Server data from the Official MCP Registry
Agent-ready local document context with stable anchors.
Agent-ready local document context with stable anchors.
File AI is a well-designed, read-only document analysis MCP server with strong security fundamentals. Authentication is not required (appropriate for read-only local file processing), file access is properly scoped to user-provided paths, and dependencies are legitimate. Minor code quality observations around error handling and input validation do not materially impact security posture. Supply chain analysis found 7 known vulnerabilities in dependencies (1 critical, 3 high severity). Package verification found 1 issue.
7 files analyzed · 12 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-flyfish-dev-file-ai": {
"args": [
"-y",
"@flyfish-dev/file-ai"
],
"command": "npx"
}
}
}From the project's GitHub README.
Read-only document awareness MCP server for agents. File AI uses Flyfish File Viewer core as the source of truth for format recognition, then turns local files into a stable profile, anchors, blocks, chunks, outline, search results, and contextual snippets so an agent can understand document content without reverse-engineering PDF, OOXML, spreadsheet, email, or archive internals.
MCP Registry name: io.github.flyfish-dev/file-ai
Use it directly with npx:
npx -y @flyfish-dev/file-ai
Or install it globally:
npm install -g @flyfish-dev/file-ai
file-ai --transport stdio
Stdio:
{
"mcpServers": {
"file-ai": {
"command": "npx",
"args": ["-y", "@flyfish-dev/file-ai"]
}
}
}
Streamable HTTP:
npx -y @flyfish-dev/file-ai --transport http --host 127.0.0.1 --port 8765
Endpoint:
http://127.0.0.1:8765/mcp
doc_analyze: parse a local file and cache a document index.doc_read: read blocks, chunks, or anchors from an existing index or path.doc_search: search cached content with source anchors.doc_context: retrieve nearby blocks around an anchor or query.doc_list_formats: list File Viewer core registry formats, all supported extensions, renderer capabilities, and File AI extractor coverage.doc://{indexId}/profiledoc://{indexId}/outlinedoc://{indexId}/chunksFile AI recognizes formats through @file-viewer/core/headless, currently covering 206 registered extensions. profile.format is therefore aligned with the same renderer selection used by Flyfish File Viewer.
Content extraction is a separate layer. File AI provides structured awareness for text/code/Markdown/JSON, PDF text, DOCX, XLSX/CSV, PPTX, EML, and archive manifests. Other File Viewer renderer-only formats such as CAD, 3D, media, image, geospatial, drawing, ebook, and data assets are still recognized in profile.format; when no structured text extractor exists yet, File AI returns a profile plus best-effort metadata/text and explicit warnings in profile.warnings and profile.extraction.
Every content block carries an anchor such as a page, slide, worksheet, row range, nested path, or byte/text location. Agents should cite returned anchorId values when making document-grounded claims.
pnpm install
pnpm build
pnpm test
pnpm validate:skill
Run locally:
pnpm dev -- --transport stdio
pnpm dev -- --transport http --port 8765
The package includes:
@flyfish-dev/file-aiserver.json.github/workflows/publish-mcp.ymlRelease flow:
git tag v0.1.0
git push origin v0.1.0
The workflow publishes the npm package first, then publishes io.github.flyfish-dev/file-ai to the official MCP Registry through GitHub OIDC. The repository must have an NPM_TOKEN secret that can publish @flyfish-dev/file-ai.
The server is read-only. It does not mutate source documents.
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