stirlingpdf-agent 0.23.0

Agent package for communicating with Stirling PDF via REST APIs.

Stirling PDF Agent

CLI or API | MCP | Agent

Version: 0.23.0

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📚 Table of Contents

• Overview
• Key Features
• Installation
• Quick Start & Usage Examples
• MCP Server Mode
  • Available MCP Tools
  • MCP Configuration Examples
• Agent Mode
  • Running the Agent CLI
  • Docker Compose Orchestration
• Environment Variables Reference
• Security & Governance
• Developer & Contribute Guidelines
• Documentation Reference

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Overview

Stirling PDF Agent is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Stirling PDF via REST APIs. It provides seamless capability to manipulate, edit, and overlay PDFs (e.g. adding watermarks) programmatically or using large language models.

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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.

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Installation

Install the Python package locally:

# Using uv (highly recommended for speed and isolation)
uv pip install stirlingpdf-agent[all]

# Using standard pip
python -m pip install stirlingpdf-agent[all]

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Quick Start & Usage Examples

Using the underlying Stirling PDF Client wrapper directly in Python:

from stirlingpdf_agent.api_client import StirlingPdfApi

# Initialize the Stirling PDF client
client = StirlingPdfApi(
    base_url="http://localhost:8080",
    token="your-stirling-pdf-api-key",
    verify=True
)

# Example action: Add a watermark to an existing PDF
response = client.add_watermark(
    filepath="input.pdf",
    watermarkText="CONFIDENTIAL",
    percentOfPage=30,
    opacity=0.5,
    rotation=45
)

# Save output PDF bytes
with open("watermarked_output.pdf", "wb") as f:
    f.write(response.data)

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MCP Server Mode

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

Available MCP Tools

Tool Module	Toggle Env Var	Enabled by Default	Description & Nested Methods
Pdf	PDF_TOOL	True	Execute any Stirling PDF API action dynamically.

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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.

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MCP Configuration Examples

1. stdio Transport (Recommended for local IDEs e.g., Cursor, Claude Desktop)

Configure your IDE's mcp.json to launch the MCP server via uvx:

{
  "mcpServers": {
    "stirlingpdf-agent": {
      "command": "uvx",
      "args": [
        "--from",
        "stirlingpdf-agent",
        "stirlingpdf-mcp"
      ],
      "env": {
        "PDFTOOL": "True",
        "STIRLINGPDF_URL": "http://localhost:8080",
        "STIRLINGPDF_API_KEY": "your_api_key_here",
        "STIRLINGPDF_AGENT_VERIFY": "True"
      }
    }
  }
}

2. Streamable-HTTP Transport (Recommended for production deployments)

Configure your client's mcp.json to launch the Streamable-HTTP server via uvx with explicit host and port definition:

{
  "mcpServers": {
    "stirlingpdf-agent": {
      "command": "uvx",
      "args": [
        "--from",
        "stirlingpdf-agent",
        "stirlingpdf-mcp"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "PDFTOOL": "True",
        "STIRLINGPDF_URL": "http://localhost:8080",
        "STIRLINGPDF_API_KEY": "your_api_key_here",
        "STIRLINGPDF_AGENT_VERIFY": "True"
      }
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name stirlingpdf-agent-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e PORT=8000 \
  -e STIRLINGPDF_URL="http://your-service:8080" \
  -e STIRLINGPDF_API_KEY="your_api_key_here" \
  knucklessg1/stirlingpdf-agent:latest

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Agent Mode

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) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export STIRLINGPDF_URL="http://localhost:8080"
export STIRLINGPDF_API_KEY="your-api-key"

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

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Docker Compose Orchestration

version: '3.8'

services:
  stirlingpdf-agent-mcp:
    image: knucklessg1/stirlingpdf-agent:latest
    container_name: stirlingpdf-agent-mcp
    hostname: stirlingpdf-agent-mcp
    restart: always
    env_file:
      - .env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"

  stirlingpdf-agent-agent:
    image: knucklessg1/stirlingpdf-agent:latest
    container_name: stirlingpdf-agent-agent
    hostname: stirlingpdf-agent-agent
    restart: always
    depends_on:
      - stirlingpdf-agent-mcp
    env_file:
      - .env
    command: [ "stirlingpdf-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://stirlingpdf-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"

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Environment Variables Reference

Stirling PDF Agent utilizes both package-specific environment configurations and standard security settings inherited from the agent-utilities system core.

Stirling PDF Agent Configs

• PDFTOOL (bool, default: True): Toggles the dynamic PDF action tool registration.
• STIRLINGPDF_URL (str, default: http://localhost:8080): The base endpoint of the external Stirling PDF API service.
• STIRLINGPDF_API_KEY (str): API connection token/secret used to authenticate REST requests.
• STIRLINGPDF_AGENT_VERIFY (bool, default: True): Toggles SSL certificate verification during REST requests.

Inherited agent-utilities Configs

• TRANSPORT (str, default: stdio): Server transport type. Options: stdio, sse, streamable-http.
• HOST (str, default: 0.0.0.0): Network host interface to bind the HTTP server.
• PORT (int, default: 8000): Port to listen on.
• ENABLE_OTEL (bool, default: False): Enables OpenTelemetry tracing integration.
• ALLOWED_CLIENT_REDIRECT_URIS (str): Comma-separated list of approved redirect URLs for authentication loops.
• AUTH_TYPE (str): Server authentication mode configurations.
• EUNOMIA_TYPE (str, default: none): Policy configuration enforcement. Options: none, embedded, remote.
• EUNOMIA_POLICY_FILE (str): Path to local JSON configuration policy maps.
• EUNOMIA_REMOTE_URL (str): Target URL for remote auth policy coordination.
• OAUTH_BASE_URL (str): Base OAuth service endpoint.
• OAUTH_UPSTREAM_AUTH_ENDPOINT (str): Upstream OAuth service authorization endpoint.
• OAUTH_UPSTREAM_CLIENT_ID (str): Client application identity ID.
• OAUTH_UPSTREAM_CLIENT_SECRET (str): Client secret credential token.
• OAUTH_UPSTREAM_TOKEN_ENDPOINT (str): Remote OAuth token resolution endpoint.

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Security & Governance

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

• 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.

Feature Guard	Functionality	Status
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

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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

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Documentation Reference

Extensive materials are available in the repository for developer guidance:

• Standard MCP Server Details: docs/mcp.md
• Complete Client & SDK details: docs/index.md
• Agent Graph Orchestration: docs/agent.md