letta-appserver-mcp
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
letta-appserver-mcp
Granular MCP server (Streamable HTTP) for the Letta App Server (:4500).
Letta is the single source of truth for memory — no local fallback.
Tools (13)
Agents: agent_create, agent_get, agent_list (optional
query/name/tags/limit/order filters), agent_lookup,
agent_update (optional system/modelSettings/
contextWindowLimit/hidden in addition to name/description/model/
tags; persona/human are create-time only — stored agents carry a single
system prompt), agent_delete (requires confirm:true),
models_list, bridge_health.
Memory: memory_save, memory_search (retrieval only — you reason over
the answer), memory_get, memory_delete.
Session: session_ask — reason with the agent's memory and answer.
One-shot turn, files nothing durable. Use for synthesis/advice/
explanation; use memory_search for retrieval.
Request / response contracts
Every tool takes a JSON object (see its schema) and returns a strict
envelope: {ok:true, data:{…}} or {ok:false, error:{code, message}}.
agent_id everywhere accepts an id or a name.
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memory_search retrieves (you reason); session_ask reasons (it
answers); memory_save files. Neither ask path instructs a durable
write — "saves nothing" is a prompt contract, not a storage lock.
Every tool that acts on an agent takes a required agent_id — the MCP
keeps no default agent. Which project uses which agent is decided per
request by the caller; one project may use many agents.
Every agent_id parameter accepts an id or a name ("dhruba" works;
exact match preferred, substring fallback, errors on miss/ambiguity), and
every response echoes agent_name next to agent_id.
New sessions bootstrap ids via agent_lookup (compact id/name/model —
no system-prompt dump; optional name filter). block-registry.json in
backups is the offline id record.
agent_create takes optional model / embedding handles, falling back
to DEFAULT_MODEL / DEFAULT_EMBEDDING (all empty by default — the server
decides when omitted). Pass them explicitly per call; change them later
via agent_update.
All responses are a strict envelope:
{ok:true, data:{…}} or {ok:false, error:{code, message}} (see the
table above for each tool's data shape).
Related MCP server: Letta MCP Server
Setup — full stack (App Server + MCP, one command)
cp .env.example .env # set LETTA_APP_SERVER_TOKEN + MCP_AUTH_TOKEN (+ model path below)
docker compose up -d
curl http://[IP_ADDRESS]:4500/readyz # App Server (letta-code image)
curl http://[IP_ADDRESS]:6507/healthz # MCP serverZero-cost path (no API keys): install Ollama on the host, ollama pull qwen3, then connect it once with docker exec -it <app-server-container> letta connect ollama pointed at http://host.docker.internal:11434, and
pass that model in agent_create. Note weaker local models can behave
unexpectedly as agent drivers — prefer a tool-capable one. Paid path: set
ANTHROPIC_API_KEY or OPENAI_API_KEY in .env instead.
docker-compose.yml runs the App Server straight from the prebuilt
ghcr.io/letta-ai/letta-code image (same letta server startup as the
official letta-app-server-deploy repo) alongside this MCP, on a shared
network with persistent letta-state / letta-workspace volumes. Pin it
with LETTA_CODE_VERSION (kept compatible with
@letta-ai/letta-agent-sdk in package.json). All ports, tokens, and
limits come from .env — see .env.example.
agent_create accepts optional model / embedding handles, falling back
to DEFAULT_MODEL / DEFAULT_EMBEDDING (all empty by default — the server
decides when omitted). models_list shows the LLM catalog; embeddings have
no catalog endpoint, so set DEFAULT_EMBEDDING to a provider-qualified
handle. For free local embeddings: docker compose --profile local-embeddings up -d, docker exec ollama ollama pull qwen3-embedding:0.6b, then OLLAMA_BASE_URL=http://ollama:11434/v1 plus
DEFAULT_EMBEDDING=ollama/qwen3-embedding:0.6b in .env.
SESSION_CWD=/workspace is the directory sessions work in — a persistent
volume where the agent reads/writes files during turns.
Setup — MCP alone (against an existing App Server)
cp .env.example .env # set LETTA_APP_SERVER_URL=http://[IP_ADDRESS]:4500 + both tokens
npm install
npm run build
npm start # $MCP_HOST:$MCP_PORT/mcp, GET /healthzOpenCode wiring (HTTP, authenticated)
{ "mcp": { "letta-mcp": {
"type": "remote",
"url": "http://localhost:6507/mcp",
"headers": { "Authorization": "Bearer {env:MCP_AUTH_TOKEN}" },
"enabled": true } } }/mcp requires the MCP_AUTH_TOKEN Bearer token (401 without it);
/healthz stays open for container healthchecks. The server refuses to
start if MCP_AUTH_TOKEN is unset.
Config
Everything is env-driven — see .env.example. No ports, URLs, timeouts,
limits, or default prompt text are hardcoded in src/.
Model provider keys live on the App Server, never here.
This server cannot be deployed
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