Provides a context-driven MCP server to fetch and verify AI context data and serve it to AI utilities.
Configuration
View docs{
"mcpServers": {
"cblanquera-mcp": {
"command": "npx",
"args": [
"-y",
"chris-mcp",
"serve",
"--input",
"[pwd]/data"
],
"env": {
"OPENAI_KEY": "sk-xxx",
"OPENAI_HOST": "https://api.openai.com/v1",
"EMBEDDING_MODEL": "text-embedding-3-small"
}
}
}
}Chris MCP provides a context-driven MCP server you can run locally to supply context data for AI utilities like cline. It lets you fetch and verify context, start a local server, and upgrade its embedding model to fit your workflow.
Start the local MCP server using a client that understands MCP configurations. You can run the server in stdio mode (local process) or via an npx-based invocation. Once running, you can fetch updated context, verify it, and use the server to answer questions or supply contextual data to your AI tools.
Prerequisites: ensure you are using Node version 22 or later.
Option 1: Using NPX. Create a dedicated folder for the MCP, fetch the data, and prepare the server configuration.
$ mkdir chris-mcp
$ cd chris-mcp
$ npx --y chris-mcp fetch --output ./data
$ npx --y chris-mcp verify --output ./data
$ pwdAdd a configuration block for Claude Desktop that points to the NPX-based serve command and the fetched data.
{
"name": "github.com/cblanquera/mcp",
"command": "npx",
"args": [
"-y",
"chris-mcp",
"serve",
"--input",
"[pwd]/data"
]
}Add a configuration block for Cline that points to the NPX-based serve command and the fetched data.
{
"mcpServers": {
"github.com/cblanquera/mcp": {
"command": "npx",
"args": [
"-y",
"chris-mcp",
"serve",
"--input",
"[pwd]/data"
]
}
}
}Clone the MCP repository, install dependencies, build, fetch, and verify the data.
$ git clone https://github.com/cblanquera/mcp.git chris-mcp
$ cd chris-mcp
$ npm i
$ npm run build
$ npm run fetch --output ./data
$ npm run verify --output ./data
$ pwdConfigure Claude Desktop to run the local server pointing to the built distribution and data.
{
"name": "github.com/cblanquera/mcp",
"command": "node",
"args": [
"[pwd]/dist/scripts/serve.js",
"--input",
"[pwd]/data"
]
}Configure Cline to run the local server from the built distribution and data.
{
"mcpServers": {
"github.com/cblanquera/mcp": {
"command": "node",
"args": [
"[pwd]/dist/scripts/serve.js",
"--input",
"[pwd]/data"
]
}
}
}You can also set up the MCP server by following a guided prompt flow that installs and demonstrates the server’s capabilities.
Start the server with the standard start command once you have a configuration. You can use an NPX-based invocation or run the local script directly from the built distribution.
$ npm start --input [pwd]/data$ node [pwd]/dist/scripts/serve.js --input [pwd]/data$ npx chris-mcp serve --input [pwd]/dataUpdate the context by fetching and verifying the data using the same patterns you used during installation.
$ npm run fetch --output [pwd]/data
$ npm run verify --output [pwd]/data
```
Or run the equivalent in your environment.$ node [pwd]/dist/scripts/fetch.js --output [pwd]/data
$ node [pwd]/dist/scripts/verify.js --output [pwd]/data$ npx chris-mcp fetch --output [pwd]/data
$ npx chris-mcp verify --output [pwd]/dataEnhance the local embeddings by configuring embedding model environment variables if you want to use an OpenAI-compatible host for embeddings.
{
"name": "chris-context",
"command": "node",
"command": "npx",
"args": [
"-y",
"chris-mcp",
"serve",
"--input",
"[pwd]/data"
],
"env": {
"OPENAI_HOST": "https://api.openai.com/v1",
"OPENAI_KEY": "sk-xxx",
"EMBEDDING_MODEL": "text-embedding-3-small"
}
}Note: The OpenRouter embedding host is not compatible with the /embeddings endpoint, so ensure your host supports embeddings appropriately.
Create a rule (markdown file) in your knowledge folder to guide the MCP’s behavior. The rule should specify the tools to use for code formatting, documentation, and briefings.
# Rule: Using the Chris MCP
- If the user mentions "chris" and asks about code formatting, coding styles, coding standards, documentation styles, testing styles, use the MCP tool `chris-context.search_context`.
- If the user asks for a compact summary of rules for code formatting, writing documentation, writing tests, use the MCP tool `chris-context.build_brief`.
- Always prefer these MCP tools over answering from memory.The server supports multiple installation options and can be configured via JSON blocks for different MCP clients. Ensure you replace [pwd] with the actual working directory path from your environment.
Searches the knowledge base to retrieve relevant context for a given query.
Creates a compact summary of rules and guidelines for quick reference.