USPTO Final Petition Decisions MCP Server
This server provides a high-performance MCP interface for the USPTO Final Petition Decisions API with intelligent context reduction, document processing, and cross-MCP integration for comprehensive patent lifecycle analysis.
Search & Discovery:
Ultra-fast minimal search (50-100 results, 95-99% context reduction) for rapid discovery
Balanced search (18 key fields, 80-88% reduction) for detailed analysis
Art unit-specific searches for examiner quality assessment
Application history retrieval for complete petition tracking
14 user-friendly convenience parameters (applicant name, decision type, dates, petition type, art unit) without query syntax
Document Processing:
Intelligent hybrid extraction (free PyPDF2 → Mistral OCR fallback at ~$0.001/page)
Secure browser-accessible download URLs via proxy (API keys never exposed)
Enhanced filenames with petition metadata for professional file management
7-day persistent links via centralized proxy integration
Cross-MCP Integration:
Seamless integration with USPTO PFW, PTAB, and Enhanced Citations MCPs
Unified document management and rate limiting across MCPs
Complete patent lifecycle tracking from prosecution through post-grant challenges
Analysis Features:
Automated red flag identification (revival petitions, examiner disputes, denied decisions)
Art unit quality assessment and examiner behavior pattern analysis
Progressive disclosure workflow (minimal → balanced → detailed → document extraction)
Context-efficient guidance system with 80-95% token reduction
Production-Ready:
Secure API key storage (Windows DPAPI encryption or environment variables)
Retry logic with exponential backoff and USPTO rate limiting compliance
Structured logging with request ID tracking and enhanced error handling
Customizable field sets via YAML configuration
Cross-platform compatibility (Linux and Windows)
Key Use Cases:
Company petition risk assessment and due diligence
Patent vulnerability analysis and litigation preparation
Art unit/examiner quality evaluation
Complete portfolio lifecycle analysis with prosecution history correlation
Prompt templates for legal analysis, research, and document management workflows
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@USPTO Final Petition Decisions MCP Serverfind recent decisions for patent 9876543"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
USPTO Final Petition Decisions MCP Server
A high-performance Model Context Protocol (MCP) server for the USPTO Final Petition Decisions API with token-saving context reduction capabilities, user-customizable fields, and cross-MCP integration for complete patent lifecycle analysis.
📚 Documentation
Document | Description |
Complete cross-platform setup with automated scripts | |
Step-by-step instructions for obtaining USPTO and Mistral API keys with screenshots | |
Function examples, workflows, and integration patterns | |
Detailed guide to sophisticated prompt templates for legal & research workflows | |
Comprehensive guidance on customizing field sets for the minimal and balanced tools | |
Comprehensive security best practices | |
Automated secret detection and prevention guide | |
Retrieved-text handling: verbatim serving, provenance labeling, injection annotation | |
Test suite documentation and API key setup | |
MIT License terms and conditions |
Related MCP server: USPTO Patent Citation MCP Server
⚡Quick Start
Windows Install
Run PowerShell as Administrator, then:
# Navigate to your user profile
cd $env:USERPROFILE
# If git is installed:
git clone https://github.com/john-walkoe/uspto_fpd_mcp.git
cd uspto_fpd_mcp
# If git is NOT installed:
# Download and extract the repository to C:\Users\YOUR_USERNAME\uspto_fpd_mcp
# Then navigate to the folder:
# cd C:\Users\YOUR_USERNAME\uspto_fpd_mcp
# The script detects if uv is installed and if it is not it will install uv - https://docs.astral.sh/uv
# Run setup script (sets execution policy for this session only):
Set-ExecutionPolicy -ExecutionPolicy Unrestricted -Scope Process
.\deploy\windows_setup.ps1
## View INSTALL.md for sample script output.
# Close Powershell Window.
# If choose option to "configure Claude Desktop integration" during the script then restart Claude DesktopThe PowerShell script will:
✅ Check for and auto-install uv (via winget or PowerShell script)
✅ Install dependencies and create executable
✅ Prompt for USPTO API key (required) and Mistral API key (optional) or Detect if you had installed the developer's other USPTO MCPs and ask if want to use existing keys from those installation.
