. You can implement additional rate limiting logic in the DataForSEOClient class if needed for your specific use case.
Q: Can I use this server with other AI models besides Claude?
A: Yes, the server follows the Model Context Protocol, making it compatible with any AI agent that supports MCP.
Q: How do I handle errors or unexpected responses from DataForSEO APIs?
A: The BaseTool class includes error handling methods. You can customize these in your tool implementations to handle specific API errors or unexpected responses.
Q: Is it possible to extend the server to support additional DataForSEO APIs?
A: Absolutely. The modular structure allows you to add new tools and modules easily. Check the “Adding New Tools/Modules” section in the README for a step-by-step guide.
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FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
Q: Is MCP secure? What are the main risks?
A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.
