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  3. Managed Agent Control MCP
Managed Agent Control MCP logo
Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 2:30:45 PM

Managed Agent Control MCP

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View Repository1 GitHub StarsTotal stargazers on GitHub for the source repository (1 stars).Visit Website
anthropicagent-controldeveloper-toolsmcp

Control Claude Managed Agent sessions through MCP, including launching, monitoring, messaging, approvals, interruption, and cleanup.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

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We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "modus-agendi-managed-agent-control-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/inspector"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives☁️ More in Cloud Platforms

Overview

The modus-agendi/managed-agent-control-mcp MCP server exposes Claude Managed Agents through tools that MCP clients can call. It uses an Anthropic API key to discover agent resources, start sessions, poll events, send messages, approve or deny tool requests, interrupt runs, and archive or delete sessions. The server supports local stdio, HTTP containers, and AWS Lambda deployments, with bearer, OIDC, or Cognito authentication for HTTP clients. Use it when an MCP client needs operational control over agents running on Anthropic's Managed Agents platform.

Use cases

•Launch a Claude Managed Agent from an MCP client
•Poll agent events while a session runs
•Approve or deny requested tool executions
•Send follow-up instructions or interrupt an active session
•Deploy agent controls behind HTTP authentication

Key features

•Discover agents, environments, vaults, and memory stores
•Start and manage Claude Managed Agent sessions
•Poll session events and inspect token usage
•Send messages, interrupt runs, and handle tool approvals
•Run over stdio, HTTP containers, or AWS Lambda
•Support bearer, OIDC, and Cognito inbound authentication

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Managed Agent Control MCP.

Extracted Tool Capabilities
Discover agents, environments, vaults, and memory stores
Start and manage Claude Managed Agent sessions
Poll session events and inspect token usage
Send messages, interrupt runs, and handle tool approvals
Run over stdio, HTTP containers, or AWS Lambda
Support bearer, OIDC, and Cognito inbound authentication

How Managed Agent Control MCP works

What modus-agendi/managed-agent-control-mcp MCP server does

The modus-agendi/managed-agent-control-mcp MCP server provides an MCP control layer for Claude Managed Agents hosted on Anthropic's platform. It does not define the agents themselves; instead, it lets an MCP client find existing agent definitions and related resources, create sessions, follow their activity, and interact with them during execution.

The server is intended for clients such as Claude.ai, Claude Code, Cursor, or custom MCP implementations. A client can use it to launch an agent with an initial instruction, inspect session state and token usage, retrieve activity events, continue a conversation, respond to approval requests, interrupt execution, and finish by archiving or deleting the session.

How it works

The modus-agendi/managed-agent-control-mcp MCP server sits between an MCP client and Anthropic's Managed Agents API. Outbound requests to Anthropic use the operator's ANTHROPIC_API_KEY. For HTTP deployments, clients can be required to authenticate separately using a static bearer token, an OIDC provider, or the Cognito authentication preset.

The interaction model is request-based rather than a live event stream. A typical workflow is to discover an agent, start a session, call session_get for status, and poll session_events for output or activity. The client can then send another message, interrupt the agent, or approve or deny a tool request with the appropriate session operation. This polling design matters when choosing a client or building an orchestration loop because new agent activity is not pushed directly into the chat.

Setup and configuration

Local operation requires Python support through the repository's documented uv workflow and an Anthropic API key with Managed Agents access. The repository also documents a PyPI-based uvx managed-agent-control-mcp invocation once the package is available there. The local server runs over stdio; container deployments expose HTTP, and an included Terraform module supports AWS Lambda deployment.

ANTHROPIC_BASE_URL can override the API endpoint for gateways or testing. HTTP deployments can set MCP_AUTH_MODE to bearer, oidc, or cognito, with additional variables for tokens and OIDC verification. Optional guardrails include an agent allowlist using MCP_ALLOWLIST_AGENTS_ACTIVE and MCP_ALLOWED_AGENT_IDS, plus MCP_ALLOW_DESTRUCTIVE to disable archive and delete operations.

Tools and capabilities

Resource discovery tools include agent_list, agent_get, environment_list, environment_get, vault_list, vault_get, memory_store_list, and memory_store_get. These help the client select an agent, sandbox environment, credential vault, or persistent memory store before starting a session.

Session operations cover the full control loop:

  • session_start creates a session and may send its first instruction.
  • session_get returns status and token usage.
  • session_list lists sessions.
  • session_events polls activity with cursor and type-filter options.
  • session_message sends a message or continues a turn.
  • session_interrupt stops or redirects a running agent.
  • session_respond approves or denies a requested tool action.
  • session_archive preserves history while ending active use, while session_delete removes a session.

Limitations and notes

The project is a community-maintained implementation and is not endorsed or maintained by Anthropic. The Managed Agents API is identified as beta in the repository. The Anthropic API key must have access to Managed Agents, and the server acts with the permissions of that key.

HTTP deployments should not be exposed without inbound authentication. Bearer, OIDC, or Cognito authentication protects access to the server, but it is separate from the outbound Anthropic credential. The polling model also means clients must repeatedly request session state or events instead of receiving a continuous stream.

The destructive archive and delete operations can be disabled with configuration, and agent launching can be restricted to an explicit allowlist. These controls are relevant when the server is shared by multiple MCP clients or deployed as an HTTP service.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
1
Stargazers on the source repository.
Last commit
18d ago
Most recent push to the default branch.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

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Frequently Asked Questions about Managed Agent Control MCP

The documented package command is `uvx managed-agent-control-mcp`. The repository also provides a local setup using `uv sync` followed by `uv run python -m managed_agents_mcp`.

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Technical Specs & Signals

Category☁️Cloud Platforms
PricingBring your own API key (usage-based cost)
More technical detailsExpand ▾
TransportSTDIO
RuntimeNode.js
AuthAPI key
LicenseMIT
ClientsCursor
Last updatedSep 7, 2026
10/13 checks healthy over the last 32d
Views3
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars1
GitHub Star CountTotal stargazers on GitHub representing community popularity (1 stars).
Last commit18d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 24, 2026
47Quality signal: Fair · 47/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership6/20
Documentation & tools25/30
Adoption & activity4/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

2 high-severity advisories on record for this package. Most advisories affect transitive dependencies and may not be exploitable in this server's actual usage — this is a directional signal, not a security audit.

Critical 1High 1Medium 0Low 0

Scanned 20d ago via OSV.dev · @modelcontextprotocol/inspector (npm)

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