mcp-server-evolutiondb 1.13.0

MCP (Model Context Protocol) server backed by EvolutionDB — long-term memory plus a personal-assistant action loop (open loops, drafts, human-approved send across gmail/teams/outlook/slack/imessage) for Claude Desktop / Claude Code.

mcp-server-evolutiondb

A Model Context Protocol server that gives Claude Desktop / Claude Code persistent long-term memory backed by EvolutionDB. Anything Claude decides to remember during a conversation is written to a real database; in any future session — same window or weeks later — Claude can search it back without you having to repaste context.

Install

pipx install mcp-server-evolutiondb
# or:  pip install --user mcp-server-evolutiondb

The package installs the mcp-server-evolutiondb console entry-point (also aliased as mcp-server-evosql). It speaks the PostgreSQL wire protocol over psycopg, so installation is pure-Python — no C toolchain, no libevosql-memory.so to build. EvolutionDB still has to be running somewhere reachable; docker compose up -d in the main repo is the easiest way.

   ┌──────────────────┐         ┌─────────────────────┐         ┌────────────────┐
   │  Claude Desktop  │  stdio  │  mcp-server-evosql  │  TCP    │   EvolutionDB  │
   │   (or Claude     │ ◀──────▶│  (this package)     │ ◀──────▶│  (port 9967)   │
   │     Code)        │  JSON-  │                     │         │                │
   │                  │   RPC    │  save_memory       │         │ MEMORY STORE   │
   │                  │   2.0    │  search_memory     │         │ ENTITY STORE   │
   │                  │          │  recent_memories   │         │                │
   │                  │          │  forget            │         │                │
   │                  │          │  list_tags         │         │                │
   └──────────────────┘          └─────────────────────┘         └────────────────┘

Why

The default Claude experience is stateless — every new chat starts from scratch, so you waste tokens re-explaining who you are, what project you're on, what your preferences are. Plug this server in and the model:

• saves preferences / decisions / facts during natural conversation,
• searches them back the next time you ask something related,
• forgets entries on demand,
• never sees the user_id that pins the namespace (we override it server-side, so the model can't accidentally fragment the namespace by inventing IDs across sessions).

Token math: 100 chats × 3,000 tokens of pre-loaded context (~$0.90 on Sonnet) → 100 chats × 250 tokens of just-relevant facts pulled on demand ($0.26). Roughly 3.5× cheaper inputs without losing context fidelity.

What's exposed to Claude

All under one evolutiondb-memory MCP server. Memory (the core):

Tool	Purpose
save_memory	Persist a fact + optional tags
search_memory	Hybrid semantic + keyword search (before answering)
recent_memories	Last N saved facts (most-recent-first)
forget	Delete by key
list_tags	All distinct tags in use, with counts
show_profile · expand_episode · feedback · restore_memory · set_language	retrieval/profile extras

Personal assistant + action loop (over your synced mail/chat — see below):

Tool	Purpose
daily_brief	Who's waiting on you, what you owe, your day
suggest_reply	Draft a reply for an open loop (grounded)
queue_reply	Queue a drafted reply for approval (sends nothing)
list_pending_replies	Review the outbox
approve_send	The only tool that delivers (opt-in, gated)
reject_reply	Cancel a queued reply
outbox_audit · send_scheduled	the send trail + flush due scheduled sends

Each call's user_id is overridden server-side from the MCP_USER_ID env var — stops the model from drifting the namespace across "user" / "default_user" / your name etc.

The assistant action loop

Beyond memory, the server runs a human-in-the-loop action loop over the sources you sync in (gmail / teams / outlook / slack / imessage, via the sibling *-sync connectors):

read (your mail/chat) → understand (open loops, self-model)
   → suggest (draft a reply) → queue → APPROVE → send → loop resolved

It is read + suggest by default — nothing is ever sent until you opt in and approve each reply. Drive it from Claude (the tools above) or from one CLI:

evolutiondb-brief                 # who's waiting on you, what you owe
evolutiondb-brief --queue 3       # draft + queue replies for the top 3 (nothing sent)
evolutiondb-outbox list           # review what's queued
evolutiondb-outbox show <id>      # preview exactly what would go out
evolutiondb-brief --approve       # deliver (dry-run unless sending is enabled)

Turning on sending (deliberately)

Sending is off by default and the sync connectors stay read-only. To enable it, ask the onboarding helper what a channel needs — it prints the exact env + the one-time consent step, and writes nothing:

evolutiondb-send-setup                  # status: enabled? channels? guards?
evolutiondb-send-setup --channel gmail  # the env to enable gmail + its auth step

Sending only happens when both EVOSQL_SEND_ENABLED=1 and a per-channel send scope (gmail.send / Mail.Send / ChatMessage.Send / chat:write) are set, and is wrapped in safe-default guards: an undo window, a rate cap, dedup (no double-reply), and a queryable audit trail. Full design + the invariants: docs/adr/ADR-004-action-loop-send-approval.md.

