eve-mcp
One Memory.
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What it can do
- Memory Store: Store a piece of knowledge into memory. Args: text: The exact text content to remember (required for semantic/episodic/auto, optional for preference store) source: The source origin of t
- Memory Search: Search memory using a natural language query. Args: query: The natural language question or keywords source_agent: Agent/tool calling this API (audit trail). limit: Max number of result
- Memory Get Preferences: Get stored preferences for a context. Use at session start to load behavioral preferences. Args: source_agent: Agent/tool calling this API (audit trail). Default "unknown". con
What data it sees
Do you need an account
No: the server works without sign-in
One Memory. Every Agent. Eve gives teams one managed memory layer across Claude, Gemini, MCP clients, and custom tooling. The product centers on tenant-safe operations, operator controls, and a visible four-store architecture instead of vague “AI memory” promises.
Server tool list (14)
Raw names from tools/list. Only developers need these.
| memory_store | Store a piece of knowledge into memory. Args: text: The exact text content to remember (required for semantic/episodic/auto, optional for preference store) source: The source origin of the memory (required for semantic/episodic/auto, optional for preference store) source_agent: Agent/tool calling this API (audit trail). Default "unknown". store: Target memory store — "auto" (default), "semantic", "episodic", "preference", or "learned_rules" context: Context scope (personal, naya, es) — default "personal" metadata: Optional dictionary with extra context entity_refs: Optional list of related entities category: Category for preference store (e.g. 'architecture') key: Key for preference store value: Value for preference store confidence: Confidence score for preference store (0.0–1.0, default 1.0) |
| memory_search | Search memory using a natural language query. Args: query: The natural language question or keywords source_agent: Agent/tool calling this API (audit trail). limit: Max number of results (default 10) min_similarity: Minimum confidence threshold (0.0-1.0) store: Memory store to search — "semantic" (default), "episodic", "learned_rules", "preference", or "all" merge_policy: Cross-store merge policy when store="all": interleave, weighted_rrf, or calibrated_fusion. context: Context scope (personal, naya, es). Defaults to "all", meaning omitted context is unscoped cross-context search. Use explicit "personal" to keep preference reads scoped. visibility: Visibility scope ("PERSONAL" or "SHARED"). Defaults to PERSONAL. category: Filter by memory category (e.g. "architecture", "security"). Use "all" or omit to search all categories. |
| memory_get_preferences | Get stored preferences for a context. Use at session start to load behavioral preferences. Args: source_agent: Agent/tool calling this API (audit trail). Default "unknown". context: Context scope (personal, naya, es) category: Optional category filter (e.g., 'architecture', 'communication_style') scope: Preference scope (user or tenant) |
| memory_pre_compaction | Save a list of critical facts as episodic entries before context compaction. Use when you have specific facts to preserve verbatim. Blocking — waits for write confirmation. Args: session_id: Current session UUID critical_facts: List of critical facts that must survive compaction source_agent: Agent/tool calling this API (audit trail). Default "unknown". context: Context scope (personal, naya, es) tool_name: Source tool name |
| memory_session_start | Log the beginning of a conversation or work session. Args: summary: Brief description of the session goal source_agent: Agent/tool calling this API (audit trail). Default "unknown". session_id: Optional UUID. Auto-generated if omitted. context: Context scope (personal, naya, es) — default "personal" details: Optional dictionary of context |
| memory_session_end | Log the end of a session. Automatically triggers the AI learning and reflexion pipeline. Args: summary: Brief description of the outcome (success/failure) source_agent: Agent/tool calling this API (audit trail). Default "unknown". session_id: Optional UUID matching the session_start context: Context scope (personal, naya, es) — default "personal" details: Optional dictionary of context status: Session outcome — "success", "failure", or "unknown" (optional) |
| memory_extract | Extract memorable facts, preferences, and events from transcript text. Batch-processes transcript using AI classification. Optionally auto-stores extracted items with dedup checking. Args: transcript: Raw transcript text to extract memories from source: Source identifier (e.g., claude_code, gemini_cli) source_agent: Agent/tool calling this API (audit trail). Default "unknown". session_id: Optional session UUID for provenance tracking auto_store: If true, automatically store extracted items (with dedup) context: Context scope (personal, naya, es) -- default "personal" min_importance: Only extract items at or above this importance (1-10, default 4) use_extraction: If true, use atomic fact extraction (produces SPO fields) instead of classifier |
| memory_ingest | Start a batch ingestion job from a conversation export file. Triggers asynchronous processing of an export file (ChatGPT, Claude, Gemini, etc.) through the ingestion pipeline. Args: file_path: Absolute path to the export file source_agent: Agent calling this tool (audit trail) source_type_hint: Override format auto-detection (chatgpt|claude_code|claude_desktop|gemini) source_priority: Priority for stored claims (1=bulk, 2=inferred, 4=tool, 5=explicit) batch_size: Number of turns per extraction batch (default 100) dry_run: If True, count facts without writing to memory predicate_allowlist: If set, only store claims with predicates in this list Returns: JSON with job_id and status=queued |
| memory_ingest_status | Poll the status of a running ingestion job. Args: job_id: The job ID returned from memory_ingest source_agent: Agent calling this tool (audit trail) Returns: JSON with job_id, status, total, processed, failed, errors |
| memory_feedback | Record consumer feedback on a retrieved memory chunk. Args: chunk_id: UUID of the semantic chunk being rated outcome: Feedback outcome — "helpful", "not_helpful", or "correction" source_agent: Agent/tool calling this API (audit trail). Default "unknown". context: Optional query or context text for this retrieval correction_text: Corrected text when outcome="correction" |
| memory_forget | Soft-delete (retract) a semantic memory chunk by its chunk_id. Sets retracted_at and valid_to so the chunk is excluded from future searches but preserved for audit purposes. Args: chunk_id: UUID of the semantic chunk to forget source_agent: Agent/tool calling this API (audit trail). Default "unknown". |
| memory_update | Update an existing memory. If text changes, facts are re-extracted. Only provided fields are updated; None fields keep existing values. Args: chunk_id: UUID of the semantic chunk to update text: New text content (triggers re-embedding and claim re-extraction if changed) metadata: New metadata dictionary (replaces existing metadata entirely) entity_refs: New entity references list (replaces existing refs entirely) source_agent: Agent/tool calling this API (audit trail). Default "unknown". |
| memory_pre_compact | Distill raw conversation messages into memories using AI extraction before context compaction. Use when you have raw conversation text to process. Extracts decisions, preferences, and learned patterns via Gemini Flash. Applies per-session (max 3) and per-agent-hour (max 30) rate limits. Skips if the same session+content hash was already processed. Args: session_id: Current session UUID messages: List of {role, content} dicts (user/assistant/system) source_agent: Agent/tool calling this API (audit trail). Default "unknown". context: Context scope (personal, naya, es) — default "personal" |
| memory_ingest_url | Ingest a web URL into memory. Fetches the web page, extracts clean content, chunks it, and stores each chunk with trust_level: "external" metadata. Full SSRF protection applied. Args: url: HTTPS URL to ingest (HTTP/other schemes blocked for security) source_agent: Agent calling this tool (audit trail) context: Context scope (personal, naya, es) — default "personal" importance: Override importance score (1-10) for all extracted chunks metadata: Optional extra metadata to attach to each chunk entity_refs: Optional list of related entities Returns: JSON with status, chunk_ids, title, word_count, url |