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ai-memory-mcp

by bargisula
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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    A3.8/5.0

    Scored across 8 tools

    Disambiguation4/5

    The retrieval tools (search_memory, search_memory_semantic, get_handoff, recent) overlap in that they all return memories, but each has a clearly described purpose: keyword search, semantic search, handoff-specific retrieval, and recency-based listing. read_memory is distinct because it loads one exact memory by path. Some confusion is possible between the two search tools, but the descriptions draw a clear boundary.

    Naming Consistency4/5

    Most tools follow a verb_object pattern: record_memory, read_memory, search_memory, get_handoff, list_projects, audit_usage. search_memory_semantic adds a modifier and 'recent' breaks the pattern by being a bare adjective rather than verb_noun. Overall the convention is consistent enough to be predictable.

    Tool Count5/5

    Eight tools is well-scoped for a personal/agent memory server. Each tool covers a distinct aspect: writing, reading, searching, semantic search, handoff retrieval, recent activity, project listing, and audit. No tool feels redundant or bloated.

    Completeness4/5

    The server covers the core memory lifecycle well: recording, reading, searching, retrieving handoffs, and auditing usage. The main gaps are the lack of update/delete operations and no way to browse tags, though these may be intentional for an append-only memory store. Agents can complete typical workflows without hitting dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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