Install + run

1. Bring up EvolutionDB

cd /path/to/evolutiondb
docker compose up -d

Zero-Docker alternative: set EVOSQL_EMBEDDED=1 and the MCP server spawns its own EvolutionDB on first connect (against a per-user data dir, reaped on exit) — no docker compose step. If no evosql-server binary is found (EVOSQL_SERVER_BINARY / PATH / dev checkout), it auto-fetches the prebuilt one for your platform from the GitHub release (checksum-verified, cached) — so a plain pip install + EVOSQL_EMBEDDED=1 needs neither Docker nor a binary. Pre-fetch with evolutiondb-embedded-fetch; disable auto-fetch with EVOSQL_EMBEDDED_AUTOFETCH=0. An already-running instance still wins, so it's safe to leave on.

2. Build the SDK once

make -C client/libevosql-memory

3. Configure Claude Desktop

Open the config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Drop in the entry from examples/claude_desktop_config.json, substituting the absolute paths for your machine. Quit + restart Claude Desktop.

You'll see a small 🔌 / hammer icon in the bottom-right of the chat composer once evolutiondb-memory is connected.

4. Talk normally

Say "remember that I take my espresso single-shot, no sugar"; Claude will run save_memory(...). Days later open a new chat, ask "what do I drink?" — Claude runs search_memory(...) and recalls.

One-command setup for all supported hosts

pip install mcp-server-evolutiondb puts a small evolutiondb-mcp-setup helper on $PATH. It autodetects every MCP host installed on the machine and merges the evolutiondb-memory server into each of their config files in one go:

evolutiondb-mcp-setup
# [setup] Claude Desktop: wrote /Users/you/Library/Application Support/Claude/claude_desktop_config.json
# [setup] ChatGPT Desktop: not detected — skipping
# [setup] Gemini CLI: wrote /Users/you/.gemini/settings.json

The merge is idempotent — running it twice changes nothing the second time. Existing mcpServers entries are preserved; only the evolutiondb-memory block is overwritten.

Useful flags:

• --dry-run — show what would change, write nothing.
• --client claude-desktop — only configure one host (repeatable).
• --user-id NAME — override the sticky namespace (default: $MCP_USER_ID or default_user).
• --port 5433 — non-default EvolutionDB PG-wire port.

Override config paths via env vars when the auto-detected location is wrong for your install: EVOSQL_CLAUDE_CONFIG, EVOSQL_CHATGPT_CONFIG, EVOSQL_GEMINI_CONFIG.

Per-host manual config

If you'd rather hand-edit, every host accepts the same JSON shape — the mcpServers block is part of the MCP spec, not specific to any vendor:

{
  "mcpServers": {
    "evolutiondb-memory": {
      "command": "uvx",
      "args":    ["mcp-server-evolutiondb"],
      "env": {
        "EVOSQL_HOST":      "127.0.0.1",
        "EVOSQL_PORT":      "5433",
        "EVOSQL_USER":      "admin",
        "EVOSQL_PASSWORD":  "admin",
        "EVOSQL_DATABASE":  "evosql",
        "MCP_USER_ID":      "your-handle",
        "MCP_STORE_PREFIX": "mcp"
      }
    }
  }
}

File paths:

Host	macOS	Linux / WSL	Windows
Claude Desktop	~/Library/Application Support/Claude/claude_desktop_config.json	~/.config/Claude/claude_desktop_config.json	%APPDATA%\Claude\claude_desktop_config.json
Claude Code	~/.claude/mcp.json	~/.claude/mcp.json	%USERPROFILE%\.claude\mcp.json
ChatGPT Desktop	~/Library/Application Support/ChatGPT/mcp.json	~/.config/ChatGPT/mcp.json	%APPDATA%\ChatGPT\mcp.json
Gemini CLI	~/.gemini/settings.json	~/.gemini/settings.json	%USERPROFILE%\.gemini\settings.json

After editing, restart the host (or for Gemini CLI, just run the next command — it re-reads its config on every launch).

Remote HTTP transport (web ChatGPT, Gemini app, etc.)

Web-only MCP hosts cannot spawn a local process and so cannot use stdio. They speak the spec's "streamable HTTP" transport instead. Run the same server as an HTTP listener and point a tunnel at it:

# 1. Start the HTTP listener (default port 8970, path /mcp).
export EVOSQL_MCP_AUTH_TOKEN=$(openssl rand -hex 24)
evolutiondb-mcp-http

# 2. Expose it through a tunnel. Pick whichever you already use.
cloudflared tunnel --url http://127.0.0.1:8970
#   → outputs https://random-words.trycloudflare.com
# OR
ngrok http 8970
#   → outputs https://random.ngrok-free.app

In the web host's "MCP Connectors" or "Custom MCP server" panel, register:

• URL: https://your-tunnel-host/mcp
• Authorization: Bearer <the token you just generated>

The server enforces three guards when bound to anything other than loopback:

• Every request must carry the bearer token.
• Origin (when present) must be on the allow-list. The defaults cover chat.openai.com, chatgpt.com, claude.ai, gemini.google.com. Override via EVOSQL_MCP_ALLOWED_ORIGINS (comma-separated).
• Bound to 127.0.0.1 by default. The CLI warns if you flip to 0.0.0.0 without a token.