🔒 If entering in API keys, the script will automatically store API keys securely using Windows DPAPI encryption
✅ Ask if you have USPTO PFW MCP already installed, and if so will used the USPTO PFW MCP's default centralized proxy
✅ Ask if you want Claude Desktop integration configured
🔒 Offer secure configuration method (recommended) or traditional method (API keys in plain text in the MCP JSON file)
✅ Backups and then automatically merge with existing Claude Desktop config (preserves other MCP servers)
✅ Provide installation summary and next steps
Claude Desktop Configuration - Manual installs
{
"mcpServers": {
"uspto_fpd": {
"command": "uv",
"args": [
"--directory",
"C:/Users/YOUR_USERNAME/uspto_fpd_mcp",
"run",
"fpd-mcp"
],
"env": {
"USPTO_API_KEY": "your_actual_USPTO_api_key_here",
"MISTRAL_API_KEY": "your_mistral_api_key_here_OPTIONAL",
"MISTRAL_OCR_MODEL": "mistral-ocr-latest_OPTIONAL_pin_a_dated_slug",
"CENTRALIZED_PROXY_PORT": "none",
"FPD_PROXY_PORT": "8081"
}
}
}
}Proxy Configuration Notes:
CENTRALIZED_PROXY_PORT:
Set to
"none"for standalone use (not recommended)Set to 8080 When USPTO PFW MCP is installed and PFW is using its default port for the local proxy. (If PFW is not using its default port change this value to match)
FPD_PROXY_PORT: Local proxy port (default:
8081, avoids conflict with PFW on8080)Only used in standalone mode (no PFW MCP detected)
When PFW MCP is installed, FPD automatically uses PFW's centralized proxy (port
8080), but will fall back to FPD's local proxy portCentralized Proxy Benefits: Single port for all USPTO MCPs, 7-day persistent links, unified rate limiting
🚀 FastMCP 4.0
The server runs on FastMCP 4.0.1 (fastmcp[apps]>=4.0.0,<5.0.0) over the
MCP Python SDK 2.x, speaking MCP protocol revision 2026-07-28. It ships
MCP Apps (petition search cards + recent-downloads panel render as iframes in
Claude Desktop), progress notifications during document extraction, 7-day
persistent download links, dual STDIO/HTTP transport, and a Dockerfile for
containerized deployment.
Environment variables:
Variable | Default | Purpose |
| — | Required. USPTO ODP API key |
| unset | Optional. Enables the Mistral OCR tier for scanned documents |
|
| Per-document page cap for the Mistral OCR tier; a capped extraction is always marked |
|
| Daily (UTC) ceiling on metered Mistral OCR usage. Unset uses the built-in default; |
| unset | Enables the self-hosted Docling OCR tier (e.g. |
|
| Docling read timeout (seconds) |
|
| Skip Docling above this page count (petition decisions are short) |
|
|
|
|
| HTTP bind address |
|
| HTTP port (cluster convention: fpd = 8005) |
|
| Stateless streamable HTTP: no server-side session table, every request self-contained. Required for clients that don't replay |
| unset | Extra CORS origins for HTTP mode (e.g. |
| unset | Required in HTTP mode (X-API-KEY auth); also signs centralized-proxy JWTs — must match PFW's |
|
| Enable the local download proxy |
|
| Start the proxy at startup vs on-demand |
|
| Local download proxy port |
| unset | Externally reachable proxy base URL (Docker/reverse proxy) — used in emitted persistent links |
| auto | Fixed proxy auth token for cross-process registration |
| unset | Extra client IPs/CIDRs allowed at the proxy (Docker subnets) |
| unset | Full base URL of the PFW centralized proxy. Docker: |
| unset | Legacy port-only PFW config (localhost) |
| unset | Extra CSP domains for MCP App iframes (comma-separated) |
|
| Logging level. Logs record flow metadata only — never query text, response bodies, OCR text, or link hashes |
|
| Log directory override (Docker volumes) |
|
| File log rotation |
|
| Mistral OCR model slug; pin to a dated version if needed |
|
| API request timeout (seconds) |
|
| PDF download timeout (seconds) |
|
| Enable admin-tool registration; must be |
|
|
|
| unset | Public HTTPS origin (required if |
| unset | JWT signing secret (required if |
| unset | Google OAuth credentials (optional; omit if using Entra only) |
| unset | Entra (Microsoft) OAuth credentials (optional; omit if using Google only) |
|
| Entra tenant: |
| unset | Static bearer token for headless clients (grants |
| unset | Separate static bearer for machine clients requiring admin scope (grants |
