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ntobjmanager-mcp is a Model Context Protocol (MCP) server for Windows RPC research that keeps a PowerShell engine alive across tool calls. That persistence lets an AI agent reuse RPC clients, variables, and returned objects—such as a context handle—in later steps instead of rebuilding the investigation after every call. It helps orchestrate analysis; it does not automatically confirm that a finding is exploitable.
Why persistent state matters in Windows RPC research
A typical RPC investigation moves through several dependent steps: find an interface, inspect its stub and parameters, connect a client, make a call, then interpret the reply and decide what to try next. The project’s author described this loop as: “Find an interface, parse its stub, connect a client, send a call, read the reply, adjust.” In the author’s September 29, 2026 introduction, this was the practical limitation of generic PowerShell MCP setups: “The one thing it cannot give an AI agent is memory.” These are the author’s descriptions of the workflow, not results from a user survey. Project introduction and repository
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With a stateless, one-call-per-process setup, parsed objects, connected clients, and variables may disappear between calls. ntobjmanager-mcp keeps a PowerShell engine running so later operations can use state created earlier. In an RPC investigation, that can mean passing an object returned by one procedure to a subsequent call without reconstructing the whole session.
How the RPC workflow fits together
Built on James Forshaw’s NtObjectManager/NtCoreLib, ntobjmanager-mcp connects an AI agent to a sequence of Windows RPC research operations. The documented flow is:
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- Find interfaces: parse a PE file for RPC server interfaces, or look for endpoints and running servers.
- Inspect definitions: review interface methods and NDR parameters to understand the calls exposed by an interface.
- Connect a client: establish an RPC client connection to a discovered endpoint or server.
- Call and inspect: invoke a procedure and examine its reply, keeping useful session objects available for later calls.
The repository also documents a lab-VM bridge for executing PowerShell in a guest and starting a persistent guest listener. It says tool calls are recorded in output/mcp_audit.log. These features give an agent a connected research workflow and a call trace; they are not independent verification of the conclusions an agent draws. Repository documentation
What the documented tools cover
The project README currently describes 24 tools, grouped into a stateful RPC pipeline, a VM lab bridge, and methodology-oriented research helpers. The author’s September 2026 introduction described 22 fixed tools at that point. These are dated project descriptions of the tool set, not competing measurements.
| Area | Documented capabilities | What to take from them |
|---|---|---|
| RPC pipeline | Interface parsing, method and parameter inspection, endpoint or server discovery, client connections, and procedure calls | Supports a multi-step investigation while retaining session state. |
| Lab VM bridge | PowerShell execution in a guest VM and a persistent guest listener | Provides a documented route for lab execution; the project does not claim this makes every operation safe. |
| Research helpers | Interface inventory, context-handle scans, default-value fuzzing that is dry-run by default, checks for interfaces associated with stopped services, ETW-based unreachable-server research, interface security checks, ALPC race-capture support, and task inventory | These are research aids. A reported condition or scan result is not, by itself, proof of a vulnerability. |
The tool counts and capability descriptions are published by the project; they are not independent validation or comparative performance data. Current README · September 2026 introduction
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhat it can—and cannot—establish
ntobjmanager-mcp helps an agent inspect RPC interfaces and coordinate calls, but several boundaries matter when interpreting results:
- NDR data alone cannot prove that two context handles have distinct types. A scan can guide investigation, but it does not establish context-handle type confusion.
- The documented underlying NtObjectManager version does not support full rogue-RPC hosting.
- Symbol-resolved procedure names depend on the environment.
- PowerShell 7 is listed as untested.
- ETW tracing and some ALPC security checks require administrator rights.
These limits distinguish workflow automation from vulnerability confirmation. A researcher still needs to validate what a finding means and whether it is security-relevant. Repository limitations and methodology
Safety: RPC calls can affect real services
The project warns that rpc_call invokes real RPC methods and can crash services. Default-value fuzzing is documented as dry-run by default, but that does not make live procedure calls harmless. Use an isolated virtual machine or other controlled lab, and only test systems for which you have authorization; do not point experimental calls at a production or everyday-use host. The repository also restricts use to lawful research and authorized testing. Safety guidance
Setup and intended audience
The project is aimed at researchers investigating Windows RPC with AI agents. Its documented setup uses the NtObjectManager PowerShell module, Python dependencies, and an MCP client configured to communicate over stdio. Consult the repository for current installation steps and client configuration, since prerequisites and commands can change. Setup documentation
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In practical terms, the distinguishing feature is not a claim that the agent can independently find or prove vulnerabilities. It is the persistent, stateful bridge between successive RPC research steps, with project-documented discovery, inspection, calling, VM, and audit features. Whether those features fit a particular workflow depends on its Windows environment, privilege needs, and lab controls.
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