MCP Agent Toolkit
Enables agents to share state via a blackboard, record and retrieve failure resolutions, and cache LLM responses to avoid redundant API calls.
README
MCP Agent Toolkit
An MCP (Model Context Protocol) server that exposes production agent-kernel tools — blackboard shared state, SCAR failure memory, and LLM response cache — as standard MCP tools any Claude or GPT agent can call.
What it does
Connects the three core reliability patterns from 18 months of building production multi-agent systems into a single MCP server:
| Tool Group | Tools | What it solves |
|---|---|---|
| Blackboard | blackboard_write, blackboard_read, blackboard_list |
Shared agent state without direct coupling |
| SCAR Memory | scar_lookup, scar_record |
Repeated failure prevention — find the known fix before retrying |
| Response Cache | cache_get, cache_set |
Stop paying twice for identical LLM requests |
Quick start
npm install
npm start
The server runs on stdio — standard MCP transport that works with Claude Desktop, Claude Code, and any MCP-compatible client.
Use with Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"agent-toolkit": {
"command": "node",
"args": ["/path/to/mcp-agent-toolkit/src/server.js"]
}
}
}
Tool reference
Blackboard
blackboard_write(run_id, agent, key, value) — persist an artifact
blackboard_read(run_id, agent, key) — read latest artifact
blackboard_list(run_id, agent) — list all keys for an agent
Agents communicate through the blackboard, never directly. The Coder writes code. The Auditor reads code. If either fails and retries, the other's work is still in SQLite.
SCAR Memory
scar_lookup(agent, error_type, context?) — retrieve known resolution
scar_record(agent, error_type, resolution, context?) — store a new resolution
When an agent hits JSONDecodeError and you fixed it with json_repair(), record it. Next time any agent hits the same error in the same context, scar_lookup returns the fix before the retry loop starts.
Response Cache
cache_get(messages, model) — check cache before calling LLM
cache_set(messages, model, response, provider) — store response after call
SHA-256 hashes the messages + model into a cache key. Identical requests never hit the API twice.
Tests
npm test
13 tests covering blackboard isolation, SCAR round-trips, and cache hit/miss behavior.
Stack
- MCP SDK (
@modelcontextprotocol/sdk) — the official Anthropic MCP server library - node:sqlite — Node.js 22 built-in synchronous SQLite, zero native compilation
- stdio transport — standard MCP pattern, works with any client
Related repos
- agent-scars — standalone SCAR pattern
- agent-recall — solution memory
- equilibrium — the AgentKernel these patterns came from
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.