F
license-
qualityB
maintenanceDemo MCP server that exposes order and customer data as read-only tools for AI assistants, simulating a business API or internal data source.
VendorOps MCP
by jacwalste
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., "@VendorOps MCPsearch for vendors in the IT category"
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.
VendorOps MCP
VendorOps is a Model Context Protocol (MCP) server for a synthetic internal vendor-management workflow. It gives AI applications structured access to vendor records, contracts, spending, renewals, and security reviews.
The project is intentionally built in small, reviewable slices. Phase 1 uses Python, the official MCP Python SDK, SQLite, Pydantic, and pytest to expose read-only vendor data over a local stdio connection.
All vendor and contract data in this repository is synthetic.
Development
Install the locked dependencies:
uv syncRun the test suite:
uv run pytestRun the local stdio server:
uv run vendorops-mcpThe server waits for an MCP host to send protocol messages over stdin. It does not open a network port or print a user interface.
By default, VendorOps creates data/vendorops.db and seeds it with 30 synthetic
vendors. Set VENDOROPS_DB_PATH to use a different SQLite file.
Related MCP server: SupliiChain MCP Sandbox
Current MCP Capabilities
Tools
search_vendors: Search vendor records by name, category, or owner, with optional category and status filters.
Maintenance
–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
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- Alicense-qualityAmaintenanceThis MCP server provides a local-first control plane for coding agents, enabling task routing, contract management, and shared learning across multiple AI vendors via markdown files.5MIT
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