Skip to main content
Glama
maschmann

mcp-context-memory

by maschmann
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to mcp-context-memory

No user-submitted related servers found.

    Related Servers

    • Paparats MCP

      IBazylchuk
      A
      license
      Not graded
      quality
      B
      maintenance
      Provides AI coding assistants with deep, semantic understanding of local codebases via AST-aware chunking, cross-repo symbol graphs, and architectural memory, enabling context-aware code search and dependency tracing.
      11
      MIT

    • CodeMind

      twly7ho2
      F
      license
      Not graded
      quality
      C
      maintenance
      Provides persistent codebase memory and semantic context for AI agents via AST-aware chunking and symbol graph indexing.
      1
      -

    • mcp-vector-memory

      BBerthod
      A
      license
      Not graded
      quality
      C
      maintenance
      Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.
      MIT
    Deploy Server
    Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

    TDQS

    A4/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clear, distinct purpose: index_project builds the semantic index, search_context queries it, and remember_decision stores explicit architectural notes. There is no functional overlap between these three operations.

    Naming Consistency5/5

    All tool names follow the same verb_noun pattern: index_project, search_context, remember_decision. The verbs are concise and the nouns accurately describe the target resource, making the API predictable and easy to navigate.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its stated purpose of context memory. Each tool covers a distinct fundamental operation (index, search, remember), and there is no bloat or redundancy.

    Completeness4/5

    The tool set covers the core lifecycle of context memory: ingest (index_project), query (search_context), and explicit knowledge persistence (remember_decision). Minor gaps include lack of delete/update for decisions and no re-indexing mechanism, but these are not critical for the primary workflow.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources