A
licenseA
qualityD
maintenanceEnables version-controlled golden dataset management and semantic evaluation for RAG/LLM pipelines using TF-IDF cosine similarity, without requiring an LLM API key.
9
MIT
ChunkTuner
Related Servers
Alternatives to ChunkTuner
No user-submitted related servers found.
Related Servers
io.github.HZYAI/ragscoreofficial
AlicenseNot gradedqualityCmaintenanceEnables generating QA datasets from documents and evaluating RAG systems with detailed metrics, failure diagnosis, and visual reports through natural language.22 PyPI15Apache 2.0- FlicenseNot gradedqualityCmaintenanceEnables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.-
- AlicenseNot gradedqualityDmaintenanceEvaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.MIT
- AlicenseAqualityAmaintenanceEnables MCP clients to score retrieval quality with recall, precision, nDCG, hit rate and MRR, compare 2-3 configurations with seeded paired-bootstrap confidence intervals, audit agent tool calls against expected traces, and detect prompt regressions against golden sets. It also generates Markdown, JSON or JUnit reports with pass/fail gates, all deterministically and offline without paid API keys.6308 npm1MIT
- AlicenseNot gradedqualityCmaintenanceEnables benchmarking of local LLM models (performance and quality) and sharing results to a public leaderboard via MCP tools.9 npm8Apache 2.0
TDQS
B3.2/5.0
Scored across 4 tools
Disambiguation5/5
Each tool has a distinct purpose: evaluating, listing, previewing, and recommending. No overlap in functionality.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern (evaluate_chunking, list_strategies, preview_chunks, recommend_config).
Tool Count5/5
Four tools is appropriate for a chunking tuner server, covering the core workflow without being too sparse or overwhelming.
Completeness4/5
The set covers listing, previewing, evaluating, and recommending chunking configurations. A minor gap is the lack of explicit strategy management, but it may be predefined.
Maintenance
ActivityInactive
ResponsivenessNo issues