Governed MCP gateway
Teams connect AI clients to one managed endpoint instead of asking every person to wire many MCP servers by hand.
About the company
About ThisMCP™
ThisMCP™ is a startup focused on making the Model Context Protocol manageable and secure for teams. It gives organizations a governed gateway between the AI clients people choose and the MCP servers those clients need to reach.
What It Is
An identity, policy, and gateway layer for MCP.
ThisMCP™ is not another MCP server. It sits between AI clients such as ChatGPT, Claude, IDE agents, workflow agents, or custom agents and the MCP servers they access.
The point is to let a team standardize the environment without forcing everyone into the same assistant. People can keep the agent that fits their work, while the organization controls discovery, authorization, approvals, routing, and audit records before requests reach upstream servers.
A control layer for MCP
Not the server. The governed path to the servers.
- 1AI clientsChatGPT, Claude, IDE agents, workflow agents, and custom agents.
- 2ThisMCPIdentity, policy, approvals, budgets, capsules, routing, and audit evidence.
- 3MCP serversThe tools, prompts, resources, APIs, and private systems teams already need.
Core Ideas
Govern the MCP surface before the model can use it.
Discovery, calls, responses, and approval gates are handled at the gateway boundary, where policy can be enforced consistently.
Policy at the boundary
Visibility and runtime calls can be shaped by user, group, project, budget, approval rule, and the capability being requested.
Choice without drift
Approved tools, prompts, and resources can travel across ChatGPT, Claude, IDE agents, workflow agents, and custom agents.
Evidence after the call
Approvals, task capsules, usage records, and audit logs give teams a durable record of what agents did with tools.
Why It Exists
MCP gets powerful quickly. Teams still need control.
- Every developer keeping a different MCP configuration
- Sensitive tools showing up in the wrong agent or project
- Credentials copied into local files, prompts, or shared chats
- Policies that depend on the model choosing to follow instructions
- No clear usage trail for tool calls, approvals, and results