Stop re-explaining yourself to every AI agent. MIE gives all your agents — Claude, ChatGPT, Cursor, Gemini — a shared, persistent knowledge graph
Stop re-explaining yourself to every AI agent. MIE gives all your agents — Claude, ChatGPT, Cursor, Gemini — a shared, persistent knowledge graph they can read and write. Decisions, context, facts, and relationships survive across. The mie mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 12 defined tools rather than through you.
Everything the assistant can do here goes through one of these:
mie_analyze — Surfaces related context before storing — the agent decides what's worth rememberingmie_store — Writes facts, decisions, entities, events, and relationships to the graphmie_bulk_store — Batch store up to 50 nodes with cross-references — ideal for importing knowledge from files or git historymie_get — Retrieve a single memory node by ID with full detailsmie_query — Semantic search, exact lookup, or graph traversal across all node typesmie_list — List and filter nodes with paginationmie_update — Invalidate outdated facts, update statuses — with full history preservedmie_delete — Remove nodes with cascade (embedding + edges) or remove individual relationshipsmie_conflicts — Detect contradictions in stored knowledgemie_export — Export the full graph as JSON or Datalogmie_repair — Rebuild HNSW indexes and clean orphaned embeddingsmie_status — Graph health, node counts, usage metricsSetup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
ollama pull nomic-embed-text MIE works without Ollama. You get exact-match search and graph traversal. Add Ollama for semantic search ("find things related to deployment" instead of exact keywords).Plenty of knowledge and memory servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Mie's toolset — mie_analyze, mie_store, mie_bulk_store and 9 more — is a fair guide to whether it matches your workflow. It is maintained by kraklabs; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| mie_analyze | Surfaces related context before storing — the agent decides what's worth remembering |
| mie_store | Writes facts, decisions, entities, events, and relationships to the graph |
| mie_bulk_store | Batch store up to 50 nodes with cross-references — ideal for importing knowledge from files or git history |
| mie_get | Retrieve a single memory node by ID with full details |
| mie_query | Semantic search, exact lookup, or graph traversal across all node types |
| mie_list | List and filter nodes with pagination |
| mie_update | Invalidate outdated facts, update statuses — with full history preserved |
| mie_delete | Remove nodes with cascade (embedding + edges) or remove individual relationships |
| mie_conflicts | Detect contradictions in stored knowledge |
| mie_export | Export the full graph as JSON or Datalog |
| mie_repair | Rebuild HNSW indexes and clean orphaned embeddings |
| mie_status | Graph health, node counts, usage metrics |
{
"mcpServers": {
"mie": {
"command": "mie",
"args": ["--mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
ollama pull nomic-embed-text MIE works without Ollama. You get exact-match search and graph traversal. Add Ollama for semantic search ("find things related to deployment" instead of exact keywords).A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.