# Memanto Mcp > Typed semantic memory for Claude. MCP server with opinions, designed for regulated B2B agents. - **Type:** MCP server - **Install:** `agentstack add mcp-geoffsdesk-memanto-mcp` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [geoffsdesk](https://agentstack.voostack.com/s/geoffsdesk) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [geoffsdesk](https://github.com/geoffsdesk) - **Source:** https://github.com/geoffsdesk/memanto-mcp - **Website:** https://github.com/geoffsdesk/memanto-mcp ## Install ```sh agentstack add mcp-geoffsdesk-memanto-mcp ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # memanto-mcp **Typed semantic memory for Claude. Memory with opinions, designed for regulated B2B agents.** Most "memory MCP" servers give you `save_memory(text)` and call it done. This one ships an opinionated memory model — typed records (`fact`, `decision`, `commitment`, `instruction`, …), provenance and confidence on every write, tenant-aware scoping, and bi-temporal recall — together with a Claude skill that tells the model *when* to remember vs. recall vs. answer, and how to keep citations and tenancy clean. If you're building B2B agents on Anthropic's platform — legal tech, finance, healthcare, customer ops, internal knowledge work — that's the audience this is built for. ## What's distinctive - **Typed memory.** Thirteen first-class memory types (`fact`, `preference`, `goal`, `decision`, `commitment`, `instruction`, `relationship`, `context`, `observation`, `event`, `artifact`, `learning`, `error`). Wrong type = noisy recall. Picking the right type is part of the API contract. - **Provenance and confidence on every record.** `explicit_statement` / `inferred` / `observed` / `validated` / `corrected` / `imported`, plus a 0.0–1.0 confidence score. Filterable on recall via `min_confidence`. The audit trail is in the data, not bolted on. - **Bi-temporal reads.** `recall_as_of(date)` returns memory as it stood on a date; `recall_current` excludes superseded/expired records. Built for "what did we know on " workflows. - **A bundled Claude skill that earns its keep.** [`skills/memanto-memory/SKILL.md`](skills/memanto-memory/SKILL.md) tells Claude when to use each verb, how to scope memories to tenant boundaries via composite `agent_id`, when raw recall beats synthesized answers (drafting external-facing text → raw recall, every time), and how to treat memory bodies as untrusted on read. - **One process.** Wraps the [memanto](https://github.com/moorcheh-ai/memanto) Python service layer directly — no separate REST process to run. ## What this is not - Not a general-purpose vector store. Memories are short, structured, and typed; if you need to index 100k contracts, do that in your document store and use this for the *decisions, commitments, and observations* the agent makes about them. - Not backend-agnostic (yet). Currently coupled to [Moorcheh](https://moorcheh.ai) as the vector backend. Free tier is 100k ops/month; pluggable backends are on the [roadmap](ROADMAP.md). - Not an enterprise audit system. The skill is opinionated about what regulated deployments need; the server doesn't yet enforce all of it. See the [roadmap](ROADMAP.md) for the gaps. ## Tools Nine MCP tools, namespaced under `memanto`: | Tool | Purpose | | -------------------------- | -------------------------------------------------------- | | `memanto_remember` | Store a typed memory (fact, preference, goal, decision, …) | | `memanto_recall` | Semantic search over an agent's memory | | `memanto_recall_current` | Recall only currently-active (non-superseded) memories | | `memanto_recall_as_of` | Recall as of a specific date | | `memanto_answer` | RAG-grounded answer using memanto's built-in LLM | | `memanto_create_agent` | Provision a new agent (creates a Moorcheh namespace) | | `memanto_list_agents` | List known agents | | `memanto_get_agent` | Get a single agent's metadata | | `memanto_delete_agent` | Delete an agent and all its memory (destructive) | ## Install ### As a Cowork plugin (Anthropic's Cowork mode) 1. Download the latest [`memanto-mcp.plugin`](https://github.com/geoffsdesk/memanto-mcp/releases/latest) release. 