The Model Context Protocol server that exposes attune workflows, help, and memory as tools

Overview

The MCP server is attune's Model Context Protocol implementation — it exposes attune's workflows, help system, and memory as structured tools, resources, and prompts that an MCP client (Claude Code) can call. The server class is EmpathyMCPServer; it speaks MCP over stdio and is launched with python -m attune.mcp.server.

It is how every other attune feature reaches a conversation: the code_review, security_audit, memory_store, help_lookup, … tools you call in Claude Code are registered and dispatched here. This page documents the server itself — its architecture, how to run and register it, and the tool/resource/prompt surface — not each individual tool (those belong to their own features).

You reach it these ways:

registration — a .mcp.json entry runs python -m attune.mcp.server (the plugin ships one); Claude Code connects over stdio;
the Python API — from attune.mcp import create_server, EmpathyMCPServer, for embedding or testing the server.

Concepts

`EmpathyMCPServer` and its mixins

EmpathyMCPServer(MemoryHandlersMixin, WorkflowHandlersMixin) is the core server. The mixins supply handler groups: WorkflowHandlersMixin runs the analysis workflows, MemoryHandlersMixin handles cross-session memory. A RateLimiter guards against tool-call floods. create_server() builds a ready instance.

The tool surface — 41 built-in tools in 5 categories

At startup the server merges five built-in tool-schema groups (from attune.mcp.tool_schemas) into one registry, then lets installed plugins register more (_register_plugin_tools — e.g. attune-redis adds its redis_* tools), so server.tools can hold more than these 41:

Category	Function	Count	Examples
Workflow	`get_workflow_tools()`	21	`code_review`, `security_audit`, `test_generation`, `release_notes`, `rag_knowledge_query`
Utility	`get_utility_tools()`	7	`auth_status`, `telemetry_stats`, `attune_set_level`, `context_get`
Help	`get_help_tools()`	5	`help_lookup`, `help_update`, `help_status`
Memory	`get_memory_tools()`	4	`memory_store`, `memory_retrieve`, `memory_search`
Personal memory	`get_personal_memory_tools()`	4	`personal_memory_capture`, `personal_memory_recall`

A _build_dispatch_table() maps each tool name to the handler method that runs it; call_tool(name, arguments) is the async dispatch entry.

Resources and prompts

Beyond tools, the server publishes three resources (read-only data) and three prompts (reusable prompt templates):

Kind	Names
Resources	`attune://workflows`, `attune://auth/config`, `attune://telemetry`
Prompts	`security-scan`, `test-gen`, `cost-report`

get_resource_list() and get_prompt_list() return them.

Rate limiting

A sliding-window RateLimiter(max_calls=60, window_seconds=60.0) caps tool calls — by default 60 calls per 60-second window — so a runaway client can't flood the server.

Transport and launch

The server runs over stdio: main() calls asyncio.run(_run_stdio()). Launch it with python -m attune.mcp.server. It logs to a temp file (attune-mcp.log) and loads .env so an ANTHROPIC_API_KEY is available to tools that need it (e.g. the help polish pass).

Notes & tips

Depend on the documented public surface. The supported API is create_server and EmpathyMCPServer from attune.mcp; the tool-schema group functions live in attune.mcp.tool_schemas. Handler methods and the dispatch table are internal.
await call_tool. It's the one async entry; the inspection helpers are sync.
Read the log file to debug. stdout is reserved for the protocol.
Tool contracts live with their features. This page covers the server; each tool's inputs are documented on its own feature page.