One Config Line, One New Capability
Adding an MCP server to Claude or Cursor takes a minute. From then on the agent can read Slack, push code, or run shell commands, and no change ticket records it.
Netra inventories the MCP servers, Agent Skills, local models, and AI components on every endpoint, scores the risky capability chains they create, and lets you audit or block the tools and skills you don’t trust.
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The Challenge
MCP servers, skills, and models install in minutes, on the endpoint.
Adding an MCP server to Claude or Cursor takes a minute. From then on the agent can read Slack, push code, or run shell commands, and no change ticket records it.
An Agent Skill is a SKILL.md plus files that can run commands, read the filesystem, and call the network. A same-name copy or an edited script looks identical from outside.
Reading labelled files is routine. Network access is routine. An agent that can do both on the same device is an exfiltration path, and a tool list won’t show it.
Inventory & Control
Found from what is configured, installed, and loaded on each endpoint.
Assets › MCP shows every MCP tool configured across Claude, Cursor, ChatGPT, Codex, and Gemini, with users, devices, risk, and control state. Select tools and create a control policy: Audit only or Block, for all users or specific departments and people. MCP Activity records each call under Observe and Block.
Assets › Skills finds Agent Skills (SKILL.md) in Claude Code, Codex, and Claude Desktop, tags their capabilities (command execution, code execution, filesystem, network, data processing), and rates their risk. In whitelist mode you Allow or Revoke skills one by one, or you blacklist a skill by name or by exact build. Skill Activity logs each call, including the ones policy refused.
Assets › Models builds the local model inventory from real weight-load events in runtimes like Ollama and LM Studio. Each model shows its source registry, weights read, runtime, the endpoints and operators that loaded it, and evidence IDs. Model Loads adds the process and path behind each load, with flags such as user-scope installs.
AI BoM finds AI components that never show up as an app: npm and pip packages, VS Code and JetBrains extensions, Windows services, scheduled tasks, autorun registry keys, Ollama and LM Studio models, and WSL packages. Persistence points sit in the same list as packages and extensions. For AI apps and accounts, see Discover Shadow AI.
Investigation
The Netra AI Data Graph scores what each user’s agents can do together.
Who runs what, with which model, through which tools, and what data it touched
Agent Chains add up what each user’s agents can do across their devices: exec + network + filesystem, critical capabilities, and MIP-labelled files on the same device.
One graph of users, devices, agent channels, models, MCP servers, skills, data resources, and domains, linked by edges such as configures_mcp, has_skill, and accessed.
Trace department → MCP server → tool → outcome. The Installed vs used view shows servers nobody calls, which is attack surface you can remove.
Next Steps
The same endpoint agent and console cover data, accounts, and spend.
Keep source code and secrets out of coding-agent prompts and responses.
Learn moreSee which AI apps, agents, and accounts your workforce actually uses.
Learn moreDetect and block sensitive data in prompts, uploads, and responses.
Learn moreAttribute AI token spend to users and departments, then cap it.
Learn moreFAQ
Everything an AI agent can load or call on an employee’s device: MCP servers and their tools, Agent Skills, local models, and AI components installed through package managers, IDEs, and system services. Netra inventories each one and ties it to users and devices.
Assets › MCP lists every MCP tool configured across Claude, Cursor, ChatGPT, Codex, and Gemini, with the users and devices that have it, a risk level, and a control state of Audit only, Blocked, or Not controlled.
Yes. Create a control policy for selected MCP tools with the action Audit only or Block, for all users or specific departments or users. For Agent Skills, use whitelist mode to Allow or Revoke each skill, or blacklist a skill by name or by exact build. Endpoints quarantine a blacklisted skill on their next policy sync, usually within a minute.
Incidents › Risky Chains builds an Agent Graph of who runs what, with which model, through which tools, and what data it touched. Each chain gets a risk score from 0 to 10 and a critical, high, or medium level, so the riskiest combinations of capability and data rise to the top.
Run a proof of value on your own endpoints and see which MCP servers, skills, and models your agents can reach, and which chains need attention.
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