Archivebox Api

CLI or API | MCP | Agent

Version: 2.1.0

Documentation — Installation, deployment, usage across the API, CLI, MCP, and A2A agent interfaces, and guidance for provisioning the ArchiveBox platform are maintained in the official documentation.

Related MCP server: mcp-archive

Table of Contents

• Overview

• Key Features

• Concept Registry

• Environment Variables

• CLI or API Usage

• MCP Server Setup

• Agentic AI Graph Agent

• Security & Governance

• Installation

• Contribute

Overview

Archivebox Api is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with the Pythonic ArchiveBox API Wrapper and Fast MCP Server for Agentic AI use!

Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.

• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.

• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).

• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

Concept Registry

This codebase is aligned with the 5 Core Pillars Architecture of the agent-utilities ecosystem:

Environment Variables

Package environment variables

Inherited agent-utilities variables (apply to every connector)

26 package + 14 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

Configure the runtime environment by creating a .env file based on .env.example. Every variable the server reads, grouped by concern.

Connection & Credentials

MCP server / transport

Tool toggles

Each action-routed tool can be disabled individually via its toggle env var (set to false): AUTHENTICATIONTOOL, CORETOOL, CLITOOL (see the Available MCP Tools table below).

Telemetry & governance

Agent CLI (full [agent] runtime only)

CLI or API Usage

You can use the API client programmatically in Python to manage ArchiveBox snapshots:

from archivebox_api import Api

# Initialize client
client = Api(
    url="http://localhost:8000",
    token="your-auth-token",
)

# Fetch snapshots
snapshots = client.get_snapshots()
for snapshot in snapshots.get("results", []):
    print(f"[{snapshot['timestamp']}] {snapshot['url']}")

Refer to docs/index.md for full developer SDK and class references.

MCP Server Setup

Install the connector-focused [mcp] extra. Examples use archivebox-api[mcp] to add FastMCP / FastAPI through agent-utilities[mcp]; the required Agent Utilities core still carries epistemic-graph[full]. The [agent] extra additionally enables model orchestration.

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Tool Catalog

See the auto-generated Available MCP Tools table below for the full, live list of tools.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.

• Environment Variables: Define standard environment variables:

  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS

  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS

• HTTP SSE Request Headers: Pass custom headers during transport initialization:

  • x-mcp-enabled-tools / x-mcp-disabled-tools

  • x-mcp-enabled-tags / x-mcp-disabled-tags

• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:

  • ?tools=tool1,tool2

  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

Local IDE Configuration (Cursor / Claude Desktop)

Add the following block to your mcp.json to configure stdio transport via uvx:

{
  "mcpServers": {
    "archivebox-api": {
      "command": "uv",
      "args": [
        "run",
        "--package",
        "archivebox-api",
        "archivebox-mcp"
      ],
      "env": {
        "ARCHIVEBOX_BASE_URL": "http://localhost:8000",
        "ARCHIVEBOX_USERNAME": "admin",
        "ARCHIVEBOX_PASSWORD": "your-password"
      }
    }
  }
}

Agentic AI Graph Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI).

Running the Agent CLI

To start the interactive command-line agent:

# Export credentials
export ARCHIVEBOX_BASE_URL="http://localhost:8000"
export ARCHIVEBOX_USERNAME="admin"
export ARCHIVEBOX_PASSWORD="your-password"

# Run agent server
archivebox-agent --provider openai --model-id gpt-4o

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/index.md.

Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.

• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.

• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Installation

Pick the extra that matches what you want to run:

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "archivebox-api[mcp]"

# Agent runtime (adds model orchestration to the shared graph engine)
uv pip install "archivebox-api[agent]"

# Everything (development)
uv pip install "archivebox-api[all]"      # or: python -m pip install "archivebox-api[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

docker build --target mcp   -t example/archivebox-api:mcp    docker/   # connector-focused MCP server
docker build --target agent -t example/archivebox-api:agent-local docker/   # agent runtime

docker/mcp.compose.yml runs the connector-focused :mcp server; docker/agent.compose.yml runs the agent (immutable agent digest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

Both [mcp] and [agent] carry the epistemic-graph engine through the required Agent Utilities core dependency (epistemic-graph[full]). The [mcp] extra keeps the server connector-focused; [agent] additionally enables model orchestration. Local deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

AGENTS.md is the canonical contributor/agent guidance.

Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

• Format code using ruff format .

• Lint code using ruff check .

• Validate type-safety with mypy .

• Execute test suites using pytest

Available MCP Tools

The table below is auto-generated from the live server — do not edit by hand.

Condensed action-routed tools (MCP_TOOL_MODE=condensed)

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

5 action-routed tool(s) · 14 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (intent default — the six verb-tools, granular set loaded on demand · condensed action-routed · verbose 1:1 · both). Auto-generated — do not edit.

Additional Deployment Options

archivebox-api can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

• Local container — launch a reviewed immutable image as a least-privilege stdio child with no listener or published port.

• Remote URL — connect through an operator-supplied authenticated HTTPS ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig.

Deploy with agent-utilities-deployment

Provision this package with the consolidated agent-utilities-deployment workflow. It selects an installed-package, editable-source, or immutable-container path; records only runtime secret and TLS-profile references in AgentConfig; and runs doctor, registration, policy, observability, and rollback gates. Ask your agent to "deploy archivebox-api with agent-utilities-deployment".

The repository embeds no deployment profile, credential value, certificate path, or environment-specific endpoint. Supply those at runtime through AgentConfig and the configured secret provider.

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, GraphOS delegation, or trace export.