| unset | Optional "Request access" link shown on sign-in screen |
|
| Access token lifetime (seconds; default 1 hour) |
|
| Refresh token lifetime (seconds; default 30 days, idle timeout) |
|
| SQLite user database; may be shared across MCP servers on the same host |
| unset | Enable cross-process rate limiter; bind-mounted directory (unset = disabled) |
|
| Token bucket rate (tokens/sec, shared across all 4 USPTO MCPs) |
|
| Concurrency slots (shared in-flight requests across all 4 MCPs) |
|
| Character budget for structured tool responses (searches, petition details) |
|
| Character budget for document-content responses |
|
|
|
|
| Overall deadline for one content-extraction tool call |
|
| Per-document extraction byte cap (25MB) |
|
| Cap on a PDF streamed through the download proxy (100MB) |
|
| Request body cap on the |
| unset | Reverse-proxy addresses (IP/CIDR) whose |
| unset (production) |
|
Response-size markers
Tool responses are measured in characters (len(json.dumps(...))), not
token estimates, because an oversized result is replaced client-side with a
truncation error the server never sees. Every search envelope carries a
paging block (limit_requested, limit_applied, offset, returned,
total, has_more, next_offset). Three further markers appear only when
they applied and are absent entirely on a no-op:
Marker | Meaning |
|
|
|
|
|
|
FPD_get_guidance(section="limits") prints the live budgets and this marker
contract at runtime.
Testing: see tests/TEST_SUITE.md for the manual
end-to-end suite; automated tests via plain uv run pytest (the
key-management/storage tests that touch real encrypted key storage are
excluded via addopts in pyproject.toml and additionally gated by
FPD_RUN_KEY_TESTS=1).
🔑 Key Features
⚙️User-Customizable Fields - Configure field sets through YAML without code changes
🎯Context Reduction - Get focused responses instead of massive API dumps (80-99% reduction)
📊Progressive Disclosure Strategy - Minimal discovery → Balanced analysis → Document extraction
🔍Petition-Type Focused Search - Specialized tools for art unit and application-specific searches
✨Intelligent Document Extraction - Auto-optimized hybrid extraction (native
pypdftext layer → OCR fallback for scanned pages) with secure browser downloads🆕Centralized Proxy Integration - Auto-detects PFW MCP and uses unified proxy (port 8080) for persistent links and cross-MCP downloads
🌐Secure Browser Downloads - Click proxy URLs to download PDFs directly while keeping API keys secure
👁️Advanced OCR Capabilities - Extract text from scanned PDFs using Mistral OCR when needed
📁 Document Bag Integration - Full petition document access alongside structured petition data
Extraction Method Transparency - Reports which extraction method was used for every document
🔐 Secure API Key Storage - Optional Windows DPAPI encryption keeps API keys secure (no plain text in config files)
🚀High Performance - Retry logic with exponential backoff, rate limiting compliance
🛡️ Production Ready - Enhanced error handling, structured logging with request IDs, comprehensive security guidelines
💻Cross-Platform - Works seamlessly on Linux and Windows
📋Complete API Coverage - All USPTO Final Petition Decisions endpoints supported
🔗Cross-MCP Integration - Seamless integration with Patent File Wrapper and PTAB MCPs for complete lifecycle analysis
Workflow Design - All Performed by the LLM with Minimal User Guidance
User Requests the following:
"Find all petitions filed by TechCorp and tell me about any red flags"
"Show me revival petitions for art unit 2128 - I'm analyzing abandonment patterns"
"Get me the petition history for application 17414168"
"Research this company's petition track record and correlate with their PTAB challenges" - * Requires that the USPTO Patent Trial and Appeal Board (PTAB) be installed - uspto_ptab_mcp and also recommended to ask LLM to perform a FPD_get_guidance tool call prior to this or any cross MCP prompt (see quick reference chart for section selection, additional details in Usage Examples)
"Analyze this art unit's prosecution quality by looking at petition frequency and types"
LLM Performs these steps:
