Skip to main content
Glama
bernardleex526-png

LiDAR Harness MCP

Related Servers

Alternatives to LiDAR Harness MCP

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.
      4
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      MCP orchestrator combining code knowledge graph and context compression into a single pipeline to reduce token usage by 60-99%.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON
      18
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      A deterministic AST evidence engine that forces AI agents to debug using verified execution facts instead of pattern-matching symptoms, enabling hallucination-free debugging for MCP-compatible agents.
      8 npm
      Business Source 1.1
    • A
      license
      Not graded
      quality
      A
      maintenance
      A semantic code retrieval engine for AI agents that enables hybrid search, graph expansion, and token-aware context packing, integrating with MCP to provide precise code context to LLMs.
      9 npm
      296
      MIT
    • A
      license
      B
      quality
      A
      maintenance
      An MCP code-intelligence server for AI agents with pre-indexed AST cache, 62 MCP tools, and TOON-compressed output, enabling token-efficient code analysis and project health grading entirely locally.
      9
      246 PyPI
      52
      MIT

    TDQS

    A4/5.0

    Scored across 5 tools

    Disambiguation4/5

    Tools have distinct purposes but some overlap exists: harness_classify and harness_init both classify task complexity, which could cause confusion about which to use. The descriptions help clarify, but ambiguity remains.

    Naming Consistency5/5

    All tools follow a consistent 'harness_' prefix with descriptive, uniform naming (classify, init, pgo, reset, review), all in lowercase with underscores.

    Tool Count5/5

    5 tools is well-scoped for a harness tool. Each covers a distinct phase: pre-work classification, initialization, incremental verification, reset, and periodic review.

    Completeness4/5

    The tool surface covers the core workflow but is missing a status/list tool to show current baselines or state, which could be useful. Still, major operations are covered.

    Maintenance

    ActivitySlowing
    ResponsivenessSyncing