Identical tool surface as stdio — save_memory, search_memory, recent_memories, forget, list_tags. New tools land in both transports at once because both go through the same MCPServer.handle.

Configuration

Env var	Default	Purpose
EVOSQL_HOST	127.0.0.1	DB host
EVOSQL_PORT	9967	EVO port
EVOSQL_USER	admin	DB user
EVOSQL_PASSWORD	admin	DB password
MCP_USER_ID	default_user	Sticky namespace for every tool call
MCP_STORE_PREFIX	mcp	Catalog object prefix
EVOSQL_PYTHON_SDK	(auto-discovered)	Override path to the Python ctypes binding
EVOSQL_MEMORY_LIB	(auto-discovered)	Override path to libevosql-memory.dylib/so
EVOSQL_EMBEDDING_PROVIDER	none	openai, local, or none. Turns search_memory into hybrid semantic+keyword
EVOSQL_EMBEDDING_MODEL	provider default	Override embedding model (e.g. text-embedding-3-large)
OPENAI_API_KEY	—	Required when provider is openai

Context-layer boosts (all opt-in)

These layer extra signal onto retrieval. Each is off by default so the plain hybrid search is unchanged; turn them on per deployment.

Env var	Default	Purpose
EVOSQL_SALIENCE_BOOST	0	Weight (0-1) for the per-row salience score (recency × sender activity × thread depth × feedback)
EVOSQL_GRAPH_BOOST	0	Weight (0-1) for knowledge-graph spreading activation — relational queries reach rows that never name the queried entity
EVOSQL_PROFILE_BOOST	0	Weight (0-1) for the user interest profile — biases results toward the cluster the query points at
EVOSQL_DECAY	0	1 to fade old, unused rows (archived, never deleted; include_archived digs them back up)
EVOSQL_GRAPH_BUILD	1	Build graph edges inline on save (cheap; needed before the graph boost helps)
EVOSQL_ENTITY_EXTRACT	1	Extract entities inline on save

salience, profile, and decay read data the background scheduler maintains, so run it for them to have any effect:

python -m mcp_server_evosql.scheduler run     # hourly/daily/weekly jobs
python -m mcp_server_evosql.scheduler status  # last run + failure ratio

It also embeds + entity-extracts new rows, recomputes salience, refreshes the interest profile, regenerates episode summaries, and runs the decay pass.

Semantic search

search_memory runs in keyword mode by default. Setting EVOSQL_EMBEDDING_PROVIDER=openai (with OPENAI_API_KEY) or EVOSQL_EMBEDDING_PROVIDER=local flips it into hybrid mode: new saves are tagged with a dense vector and queries are ranked by a weighted mix of cosine similarity (0.7) and substring overlap (0.3). Rows saved before you enabled embeddings continue to score on keyword overlap only, so the switch is non-destructive — older memories don't disappear, they just rank lower against semantically strong matches.

The local provider needs an extra install:

pip install 'mcp-server-evolutiondb[embeddings-local]'

Tests

cd client/mcp-server-evosql
python3 tests/test_mcp.py

Eight cases — initialize handshake, tools/list discovery, save+search round-trip, tag-filtered search, recent ordering, forget, list_tags aggregation, and the "user_id can't be hijacked from the LLM side" isolation case. Each test spawns the server as a real subprocess and talks JSON-RPC, so framing bugs that an in-process unit test would hide get caught.

Inspect the database directly

While Claude is using the server, open another terminal and:

docker compose exec evosql evosql-cli -W admin

Then:

SELECT mem_namespace, mem_key, mem_value FROM __mem_mcp_mem;
ENTITY RANK FROM mcp_ents;

Everything Claude has decided to remember is right there as queryable rows — no opaque blob storage.

Wire format

Newline-delimited JSON-RPC 2.0 over stdio (no Content-Length headers — that's the LSP variant; MCP uses plain \n-delimited). The server speaks protocol version 2024-11-05.

{"jsonrpc":"2.0","id":1,"method":"initialize",
 "params":{"protocolVersion":"2024-11-05","capabilities":{}}}

{"jsonrpc":"2.0","id":2,"method":"tools/list"}

{"jsonrpc":"2.0","id":3,"method":"tools/call",
 "params":{"name":"save_memory",
           "arguments":{"fact":"loves jazz","tags":["preference"]}}}

Errors come back as {"jsonrpc":"2.0","id":N,"error":{"code":..,"message":..}} or as a tools/call result with isError: true.

Known limitations

• Single-process EvolutionDB connection. The server holds one Connection — the SDK contract is one-per-thread, and MCP stdio is inherently single-threaded so this is fine.
• No streaming responses. Tool results return as single JSON blobs. Larger memories (>100 facts) take ~50 ms to serialise; the protocol can stream but Claude's tool-use UI doesn't render partial responses anyway.
• Authentication via env-vars only. If you expose the server to another machine (which you shouldn't — it's stdio), set EVOSQL_PASSWORD accordingly. The server doesn't rotate secrets.