context-mcp 0.1.1

MCP server for context management, RAG processing, and temporal reasoning - inspired by memory-gate patterns
Documentation

context-mcp

Crates.io Documentation License: MIT

MCP server for context management, temporal metadata, and lightweight retrieval/RAG plumbing.

This crate is intended to be a "memory service" for agents: store/retrieve context items with timestamps and metadata, and support simple query/retrieval patterns.

Features

  • Multi-tier Storage: In-memory (LRU), cache, and disk persistence
  • Temporal Reasoning: Timestamps and age tracking for context relevance
  • RAG Processing: CPU-optimized retrieval-augmented generation support
  • MCP Protocol: Full compatibility with VS Code, Copilot, and other MCP clients
  • Safe Input Handling: Integrates with security-mcp for screened inputs

Status

Alpha / under active development. APIs and tool names may change.

Installation

Add this to your Cargo.toml:

[dependencies]
context-mcp = "0.1.0-alpha.1"

Usage

As a Library

use context_mcp::{ContextStore, StorageConfig, Context, ContextDomain};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create storage configuration
    let config = StorageConfig {
        memory_cache_size: 1000,
        enable_persistence: true,
    };

    // Create context store
    let store = ContextStore::new(config)?;

    // Store some context
    let ctx = Context::new("This is some important information", ContextDomain::Code);
    let id = store.store(ctx).await?;

    // Retrieve it
    let retrieved = store.get(&id).await?;
    println!("Retrieved: {}", retrieved.content);

    Ok(())
}

As an MCP Server

Run as HTTP server:

context-mcp --host 127.0.0.1 --port 3000

Run as stdio transport:

context-mcp --stdio

What It Does Today (Code-Backed)

  • Runs an MCP-compatible JSON-RPC server over HTTP/WebSocket.
  • Stores context with IDs, domains, timestamps, and metadata.
  • Supports tiered storage primitives (in-memory + on-disk via sled).
  • Provides MCP tools for context storage, retrieval, and querying.
  • Basic temporal reasoning with time-based filtering.

What It Does Not Do Yet

  • Production-grade embedding/vector search.
  • Full RAG pipelines with reliable chunking/citation policies.
  • Strong consistency guarantees across distributed replicas.
  • Advanced query capabilities (semantic search, etc.).

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │    │  Context Gateway │    │ Storage Layer   │
│                 │    │                  │    │                 │
│ • VS Code       │◄──►│ • Store/Retrieve │◄──►│ • In-Memory LRU │
│ • Copilot       │    │ • Temporal Query │    │ • Sled Disk DB  │
│ • CLI Tools     │    │ • RAG Processing │    │ • Vector Index  │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Contributing

See CONTRIBUTING.md for development setup and contribution guidelines.

License

Licensed under the MIT License. See LICENSE for details.