Gnosem
Cross-vendor AI memory over MCP. One semantic store, readable and writeable from every MCP client.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"gnosem": {
"url": "https://gnosem.dev/mcp"
}
}
}Remote-Endpunkte
https://gnosem.dev/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (6)
🟡memory_write(content, tags, written_by, session_id, no_optimize, ...)
Save a fact, preference, decision, or note to the user's cross-model memory. Any MCP client can read this back later. Include written_by (e.g. 'claude-code', 'gpt-5', 'kimi-k2') for provenance and session_id to group related writes. Long content (>400 chars) is automatically compressed on write to a structured-facts form optimized for LLM reading — the raw text is preserved. Pass no_optimize:true to skip. Writes are deduped by default: (1) SHA-256 of trim(content) short-circuits byte-identical writes with { id, exact_duplicate:true } for free (no embed call); (2) failing that, semantic dedup returns { id, deduped:true, matched_score } when cosine ≥ 0.85. Pass force:true to bypass both, or use memory_supersede to explicitly correct a prior memory.
Eingabe-Schema
{
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "The fact or note to remember. Plain text, max 8000 characters."
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional short labels for filtering (e.g. ['preference','stack'])."
},
"written_by": {
"type": "string",
"description": "Identifier of the model / client writing this (e.g. 'claude-code', 'gpt-5', 'kimi-k2', 'manual')."
},
"session_id": {
"type": "string",
"description": "Opaque identifier grouping related writes from the same conversation."
},
"no_optimize": {
"type": "boolean",
"description": "Skip AI compression of long content. Default false."
},
"force": {
"type": "boolean",
"description": "Bypass semantic dedup and write anyway. Default false."
}
},
"required": [
"content"
]
}🟢memory_search(query, k, mode, raw, tags, ...)
Search the user's memories. Default mode is 'hybrid': blends semantic (cosine over Vectorize) and keyword (BM25 over SQLite FTS5) hits via Reciprocal Rank Fusion (k=60). Semantic catches paraphrases; keyword catches exact-string hits (IDs, dates, code snippets). Pass mode:'semantic' or mode:'keyword' to run just one. Content defaults to the LLM-optimized (compressed) form when available (raw:true to invert). Excludes forgotten + superseded. Optional filters narrow after retrieval: tags (AND), written_by, session_id, and/or since/until (ms epoch).
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query. Interpreted as natural language for semantic mode and as FTS5-safe text for keyword mode."
},
"k": {
"type": "integer",
"description": "Max results (1–50). Default 10."
},
"mode": {
"type": "string",
"enum": [
"semantic",
"keyword",
"hybrid"
],
"description": "Retrieval mode. Default 'hybrid'."
},
"raw": {
"type": "boolean",
"description": "Return original prose instead of the compressed form. Default false."
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"description": "Only return memories containing ALL of these tags (AND semantics)."
},
"written_by": {
"type": "string",
"description": "Only return memories with an exact written_by match (e.g. 'claude-code')."
},
"session_id": {
"type": "string",
"description": "Only return memories with an exact session_id match."
},
"since": {
"type": "integer",
"description": "Only return memories created at or after this ms-epoch timestamp."
},
"until": {
"type": "integer",
"description": "Only return memories created strictly before this ms-epoch timestamp."
}
},
"required": [
"query"
]
}🟢memory_list(limit, cursor, raw, tags, written_by, ...)
List the user's most recent memories in reverse chronological order. Use for browsing or catching up on what the user's other model sessions have written recently. Same content/content_raw shape as memory_search. Optional filters (tags, written_by, session_id, since, until) narrow the listing at the SQL level.
Eingabe-Schema
{
"type": "object",
"properties": {
"limit": {
"type": "integer",
"description": "Max rows to return (1–200). Default 50."
},
"cursor": {
"type": "integer",
"description": "Pagination cursor from a previous call's `cursor` field (ms epoch); returns rows older than this timestamp."
},
"raw": {
"type": "boolean",
"description": "Return original prose instead of the compressed form. Default false."
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"description": "Only return memories containing ALL of these tags (AND semantics)."
},
"written_by": {
"type": "string",
"description": "Only return memories with an exact written_by match."
},
"session_id": {
"type": "string",
"description": "Only return memories with an exact session_id match."
},
"since": {
"type": "integer",
"description": "Only return memories created at or after this ms-epoch timestamp."
},
"until": {
"type": "integer",
"description": "Only return memories created strictly before this ms-epoch timestamp."
}
}
}🔴memory_forget(id)
Soft-delete a memory by id. The row is retained for audit but excluded from search/list and removed from the vector index.
Eingabe-Schema
{
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "UUID of the memory to forget."
}
},
"required": [
"id"
]
}⚪memory_supersede(old_id, new_content, tags, written_by, session_id)
Replace a stale memory with a corrected one. The old row is marked superseded and excluded from future reads; the new row becomes the current version. Use for corrections; use memory_forget for pure deletions.
Eingabe-Schema
{
"type": "object",
"properties": {
"old_id": {
"type": "string",
"description": "UUID of the memory to replace."
},
"new_content": {
"type": "string",
"description": "New content that supersedes the old memory."
},
"tags": {
"type": "array",
"items": {
"type": "string"
}
},
"written_by": {
"type": "string"
},
"session_id": {
"type": "string"
}
},
"required": [
"old_id",
"new_content"
]
}🟡memory_write_bulk(memories)
Write up to 50 memories in a single call. Each entry runs the same path as memory_write (semantic dedup by default; pass force:true per-entry to skip). Embeddings + optimizations run in parallel; D1 inserts are batched. Returns { results: [...] } with one entry per input in the same order — each is { id, created_at, optimized? } on success, { id, created_at, deduped, matched_score } on dedup, or { error } on failure. Free-tier limits apply to the sum: if adding N would exceed 200, the first (200 - existing) succeed and the rest return an error.
Eingabe-Schema
{
"type": "object",
"properties": {
"memories": {
"type": "array",
"description": "Array of memory-write entries (1–50).",
"minItems": 1,
"maxItems": 50,
"items": {
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "The fact or note to remember. Plain text, max 8000 characters."
},
"tags": {
"type": "array",
"items": {
"type": "string"
}
},
"written_by": {
"type": "string"
},
"session_id": {
"type": "string"
},
"no_optimize": {
"type": "boolean",
"description": "Skip AI compression of long content. Default false."
},
"force": {
"type": "boolean",
"description": "Bypass semantic dedup for this entry. Default false."
}
},
"required": [
"content"
]
}
}
},
"required": [
"memories"
]
}Community
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