Step 1: Discovery (Minimal) → Step 2: Selection and Analysis (Balanced - Optional) → Step 3: Detailed Petition Review → Step 4 (Optional): Select specific petition documents for examination → Step 5 (Optional): Retrieve document_id(s) from documentBag → Step 6 (Optional): Document Extraction for LLM use and/or Download Links of PDFs for user's use
The field configuration supports an optimized research progression:
Discovery (Minimal) returns 50-100 petitions efficiently without document bloat
Selection and Analysis (Balanced - Optional) from the retrieved select likely petition(s). Optional balanced search(es) performed if needed in advanced workflows and/or cross-MCP workflows with Patent File Wrapper or PTAB
Detailed Petition Review via
FPD_Get_petition_detailsfor selected petitions with complete structured data for LLM's use in analysisSelect specific petition documents for examination (Optional) e.g. Decision letters, petition filings, supporting evidence
Retrieve document_id(s) from documentBag (Optional) use
FPD_Get_petition_detailswithinclude_documents=Trueto get the document_id(s)Document Extraction for LLM use and/or Download Links (Optional) Document extraction via intelligent hybrid tool that auto-optimizes for speed and quality, and Downloads of the documents as PDFs uses URLs from an HTTP proxy that obscures the USPTO's API key from chat history
🎯 Prompt Templates
This MCP server includes sophisticated AI-optimized prompt templates for complex petition workflows. Prompt templates are opt-in server-side: they register only when the server is started with FPD_ENABLE_PROMPTS=true (default off - no prompts are advertised). For detailed documentation on all templates, features, and usage examples, see PROMPTS.md.
Quick Template Overview
Category | Templates | Purpose |
Legal Analysis |
| Due diligence, PTAB risk assessment, litigation preparation |
Research & Prosecution |
| Art unit analysis, examiner behavior, abandonment patterns |
Document Management |
| Organized retrieval, comprehensive lifecycle analysis |
Key Features Across All Templates:
Enhanced Input Processing - Flexible identifier support (petition IDs, application numbers, company names)
Smart Validation - Automatic format detection and guidance
Cross-MCP Integration - Seamless workflows with PFW, PTAB, and Citations MCPs
Context Optimization - Token reduction through progressive disclosure
📊 Available Functions
Search Functions (4 Focused Tools)
Registered Tool Name | Context Reduction | Use Case |
| typical 95-99% | Ultra-fast petition discovery (user-customizable minimal fields) |
| typical 80-88% | Key fields for detailed analysis (no documentBag) |
| typical 80-88% | Art unit quality assessment with date range filtering |
| typical 80-88% | Complete petition history for specific application |
Search Strategies
Specialized Search Strategies
Art Unit Quality Assessment - Use
FPD_Search_petitions_by_art_unitto analyze petition patterns across art units for examiner behavior and technology difficulty assessmentApplication Petition History - Use
FPD_Search_petitions_by_applicationto get complete petition timeline for specific applications during prosecutionCross-MCP Integration - Link petition data with PFW prosecution history using
applicationNumberTextand PTAB challenges usingpatentNumberRed Flag Identification - Focus on revival petitions (37 CFR 1.137), examiner disputes (37 CFR 1.181), and denied decisions for prosecution quality analysis
Query Examples
# Art unit quality assessment
FPD_Search_petitions_by_art_unit(
art_unit="2128",
date_range="2020-01-01:2024-12-31",
limit=100
)
# Complete application petition history
FPD_Search_petitions_by_application(
application_number="17414168",
include_documents=False
)
# Cross-MCP workflow example
# 1. Find applications with PFW
# 2. Check petition history for red flags
FPD_Search_petitions_by_application(
application_number=app_from_pfw,
include_documents=True
)Document Processing Functions
Registered Tool Name | Purpose | Requirements |
| Full petition details by UUID with optional documentBag | USPTO_API_KEY |
| Intelligent hybrid document extraction | USPTO_API_KEY (+ |
| Secure browser-accessible download URLs | USPTO_API_KEY |
Document Processing Capabilities
Petition Details Tier (
