Agent skill

mcp-local-rag

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/mcp-local-rag

SKILL.md

MCP Local RAG Skills

Tools

Tool Use When
ingest_file Local files (PDF, DOCX, TXT, MD)
ingest_data Raw content (HTML, text) with source URL
query_documents Semantic + keyword hybrid search
delete_file / list_files / status Management

Search: Core Rules

Hybrid search combines vector (semantic) and keyword (BM25).

Score Interpretation

Lower = better match. Use this to filter noise.

Score Action
< 0.3 Use directly
0.3-0.5 Include if mentions same concept/entity
> 0.5 Skip unless no better results

Limit Selection

Intent Limit
Specific answer (function, error) 5
General understanding 10
Comprehensive survey 20

Query Formulation

Situation Why Transform Action
Specific term mentioned Keyword search needs exact match KEEP term
Vague query Vector search needs semantic signal ADD context
Error stack or code block Long text dilutes relevance EXTRACT core keywords
Multiple distinct topics Single query conflates results SPLIT queries
Few/poor results Term mismatch EXPAND (see below)

Query Expansion

When results are few or all score > 0.5, expand query terms:

  • Keep original term first, add 2-4 variants
  • Types: synonyms, abbreviations, related terms, word forms
  • Example: "config" → "config configuration settings configure"

Avoid over-expansion (causes topic drift).

Result Selection

When to include vs skip—based on answer quality, not just score.

INCLUDE if:

  • Directly answers the question
  • Provides necessary context
  • Score < 0.5

SKIP if:

  • Same keyword, unrelated context
  • Score > 0.7
  • Mentions term without explanation

Ingestion

ingest_file

ingest_file({ filePath: "/absolute/path/to/document.pdf" })

ingest_data

ingest_data({
  content: "<html>...</html>",
  metadata: { source: "https://example.com/page", format: "html" }
})

Format selection — match the data you have:

  • HTML string → format: "html"
  • Markdown string → format: "markdown"
  • Other → format: "text"

Source format:

  • Web page → Use URL: https://example.com/page
  • Other content → Use scheme: {type}://{date} or {type}://{date}/{detail}
    • Examples: clipboard://2024-12-30, chat://2024-12-30/project-discussion

HTML source options:

  • Static page → LLM fetch
  • SPA/JS-rendered → Browser MCP
  • Auth required → Manual paste

Re-ingest same source to update. Use same source in delete_file to remove.

References

For edge cases and examples:

  • html-ingestion.md - URL normalization, SPA handling
  • query-optimization.md - Query patterns by intent
  • result-refinement.md - Contradiction resolution, chunking

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