File AI
File AI is a read-only document awareness server that lets agents understand local file contents without parsing file internals.
Analyze documents (
doc_analyze): Parse a local file into a structured index containing a profile, anchors, outline, blocks, and chunks — with optional chunking configuration (size, overlap, max text length per anchor).Read document content (
doc_read): Retrieve specific blocks, chunks, or anchors from a cached document index by index ID, block ID, chunk ID, or anchor ID.Search document content (
doc_search): Run keyword searches across cached document blocks, returning excerpts tied to stable anchors, with optional case sensitivity, result limits, and context character controls.Retrieve contextual windows (
doc_context): Fetch nearby blocks surrounding a specific anchor or query match, with configurable before/after block counts.List supported formats (
doc_list_formats): View all 206+ formats recognized by the Flyfish File Viewer core, their renderer capabilities, and File AI's extraction coverage.Access document resources: Directly access a document's profile (
doc://{indexId}/profile), outline (doc://{indexId}/outline), and chunks (doc://{indexId}/chunks) as MCP resources.Wide format support: Full structured extraction for PDF, DOCX, XLSX/CSV, PPTX, EML, Markdown, JSON, code, and archive manifests; best-effort metadata for CAD, 3D, media, image, geospatial, and other formats.
Anchor-based citations: Every content block carries a stable
anchorId(page, slide, worksheet, row range, etc.) for grounded document citations.Read-only: Original source documents are never modified.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@File AI Analyze the quarterly report.docx and show its outline"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
File AI
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
Install
Use it directly with npx:
npx -y @flyfish-dev/file-aiOr install it globally:
npm install -g @flyfish-dev/file-ai
file-ai --transport stdioRelated MCP server: RAG Document Server
MCP Client Config
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 8765Endpoint:
http://127.0.0.1:8765/mcpTools
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.
Resources
doc://{indexId}/profiledoc://{indexId}/outlinedoc://{indexId}/chunks
Supported Files
File 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.
Development
pnpm install
pnpm build
pnpm test
pnpm validate:skillRun locally:
pnpm dev -- --transport stdio
pnpm dev -- --transport http --port 8765Publishing
The package includes:
npm metadata for
@flyfish-dev/file-aiMCP Registry metadata in
server.jsonGitHub Actions workflow
.github/workflows/publish-mcp.yml
Release flow:
git tag v0.1.0
git push origin v0.1.0The 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.
Available Tools
5 toolsdoc_analyzeAnalyze documentB
Parse a local document into a read-only profile, anchors, outline, blocks, and chunks for agent context.
Inputs: path (required), refresh, maxTextLength, chunkSize, +1 more.
doc_contextRead contextual document windowC
Return nearby blocks around an anchor or the first anchors matching a query.
Inputs: indexId, path, anchorId, query, +3 more.
doc_list_formatsList supported formatsB
List Flyfish File Viewer known formats and document-engine extractors.
Inputs: no named parameters.
doc_readRead document index contentB
Read blocks, chunks, or anchors from a cached index without inspecting source document internals.
Inputs: indexId, path, anchorId, blockId, +2 more.
doc_searchSearch document contentC
Search document blocks and return excerpts tied to stable anchors.
Inputs: query (required), indexId, path, limit, +2 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: analyze parses documents, context retrieves nearby blocks, list_formats lists supported formats, read reads blocks, and search searches. No overlapping functionality.
All tools follow a consistent 'doc_verb' pattern (doc_analyze, doc_context, doc_list_formats, doc_read, doc_search), making the naming predictable and easy to understand.
With 5 tools, the server is well-scoped for a document analysis agent. While slightly minimal, each tool earns its place and the count is reasonable for the domain.
The tool surface covers core operations: parsing, reading, searching, and context retrieval. Missing features like document listing or updates, but for a read-only analysis server, it is largely complete.
Maintenance
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Query your own PDFs and documents from any MCP client. Every answer cites the page it came from.
MCP server for agentverse documentation, generated by doc2mcp.
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