FPD_Get_petition_details): Complete petition data retrievalUUID-based lookup - Find petition by unique identifier
Optional document bag - Include/exclude documents based on need
LLM-optimized parsing - Extracts issues, rules cited, statutes, decision details
Cross-reference fields - applicationNumberText, patentNumber, groupArtUnitNumber for cross-MCP workflows
Intelligent Extraction Tier (
FPD_get_document_content_with_ocr): Hybrid auto-optimized extractionCapability-ordered tiers - Reads the PDF's native text layer with
pypdffirst; falls back to OCR only for pages that have no usable text layerQuality detection - Automatically determines whether the native text layer is usable or the page needs OCR
Two OCR backends - Mistral OCR (
MISTRAL_API_KEY, optional) or a self-hosted Docling backend such asdocling-serve(DOCLING_SERVE_URL); either is sufficient, neither is required for text-layer PDFsTransparent reporting -
extraction_methodnames the tier that produced the text (pypdf,Mistral OCR (...), orDocling (docling-serve))Unified interface - Single tool handles all document types (eliminates tool confusion)
Speed - Instant for text-layer PDFs; OCR of scanned documents is slower per page
Browser Download Tier (
FPD_get_document_download): Secure proxy downloads with enhanced filenamesClick-to-download URLs that work directly in any browser
Centralized proxy integration - If set up, auto-detects PFW MCP and uses unified proxy (port 8080) for all USPTO documents downloads, will fall back to local proxy if issues detected with centralized proxy.
Persistent links - 7-day encrypted links when using PFW centralized proxy (work across MCP restarts)
Unified architecture - Single HTTP proxy (port 8080) for all USPTO MCPs when PFW installed
Standalone fallback - Local proxy (port 8081) when PFW not detected
Enhanced filenames - Professional format with petition date, app/patent numbers, and description
Format:
PET-2013-09-10_APP-13632078_PAT-8803593_PATENT_PROSECUTION_HIGHWAY_DECISION.pdfChronological sorting by petition filing date
Instant context for patent attorneys and file management
API key security - USPTO credentials never exposed in chat history or browser
Rate limiting compliance - Automatic enforcement of USPTO's 5 downloads per 10 seconds
LLM Guidance Function
Registered Tool Name | Purpose | Requirements |
| Context-efficient sectioned LLM guidance (80-95% token reduction) | None |
Admin Function (OAuth deployments only)
Registered Tool Name | Purpose | Requirements |
| Registered-user management (list/add/set_role/activate/deactivate) |
|
Context-Efficient Guidance System
FPD_get_guidanceTool - Solves MCP Resources visibility problem with selective guidance sections:
🎯 Quick Reference Chart - What section for your question?
🔍 "Find petitions by company/art unit" → tools
🚩 "Identify petition red flags" → red_flags
📄 "Download petition documents" → documents
🤝 "Correlate petitions with prosecution" → workflows_pfw
⚖️ "Analyze petition + PTAB patterns" → workflows_ptab
📊 "Citation quality + petition correlation" → workflows_citations
🏢 "Complete portfolio due diligence" → workflows_complete
📚 "Research CFR rules with Assistant" → workflows_assistant
🎯 "Ultra-minimal PFW + FPD workflows" → ultra_context
"Choose an extraction approach" → extraction
📏 "Why was my response truncated / how do I page it?" → limits
🗓️ "Why did an old petition return zero results?" → coverage
The tool provides specific workflows, field recommendations, API call optimization strategies, anti-patterns to avoid, and cross-MCP integration patterns for maximum efficiency. See USAGE_EXAMPLES.md for detailed examples and integration workflows.
💻 Usage Examples & Integration Workflows
For comprehensive usage examples, including:
Basic petition searches (company, type, outcome)
Art unit quality assessment (petition frequency, types, examiner disputes)
Application petition history (complete lifecycle tracking)
Cross-MCP integration workflows (FPD + PFW + PTAB + Pinecone)
Red flag identification (revival petitions, examiner disputes, denied petitions)
Document extraction and downloads (hybrid
pypdftext-layer / OCR approach)Extraction strategy guidance
See the detailed USAGE_EXAMPLES.md documentation.