2. Drop it into Cowork via the plugin install UI. 3. Set `MOORCHEH_API_KEY` in the environment Cowork inherits, then restart Cowork. ### As a standalone MCP server (Claude Code, Cline, Cursor, Continue, …) ```bash pip install memanto-mcp export MOORCHEH_API_KEY="mch_..." ``` Then point your MCP host at the binary. Claude Code: ```bash claude mcp add memanto -- memanto-mcp ``` Cursor / Cline / Continue: add to your MCP config as a stdio server with command `memanto-mcp`. For the bundled skill to load in MCP hosts that support skills (Cowork, Claude Code with the skills plugin), copy [`skills/memanto-memory/SKILL.md`](skills/memanto-memory/SKILL.md) into the host's skills directory. ### Get a Moorcheh API key Sign up at . Free tier is 100k ops/month. ## First run ``` > Create a memanto agent called acme:matter-4711:user-jdoe and remember > that this matter has a 60-day notice period. ``` Claude calls `memanto_create_agent` then `memanto_remember(memory_type="fact", content="Matter 4711 (Acme): 60-day notice period.", confidence=0.95)`. ``` > What do we know about Matter 4711? ``` Claude calls `memanto_recall(query="Matter 4711", agent_id="acme:matter-4711:user-jdoe")` and quotes the stored fact. ``` > What was our position on Matter 4711 before the redesign on 2026-03-01? ``` Claude calls `memanto_recall_as_of(query="Matter 4711 position", agent_id=..., as_of_date="2026-02-28")`. ## How the skill thinks about scoping The server uses `agent_id` as the scoping primitive. The skill teaches Claude to treat it as a composite key: ``` agent_id = "::" ``` For example: `acme:matter-4711:user-jdoe`. Cross-tenant recall is the highest-impact failure mode in production memory systems — the skill makes Claude refuse it loudly rather than silently returning empty results when the boundary is wrong. For axes that don't fit the composite key (jurisdiction, region, conflict-of-interest group, product line), use `tags` — they're filterable on recall. ## Drafting rule When the user is composing **external-facing text** — a contract amendment, a customer-facing email, a regulatory filing, a clinical summary, a briefing memo — the skill instructs Claude to use `memanto_recall` (raw hits) and quote, not `memanto_answer` (synthesized). The audit cost of a paraphrased citation is too high. When the user is researching, exploring, or briefing themselves, `memanto_answer` is fine. ## Roadmap The honest gaps — what the skill assumes you'd want in a fully regulated deployment and what the server doesn't do yet — are documented in [ROADMAP.md](ROADMAP.md). Highlights: structured `source_uri` + `source_span` fields (citations currently get encoded inside `content`), versioned mutations + redact API, pluggable storage backends, and an HTTP transport for non-stdio hosts. ## Repository layout ``` memanto-mcp/ ├── .claude-plugin/plugin.json # Cowork plugin manifest ├── .mcp.json # MCP server registration (Cowork) ├── server/memanto_mcp_server.py # FastMCP server (stdio) ├── skills/memanto-memory/SKILL.md # the bundled skill ├── pyproject.toml # for `pip install memanto-mcp` ├── README.md # this file ├── ROADMAP.md # what's missing, ranked ├── CONTRIBUTING.md ├── CHANGELOG.md ├── LICENSE # MIT └── docs/ └── launch-post.md # cross-post draft ``` ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md). The two highest-leverage contributions right now are (1) a pluggable storage backend (interface + a SQLite or pgvector implementation) and (2) a structured citation model (`source_uri` + `source_span` + `quote`). Both are on the [roadmap](ROADMAP.md). ## License MIT. See [LICENSE](LICENSE). ## Credits - [memanto](https://github.com/moorcheh-ai/memanto) and [Moorcheh](https://moorcheh.ai) — the typed memory model and vector backend this wraps. - [Anthropic's MCP](https://modelcontextprotocol.io) and [Agent Skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills) — the protocol and skill format. - The teams shipping enterprise agentic systems whose published patterns informed the skill: Harvey, Hebbia, Robin AI, Clio, EvenUp, Casetext. Not affiliated with Anthropic or Moorcheh. ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [geoffsdesk](https://github.com/geoffsdesk) - **Source:** [geoffsdesk/memanto-mcp](https://github.com/geoffsdesk/memanto-mcp) - **License:** MIT - **Homepage:** https://github.com/geoffsdesk/memanto-mcp Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-geoffsdesk-memanto-mcp - Seller: https://agentstack.voostack.com/s/geoffsdesk - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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