MCP Local RAG Skills

Tools

MCP Tool	CLI Equivalent	Use When
`ingest_file`	`npx mcp-local-rag ingest <path>`	Local files (PDF, DOCX, TXT, MD). CLI for bulk/directory.
`ingest_data`	—	Raw content (HTML, text) with source URL
`query_documents`	`npx mcp-local-rag query <text>`	Semantic + keyword hybrid search
`delete_file`	`npx mcp-local-rag delete <path>`	Remove ingested content
`list_files`	`npx mcp-local-rag list`	File ingestion status
`status`	`npx mcp-local-rag status`	Database stats

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
0.5-0.7	Include only if directly relevant to the question
> 0.7	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

fileTitle

Each result includes fileTitle (document title extracted from content). Null when extraction fails.

Use	How
Disambiguate chunks	Use fileTitle to identify which document the chunk belongs to
Group related chunks	Same fileTitle = same document context
Deprioritize mismatches	fileTitle unrelated to query AND score > 0.5 → rank lower

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} where {type} is a short identifier for the content origin (e.g., clipboard, chat, note, meeting)

HTML source options:

Static page → HTTP fetch
SPA/JS-rendered → Browser/web tool with DOM rendering
Auth required → Manual paste

If HTTP fetch returns empty or minimal content, retry with a browser/web tool.

Source URLs are normalized: query strings and fragments are stripped. See html-ingestion.md for cases where this matters.

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

CLI commands

CLI subcommands mirror MCP tools. Useful for bulk operations, scripting, and environments without MCP.

query, list, status, delete output JSON to stdout
ingest outputs progress to stderr
Use --help on any command for options
See cli-reference.md for options and config matching

References

For edge cases and examples:

html-ingestion.md - URL normalization, SPA handling
query-optimization.md - Query patterns by intent
result-refinement.md - Synthesis vs filter strategy, contradiction resolution, chunking
cli-reference.md - CLI command options, config matching, output conventions

Search AI Tools

mcp-local-rag

Install this agent skill to your Project

SKILL.md