Skip to main content
Glama
smat-dev

Jinni: Bring Your Project Into Context

by smat-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to Jinni: Bring Your Project Into Context

No user-submitted related servers found.

    Related Servers

    • Solumbe

      BASHBOP
      A
      license
      A
      quality
      A
      maintenance
      Local-first code context CLI + MCP server: discover, index, and search repos; generate task-aware context packs, code maps, agent harnesses, and PR-review/merge-readiness gates for coding agents and reviewers.
      4
      14
      297 npm
      2
      MIT

    • MCP Prompt Enhancer

      soniankur948
      F
      license
      Not graded
      quality
      D
      maintenance
      Intelligently analyzes codebases to enhance LLM prompts with relevant context, featuring adaptive context management and task detection to produce higher quality AI responses.
      2
      -

    • JYMP MCP Server

      mahendra189
      A
      license
      Not graded
      quality
      D
      maintenance
      Combines codebase files into a single prompt for AI assistants, enabling code review, documentation, and debugging via natural language.
      7 npm
      2
      MIT

    • claude-init

      horiastanxd
      A
      license
      A
      quality
      C
      maintenance
      Generates AI context files (CLAUDE.md, AGENTS.md, Cursor/Windsurf/Cline/Continue/Kilo Code/Trae rules, GEMINI.md, Copilot, Aider, Junie, Warp) for any repository. Runs as CLI or MCP server, 100% local.
      3
      14 npm
      1
      MIT

    • code-search-mcp

      genautkin
      A
      license
      A
      quality
      B
      maintenance
      Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.
      4
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have completely distinct purposes: 'read_context' is for reading project files and directories, while 'usage' is for retrieving documentation. There is no overlap or ambiguity between them; an agent would never confuse one for the other.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, 'read_context' follows a verb_noun pattern, while 'usage' is a noun only, representing a minor deviation from a fully uniform convention.

    Tool Count2/5

    With only 2 tools, the server feels thin for its purpose of bringing projects into context. While 'read_context' is core, there are likely missing operations like updating context, managing rules, or querying context metadata, making the set under-scoped.

    Completeness2/5

    The tool surface is severely incomplete for the domain of project context management. It only supports reading context and accessing documentation, lacking essential operations such as writing/modifying context, listing available contexts, or configuring rules beyond defaults, which will limit agent capabilities.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources