Contentful Delivery MCP Server
LocalAbout Contentful Delivery MCP Server
Contentful Delivery MCP Server lets AI assistants query and retrieve content from Contentful's Delivery API. It covers entries, assets, content type schemas, pagination, and rich text handling.
Key features
- 01Search entries with natural-language queries
- 02Fetch entries by ID or content type
- 03Browse assets and retrieve asset details
- 04Inspect content type schemas
- 05Pagination and rich text handling
Use cases
- 01Search Contentful content from an AI assistant
- 02Retrieve a specific entry or filtered entry list
- 03Browse media assets and content type definitions
MCP support
The server exposes Contentful Delivery API operations as MCP tools for AI clients, including entry queries, asset access, and content type inspection.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEContentful Delivery MCP Server
A Model Context Protocol (MCP) server that provides seamless access to Contentful's Delivery API through AI assistants. Query and retrieve content entries, assets, and content types using natural language.
Quick Start
Install the package in your project:
npm install @mshaaban0/contentful-delivery-mcp-server
Or globally:
npm install -g @mshaaban0/contentful-delivery-mcp-server
Set up your Contentful credentials:
export CONTENTFUL_SPACE_ID="your_space_id"
export CONTENTFUL_ACCESS_TOKEN="your_access_token"
# Optional: Restrict content to specific content types
export CONTENTFUL_CONTENT_TYPE_IDS="blogPost,article,product"
Features
- Natural language queries to search content
- Retrieve entries by ID or content type
- Asset management
- Content type schema access
- Pagination support
- Rich text content handling
Available Tools
query_entries- Natural language search across all contentget_entry- Fetch specific entry by IDget_entries- List entries with filteringget_assets- Browse all assetsget_asset- Get asset details by IDget_content_type- View content type schemaget_content_types- List available content types
Integration with Mastra AI
Mastra AI provides seamless integration with this MCP server. Here's how to set it up:
import { MastraMCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
// Initialize the MCP client
const contentfulClient = new MastraMCPClient({
name: "contentful-delivery",
server: {
command: "npx",
args: ["-y", "@mshaaban0/contentful-delivery-mcp-server@latest"],
env: {
CONTENTFUL_ACCESS_TOKEN: "your_access_token",
CONTENTFUL_SPACE_ID: "your_space_id",
// Optional: Restrict content to specific content types
CONTENTFUL_CONTENT_TYPE_IDS: "blogPost,article,product"
}
}
});
// Create an AI agent with access to Contentful
const assistant = new Agent({
name: "Content Assistant",
instructions: `
You are a helpful assistant with access to our content database.
Use the available tools to find and provide accurate information.
`,
model: "gpt-4",
});
// Connect and register tools
await contentfulClient.connect();
const tools = await contentfulClient.tools();
assistant.__setTools(tools);
// Example usage
const response = await assistant.chat("Find articles about machine learning");
Development
# Clone the repo
git clone https://github.com/mshaaban0/contentful-delivery-mcp-server.git
# Install dependencies
npm install
# Build
npm run build
# Development with auto-rebuild
npm run watch
# Run the inspector
npm run inspector
Debugging
The MCP Inspector provides a web interface for debugging:
npm run inspector
Visit the provided URL to access the debugging tools.
Resources
Security Audits
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
