Click on "Install 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., "@NetDocuments MCP Serversearch for Q4 financial reports in the legal department cabinet"
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.
NetDocuments MCP (Draft)
Minimal remote MCP server for ChatGPT connectors that lets users search and fetch NetDocuments files via per-user OAuth (Authorization Code + PKCE). Binary files are downloaded and converted to plaintext (PDF/DOCX/TXT best effort) and returned to the model.
What this is
An MCP server exposing only two tools:
search(query: string)→{ results: [{ id, title, text, url }] }fetch(id: string)→{ id, title, text, url, metadata }
Built for ChatGPT Connectors (Deep Research & chat).
Uses Authorization Code + PKCE with scope=read.
Connectors do not upload the original binary like drag-and-drop; they consume returned text and urls.
Related MCP server: Fetch MCP Server
Quickstart (Replit or local)
Install deps
Create from
.env.exampleand fill in:ND_CLIENT_ID=...ND_CLIENT_SECRET=...ND_REDIRECT_URI=https://<your-replit-host>.repl.co/oauth/callback(Optional) adjust
SEARCH_DEFAULT_TOP,MAX_FETCH_CHARS, region URLs.
Authorize once (saves tokens to
tokens.json):
Run the MCP server (SSE):
Confirm your repl URL ends with /sse/ (FastMCP exposes this automatically).
Connect in ChatGPT → Settings → Connectors
Add your/sse/URL, allowsearchandfetch(approval: never). Test a query.
Tool behavior
search(query: string)
Accepts a single string (per MCP spec), but also supports an inline mini-language to pass ND params:
cabinetId:<id>— Cabinet to search (if omitted, uses the first available cabinet for the user).top:<n>— Page size (default 50; ND allows up to 500).orderby:<relevance|lastMod>— Sort order (desc).select:<standardAttributes>— Returned fields set.
Remaining words are treated as the full-text parameter (pass ND query syntax directly).
Returns up to
topresults, each with{id, title, text (snippet), url}.
fetch(id: string)
Looks up metadata, downloads binary via
GET /v1/Document/{id}?base64=true, decodes, and extracts plaintext:PDF →
pdfminer.sixDOCX →
python-docx(toggle viaENABLE_DOCX=truein.env)TXT/CSV/JSON → decoded as text
Others → best-effort decode; if not extractable,
textis empty with metadata hint.
Truncates output over
MAX_FETCH_CHARSwithmetadata.truncated=true.
Notes & limits
Per-user OAuth: this MVP stores a single user's tokens in
tokens.json. For multi-user, add a session/token store keyed per SSE connection or user subject.Cross-cabinet search: supported by ND. If you omit
cabinetId, we attempt a cross-cabinet call. To be precise per ND docs, some scenarios require special qualifiers inq. Supply full ND syntax in your query if needed.Rate limits: the client retries on 401 by refreshing; consider backoff for 429 (future work).
Security: scope is
read. Do not log tokens. Consider encryptingtokens.jsonfor real deployments.
Environment variables
See .env.example. All values are read from .env.
ND_CLIENT_ID,ND_CLIENT_SECRET,ND_REDIRECT_URI,ND_OAUTH_SCOPEND_AUTH_AUTHORIZE_URL,ND_AUTH_TOKEN_URL,ND_API_BASE(US by default)SERVER_HOST,SERVER_PORTSEARCH_DEFAULT_TOP,SEARCH_DEFAULT_ORDER,MAX_FETCH_CHARS,ENABLE_DOCX
Development
Extend
nd_client.search(...)to expose more ND search params as needed.Consider adding
$skiptokensupport for paging if you want "Load more".Add per-connection token scoping if you expect multiple concurrent users.
License
MIT (draft)