PDFReaderExecutor Server

Overview

PDFReaderExecutor provides PDF text extraction with optional human simulation capabilities.

Server Type: Action
Deployment: Local (in-process)
Agent: AppAgent, HostAgent
LLM-Selectable: ✅ Yes

Server Information

Property Value
Namespace PDFReaderExecutor
Server Name UFO PDF Reader MCP Server
Platform Cross-platform (Windows, Linux, macOS)
Dependencies PyPDF2
Tool Type action

Tools

extract_pdf_text

Extract text content from a single PDF file with optional human simulation.

Parameters

Parameter Type Required Default Description
pdf_path str ✅ Yes - Full path to PDF file
simulate_human bool No True Simulate human-like document review

Returns

str - Extracted text content with page markers

Human Simulation Behavior

When simulate_human=True: 1. Opens PDF with default application 2. Waits 2-5 seconds (random) to simulate reading 3. Extracts text with page-by-page delays (0.5-1.5 seconds) 4. Closes PDF file

When simulate_human=False: - Direct text extraction (no delays) - No application launching

Example

# With human simulation (default)
result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_pdf_text",
        tool_name="extract_pdf_text",
        parameters={
            "pdf_path": "C:\\Documents\\report.pdf",
            "simulate_human": True
        }
    )
])

# Fast extraction (no simulation)
result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_pdf_text",
        tool_name="extract_pdf_text",
        parameters={
            "pdf_path": "C:\\Documents\\report.pdf",
            "simulate_human": False
        }
    )
])

Output Format

--- Page 1 ---
This is the content of page 1.

--- Page 2 ---
This is the content of page 2.

--- Page 3 ---
This is the content of page 3.

Error Handling

Returns error message string if: - File not found: "Error: PDF file not found at {path}" - Not a PDF: "Error: File {path} is not a PDF file" - Read error: "Error reading PDF {path}: {details}"

list_pdfs_in_directory

List all PDF files in a specified directory.

Parameters

Parameter Type Required Description
directory_path str ✅ Yes Directory path to scan

Returns

List[str] - List of PDF file paths (sorted)

Example

result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::list_pdfs_in_directory",
        tool_name="list_pdfs_in_directory",
        parameters={"directory_path": "C:\\Documents\\Reports"}
    )
])

# Output: [
#     "C:\\Documents\\Reports\\Q1_Report.pdf",
#     "C:\\Documents\\Reports\\Q2_Report.pdf",
#     "C:\\Documents\\Reports\\Q3_Report.pdf"
# ]

Error Handling

Returns empty list [] if: - Directory doesn't exist - Path is not a directory - No PDF files found

extract_all_pdfs_text

Extract text from all PDF files in a directory with human simulation.

Parameters

Parameter Type Required Default Description
directory_path str ✅ Yes - Directory containing PDFs
simulate_human bool No True Simulate human review for each PDF

Returns

Dict[str, str] - Dictionary mapping filenames to extracted text

Human Simulation Behavior

When simulate_human=True: - Brief pause between files (1-3 seconds random) - Each PDF processed with human simulation - Progress messages logged

Example

result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_all_pdfs_text",
        tool_name="extract_all_pdfs_text",
        parameters={
            "directory_path": "C:\\Documents\\Reports",
            "simulate_human": True
        }
    )
])

# Output: {
#     "Q1_Report.pdf": "--- Page 1 ---\nQ1 Sales Report\n...",
#     "Q2_Report.pdf": "--- Page 1 ---\nQ2 Sales Report\n...",
#     "Q3_Report.pdf": "--- Page 1 ---\nQ3 Sales Report\n..."
# }

Error Handling

Returns dictionary with error key if: - Directory not found: {"error": "Directory not found: {path}"} - Not a directory: {"error": "Path is not a directory: {path}"} - No PDFs found: {"message": "No PDF files found in directory: {path}"}

Configuration

AppAgent:
  default:
    action:
      - namespace: PDFReaderExecutor
        type: local

HostAgent:
  default:
    action:
      - namespace: PDFReaderExecutor
        type: local

Best Practices

1. Disable Simulation for Batch Processing

# ✅ Good: Fast batch processing
result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_all_pdfs_text",
        tool_name="extract_all_pdfs_text",
        parameters={
            "directory_path": "C:\\Documents",
            "simulate_human": False  # Faster
        }
    )
])

# ❌ Bad: Slow with simulation
result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_all_pdfs_text",
        tool_name="extract_all_pdfs_text",
        parameters={
            "directory_path": "C:\\Documents",
            "simulate_human": True  # 2-5 seconds per file
        }
    )
])

2. Verify Files Exist

# List PDFs first
pdf_list = await computer.run_actions([
    MCPToolCall(
        tool_key="action::list_pdfs_in_directory",
        parameters={"directory_path": "C:\\Documents"}
    )
])

if pdf_list[0].data:
    # Extract from first PDF
    text = await computer.run_actions([
        MCPToolCall(
            tool_key="action::extract_pdf_text",
            parameters={
                "pdf_path": pdf_list[0].data[0],
                "simulate_human": False
            }
        )
    ])
else:
    logger.warning("No PDF files found")

3. Handle Large Documents

# Extract text
result = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_pdf_text",
        parameters={"pdf_path": "large_document.pdf", "simulate_human": False}
    )
])

text = result[0].data

# Process in chunks if needed
if len(text) > 100000:  # Large document
    chunks = [text[i:i+50000] for i in range(0, len(text), 50000)]
    for chunk in chunks:
        process_chunk(chunk)

Use Cases

Document Analysis Pipeline

# 1. List all PDFs
pdfs = await computer.run_actions([
    MCPToolCall(
        tool_key="action::list_pdfs_in_directory",
        parameters={"directory_path": "C:\\Contracts"}
    )
])

# 2. Extract text from each
for pdf_path in pdfs[0].data:
    text = await computer.run_actions([
        MCPToolCall(
            tool_key="action::extract_pdf_text",
            parameters={"pdf_path": pdf_path, "simulate_human": False}
        )
    ])

    # 3. Analyze text
    analyze_contract(text[0].data)

Batch Report Processing

# Extract all reports at once
reports = await computer.run_actions([
    MCPToolCall(
        tool_key="action::extract_all_pdfs_text",
        tool_name="extract_all_pdfs_text",
        parameters={
            "directory_path": "C:\\Reports\\2024",
            "simulate_human": False
        }
    )
])

# Process all reports
for filename, content in reports[0].data.items():
    logger.info(f"Processing {filename}")
    # Extract data from content
    data = extract_report_data(content)

Limitations

  • Text-only: Cannot extract images or formatting
  • OCR not supported: Scanned PDFs with no text layer will return empty
  • Table parsing: Complex tables may not preserve structure
  • No modification: Read-only operations (cannot edit PDFs)

Performance

Operation simulate_human=True simulate_human=False
Single PDF (10 pages) ~10-20 seconds ~1 second
Batch 10 PDFs ~2-3 minutes ~10 seconds
Large PDF (100 pages) ~2-5 minutes ~5-10 seconds