by offorte
Offorte streamlines sales with proposal creation, contact management, and CRM customer management for efficient client r
Connects AI agents to Offorte's proposal management platform to automatically create and send business proposals. Enables voice-controlled and AI-powered proposal workflows.
Offorte is an official MCP server published by offorte that provides AI assistants with tools and capabilities via the Model Context Protocol. Offorte streamlines sales with proposal creation, contact management, and CRM customer management for efficient client r It is categorized under productivity, developer tools.
You can install Offorte in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Offorte is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Offorte has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Offorte reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Offorte into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Offorte surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated Offorte against two servers with overlapping tools; this profile had the clearer scope statement.
Offorte is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Offorte is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Offorte for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Offorte benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Offorte is the kind of server we cite when onboarding engineers to host + tool permissions.
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MCP server for Offorte - Create & send proposals using AI.
This server acts as the bridge between AI agents and Offorte's proposal engine. It enables external models to create and send proposals via Offorte. Built for automation workflows, the MCP makes it easy to integrate proposal actions into AI tools, chat interfaces, and autonomous systems.
Early technology Please note that MCP (Model Context Protocol) is a new approach to AI integration. While powerful, it's still evolving and may occasionally produce unexpected or undesired results.
Offorte is automated proposal software which helps businesses create, send, and track beautiful interactive proposals. Built for speed, flexibility, and real-world use, it combines automation and smart workflows without sacrificing the personal touch. Learn more about Offorte.
The MCP server is using the Offorte Public API v2. Read the API documentation for more information.
Experience the future of proposals: voice-triggered, AI-powered, fully automated. See how Offorte connects voice and workflow in this real demo.
The goal of this project is too create & send proposals using AI via the protocol. Because of the experimental character, the full Offorte API is not covered and its limited to the tools needed to create & send proposals. Tools which could lead to an LLM updating and deleting stuff are not yet implemented. Update and delete commands might be added in the future, based on reallife results & user feedback.
20.x.x)TRANSPORT_TYPE (optional): Set to sse to enable Server-Sent Events (SSE) mode, or leave unset/default for stdio (default: stdio).Currently, this MCP server has only been tested with Claude Desktop. More client examples will be added in the future.
Find your claude_desktop_config.json at Claude > Settings > Developer > Edit Config and depending on which option you'd like, add JUST ONE of the following:
Running it straight from the npm registry.
{
"mcpServers": {
"offorte-proposals": {
"command": "npx",
"args": ["-y", "@offorte/mcp-server"],
"env": {
"OFFORTE_ACCOUNT_NAME": "<YOUR_ACCOUNT_NAME>",
"OFFORTE_API_KEY": "<YOUR_TOKEN>"
}
}
}
}
Dependencies should have been installed & the project is build before you use this method (pnpm install).
{
"mcpServers": {
"offorte-proposals": {
"command": "node",
"args": ["/path/to/directory/offorte-mcp-server/dist/server.js"],
"env": {
"OFFORTE_ACCOUNT_NAME": "<YOUR_ACCOUNT_NAME>",
"OFFORTE_API_KEY": "<YOUR_TOKEN>"
}
}
}
}
To get started, clone the repository and install the dependencies. Make sure you have an .env file and it includes the proper values.
git clone https://github.com/offorte/offorte-mcp-server.git
cd offorte-mcp-server
pnpm install
pnpm dev
Check the NPM scripts for all commands, below is a short summary of the most important onces.
| Script | Info |
|---|---|
pnpm build | Build the project for production |
pnpm start | Start the production server |
pnpm dev | Start the development server |
pnpm inspect | Inspect the server |
pnpm format | Format code using Prettier and ESLint |
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.