goodmem-mcp

name	goodmem:mcp
description	Workflow guide for GoodMem MCP server tools. Use when an agent needs to understand what GoodMem MCP tools are available and how to use them. Parameter signatures are provided by the MCP protocol itself — do not hardcode them here.
allowed-tools	Read

GoodMem MCP Tools — Workflow Guide

The GoodMem MCP server exposes tools across 10 namespaces plus 3 local utilities. Most tools map to a GoodMem REST API endpoint; the utilities (goodmem_configure, goodmem_lookup_model, goodmem_client_info) run locally without contacting the server. Use tools/list to discover exact parameter schemas — this document covers workflow and patterns only.

Setup: The MCP server needs GOODMEM_BASE_URL and GOODMEM_API_KEY. These can be set as environment variables before launch, or configured from chat via goodmem_configure.

TLS: If the server uses self-signed or private CA certificates, set NODE_EXTRA_CA_CERTS=/path/to/rootCA.pem or NODE_TLS_REJECT_UNAUTHORIZED=0 (local dev only) as an environment variable before launch.

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Typical workflow

1. Configure — call goodmem_configure if credentials weren't set via env vars.
2. Register providers — create embedder, LLM, and optionally reranker. Use goodmem_lookup_model to check the model registry before creating — it auto-infers provider, endpoint, and dimensionality for known models.
3. Create a space — a space binds an embedder and chunking config. Memories ingested into a space are automatically chunked and embedded.
4. Ingest memories — use goodmem_memories_create (single) or goodmem_memories_batch_create (bulk). Supports text, base64-encoded files, or URL references.
5. Wait for processing — after ingestion, memories are processed asynchronously. Poll with goodmem_memories_get until processingStatus is COMPLETED.
6. Retrieve — goodmem_memories_retrieve performs semantic search. Returns NDJSON with ranked results.

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Namespaces

Embedders (goodmem_embedders_*)

Register and manage embedding models. The four SaaS provider types (OpenAI, Cohere, Voyage, Jina) require credentials. OpenAI-compatible endpoints (Anthropic, Google, Mistral) also require credentials when used under the OPENAI provider type. Omitting credentials for a known SaaS endpoint throws an error before the request is sent.

CRUD: create, list, get, update, delete.

LLMs (goodmem_llms_*)

Register and manage LLM configurations. Same credential pattern as embedders.

CRUD: create, list, get, update, delete.

Rerankers (goodmem_rerankers_*)

Register and manage reranker models for result re-ranking.

CRUD: create, list, get, update, delete.

Spaces (goodmem_spaces_*)

Memory spaces — containers that bind an embedder and chunking config.

CRUD: create, list, get, update, delete.

Memories (goodmem_memories_*)

Store, retrieve, and manage memories within spaces.

• create / batch_create — ingest content (text, base64, or URL reference)
• retrieve — semantic search across one or more spaces (returns NDJSON)
• get / batch_get — fetch by ID
• list — list memories in a space
• delete / batch_delete — remove memories
• pages — list memory page images with optional filters and pagination

Not available via MCP (binary response): content, pages_image.

OCR (goodmem_ocr_*)

• document — extract text from a document using OCR

Users (goodmem_users_*)

• me — get the current authenticated user
• get — get a user by ID or email

API Keys (goodmem_apikeys_*)

Manage API keys for authentication.

CRUD: create, list, update, delete.

System (goodmem_system_*)

• info — server version and configuration
• init — first-time server initialization

Admin (goodmem_admin_*)

• drain — request the server to enter drain mode
• background_jobs_purge — purge completed background jobs
• license_reload — reload the server license

Utilities

• goodmem_configure — set base URL and API key from chat
• goodmem_lookup_model — look up model registry entries before creating embedders/LLMs/rerankers
• goodmem_client_info — return MCP client version and the goodmem server commit it was built against