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Anonymous NDA risk analysis for AI agents. $9 per report. No signup, no data retention.
Anonymous NDA risk analysis for AI agents. $9 per report. No signup, no data retention.
Remote endpoints: streamable-http: https://nda-mcp-production.up.railway.app/mcp
Valid MCP server (1 strong, 4 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Open access · No issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
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Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-valtirman-ndasentry": {
"url": "https://nda-mcp-production.up.railway.app/mcp"
}
}
}From the project's GitHub README.
Anonymous NDA risk analysis for AI agents. $9 per report. No signup, no account, no data retention.
NDASentry exposes a multi-stage NDA risk analysis pipeline as MCP tools so any AI agent (Claude Desktop, Cursor, Cline, etc.) can review NDAs on behalf of its user — discover risky clauses, flag missing protections, return a structured risk report — without asking the user to leave the agent, create an account, or wait for a lawyer.
The wedge: every other legal MCP server in this space requires an account, an enterprise login, or attorney-in-the-loop review. NDASentry is designed to be the simplest path for a personal AI agent: call anonymously, pay $9 with a card, get a structured analysis in under a minute.
Two tools:
preview_nda_risk(pdf_base64, filename) — Free preview. Stages the NDA, runs cheap-stage detection (regex-based clause finding, no LLM calls), returns a clause-level summary plus a Stripe payment link. Safe to expose to anonymous agent traffic; zero LLM cost on the preview path.
get_nda_report(session_token) — Paid full report. Polls payment status, then runs the full multi-stage LLM pipeline (qualifier, detector, scorer, critic, synthesizer, decision policy) and returns structured JSON with clause-level risk findings, aggressive-clause signals, missing protections, a critique, and a recommended action.
The full pipeline output is designed for agent consumption — flat structured JSON, every clause carries risk level, evidence, and reasoning, so agents can filter, summarize, or route based on what their user cares about.
Point your MCP client at the public hosted endpoint:
https://nda-mcp-production.up.railway.app/mcp
Add to claude_desktop_config.json:
{
"mcpServers": {
"ndasentry": {
"url": "https://nda-mcp-production.up.railway.app/mcp",
"transport": "streamable-http"
}
}
}
Restart Claude Desktop. The two tools will appear in the agent's tool catalog.
Review this NDA and tell me if there is anything I should push back on before signing. [attach NDA PDF]
The agent calls preview_nda_risk first, returns a preview plus a payment URL. You pay $9 in your browser. The agent calls get_nda_report and returns the full analysis.
For users who want their own instance, or local development:
git clone https://github.com/valtirman/ndasentry-mcp.git
cd ndasentry-mcp/mcp_server
python -m venv .venv-mcp
source .venv-mcp/bin/activate
pip install -r requirements.txt
export NDASENTRY_BACKEND_URL=https://ndasentry.ai
python -m mcp_server.server
The server listens on http://localhost:1966/mcp by default. Point your MCP client at it:
{
"mcpServers": {
"ndasentry": {
"url": "http://localhost:1966/mcp",
"transport": "streamable-http"
}
}
}
| Env var | Default | Purpose |
|---|---|---|
| NDASENTRY_BACKEND_URL | http://localhost:8001 | Backend API that runs the analysis pipeline |
| PORT | 1966 | Port the MCP server binds to |
| MCP_ALLOWED_HOSTS | (empty) | Comma-separated production hosts to add to DNS rebinding allowlist |
| MCP_ALLOWED_ORIGINS | (empty) | Comma-separated production origins to add to DNS rebinding allowlist |
The backend (ndasentry.ai by default for the hosted version) handles document analysis and Stripe payment verification. The MCP server is a thin protocol adapter that does not reimplement any pipeline logic. The same backend serves the web product at https://ndasentry.ai.
Input:
Output: JSON with session_token, payment_url (Stripe link with the session token bound as client_reference_id), clause_summary, missing_required_clauses, a labeled sample clause showing the shape of a paid analysis, and a disclaimer.
Input:
Output: Full structured AnalysisReport JSON with clauses, risk scores, critique, completeness, qualification, aggressive-clause signals, recommended action, and disclaimer. If payment is not yet complete, returns a polling status response instead.
NDASentry is a contract risk screening tool, not a law firm. Output is not legal advice and does not create an attorney-client relationship. For binding legal interpretation or high-stakes decisions, consult licensed counsel.
The hosted backend at ndasentry.ai is designed around an "evaporates" model:
Built by Val Tirman at FrontRange Mountain AI LLC. Solo indie effort. The web product runs at https://ndasentry.ai; this MCP server is the agent-facing channel on the same backend.
If you build something with NDASentry MCP, drop a note: frontrangesupport@gmail.com. Genuinely interested in what agents do with this.
MIT — see LICENSE file in the repository root.
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