Tools & MCP

HiveMind OS comes loaded with built-in tools — and can connect to virtually any external service through the Model Context Protocol (MCP).

Built-in Tools

Every HiveMind OS installation ships with a rich set of tools the agent can call to get things done:

	Category (prefix)	What It Does
📁	filesystem.*	Read, write, list, search, and glob files on your machine
💻	shell.*	Execute commands in a sandboxed environment with configurable approval
⚙️	core.*	Core agent operations and internal utilities
🧠	knowledge.*	Query, create, and update nodes in the knowledge graph
🌐	http.*	Fetch URLs and make HTTP requests
📝	json.*	JSON parsing, querying, and transforms
🔢	math.*	Mathematical operations and calculations
🕐	datetime.*	Date and time utilities
🔄	process.*	Process management and execution
⚡	workflow.*	Workflow orchestration and management
💬	comm.*	Communication and notifications
📅	calendar.*	Calendar access and scheduling
👤	contacts.*	Contact management
💾	drive.*	Drive and storage access
🔌	connector.*	External service connectors

These tools are always available — no setup required. The agent calls them automatically as part of its agentic loop.

What Is MCP?

MCP in Plain English

Model Context Protocol is an open standard for connecting AI agents to external tools and data sources. Think of it as USB ports for your AI — plug in any compatible server and the agent instantly gains new capabilities, from browsing GitHub repos to querying databases.

MCP servers communicate over two transport types:

• stdio — runs as a local process on your machine (fast, private)
• SSE / Streamable HTTP — connects to a remote server over HTTP (great for shared corporate tools)

When you connect an MCP server, HiveMind OS automatically discovers its tools, resources, and prompts. They become first-class actions the agent can use — just like built-in tools.

Adding an MCP Server

1. Open Settings → MCP Servers → Add
2. Choose a transport type (stdio for local, HTTP for remote)
3. Fill in the connection details and save

Here's an example configuration that adds three MCP servers:

mcp_servers:
  - id: filesystem
    transport: stdio
    command: npx @modelcontextprotocol/server-filesystem /Users/me/projects
    channel_class: local-only

  - id: github
    transport: stdio
    command: npx @modelcontextprotocol/server-github
    env:
      GITHUB_TOKEN: env:GITHUB_TOKEN
    channel_class: internal

  - id: corporate-kb
    transport: streamable-http
    url: https://internal.corp/mcp
    headers:
      Authorization: "Bearer ${CORP_TOKEN}"
    channel_class: internal

Popular MCP Servers to Try

• GitHub — manage repos, PRs, and issues directly from conversation
• Filesystem — enhanced file browsing with directory trees and search
• Brave Search — web search without leaving HiveMind OS
• Postgres / SQLite — query your databases in natural language
• Slack — read and send messages across your workspace

Browse the full directory at MCP Servers Directory.

Tool Approval Policies

Not every tool should run without oversight. HiveMind OS lets you set a policy for each tool:

Policy	Behaviour
Auto	Tool runs immediately without asking — best for trusted, read-only tools
Ask	Prompts you for confirmation before running (default for new tools)
Deny	Tool is blocked entirely — it won't appear in the agent's available actions

You can configure these per-tool in your agentic loop config:

tool_policy:
  auto_approve:
    - filesystem.read
    - github.get_issue
  require_confirmation:
    - filesystem.write
    - github.create_pr
  deny:
    - shell.execute

Channel Classification on Tools

MCP servers get a channel classification level just like providers. The same data-classification rules apply: the agent will never send CONFIDENTIAL data to a server classified as public.

This means you can safely mix trusted internal servers with public ones — HiveMind OS enforces the boundaries automatically. A GitHub server classified as internal can see your private repo names, but a public search server won't receive anything beyond PUBLIC data.

Example: Summarise Your Open PRs

Connect the GitHub MCP server and try this:

"Summarise my open pull requests and flag any that have been waiting for review for more than 3 days."

HiveMind OS will call the GitHub MCP server's tools to list your PRs, check review status, and present a neat summary — all without you writing a single API call.

Managed Runtimes

Many tools and MCP servers require Node.js or Python to run. HiveMind OS ships with managed runtimes that are downloaded automatically — the agent uses these instead of relying on your system-installed versions. This ensures consistent behaviour for shell commands, process execution, and MCP stdio servers like npx @modelcontextprotocol/server-github.

Learn More

• Configure MCP Servers — Detailed setup guide for MCP
• Privacy & Security — How classification protects your data across tools
• Agentic Loops — How the agent decides which tools to call