🔧 Field Customization
The MCP server supports user-customizable field sets through YAML configuration for optimal context reduction. You can modify field sets without changing any code!
Configuration file: field_configs.yaml (in project root)
For complete customization guidance, including progressive workflow strategies, token optimization, and advanced field selection patterns, see CUSTOMIZATION.md.
🔗 Cross-MCP Integration
This MCP is designed to work seamlessly with other USPTO MCPs and knowledge bases for comprehensive patent lifecycle analysis:
Related USPTO MCP Servers
MCP Server | Purpose | GitHub Repository |
USPTO Patent File Wrapper (PFW) | Prosecution history & documents | |
USPTO Final Petition Decisions (FPD) | Petition decisions during prosecution | |
USPTO Enhanced Citations | AI-extracted citation intelligence from Office Actions mailed Oct 2017-present (apps filed 2015+) | |
USPTO Patent Trial and Appeal Board (PTAB) | Post-grant challenges | |
Pinecone Assistant MCP | Patent law knowledge base with AI-powered chat and citations (MPEP, examination guidance) - 1 API key, limited free tier | |
Pinecone RAG MCP | Patent law knowledge base with custom embeddings (MPEP, examination guidance) - Requires Pinecone + embedding model, monthly resetting free tier |
Integration Overview
The Final Petition Decisions (FPD) MCP bridges prosecution and post-grant challenges, tracking procedural petitions that reveal prosecution quality issues. When combined with the other MCPs, it enables:
FPD + PFW: Understand petition context by cross-referencing with prosecution history
FPD + PFW + Enhanced Citations: Correlate petition patterns with examiner citation quality for comprehensive prosecution assessment (Oct 2017+ applications)
FPD + PTAB: Correlate petition red flags with post-grant challenge outcomes
PFW + FPD + PTAB: Complete patent lifecycle tracking from filing through post-grant challenges
PFW + FPD + Enhanced Citations: Art unit quality assessment with citation intelligence and petition pattern analysis
FPD + Pinecone (Assistant or RAG): Research MPEP guidance and petition standards before pulling full document text
Key Integration Patterns
Cross-Referencing Fields:
applicationNumberText- Primary key linking petitions to PFW prosecution and Enhanced CitationspatentNumber- Secondary key linking granted patents to PTAB challengesgroupArtUnitNumber- Art unit analysis across all MCPs (FPD, PFW, Enhanced Citations, PTAB)firstApplicantName- Party matching across MCPsexaminerCitedReferenceIndicator(Citations MCP) - Examiner vs applicant citation analysis for petition quality correlation
Progressive Workflow:
Discovery (FPD): Find petitions using minimal search
Prosecution Context (PFW): Cross-reference petition applications with prosecution history
Citation Intelligence (Enhanced Citations): Analyze examiner citation quality for applications with petitions (Oct 2017+ only)
Challenge Assessment (PTAB): Check if patents with petition red flags faced post-grant challenges
Knowledge Research (RAG): Research MPEP petition guidance if available
Detailed Analysis (FPD): Extract petition documents for Director's reasoning
Risk Scoring: Quantify prosecution quality based on petition patterns, citation quality, and outcomes
For detailed integration workflows, cross-referencing examples, and complete use cases, see USAGE_EXAMPLES.md.
🆕Centralized Proxy Integration (PFW + FPD)
When both PFW and FPD MCPs are installed, FPD automatically integrates with PFW's centralized proxy for unified document management:
Architecture Benefits:
Single Port - One HTTP server (port 8080) for all USPTO document downloads
Persistent Links - 7-day encrypted links via PFW's SQLite database (work across MCP restarts)
Unified Rate Limiting - Shared USPTO limits (5 requests/10 seconds) across all MCPs
Cross-MCP Caching - PFW caches documents from all USPTO MCPs for faster access
Automatic Detection - FPD detects PFW at startup and switches to centralized mode
How It Works:
FPD extracts PDF download URL from USPTO API response
FPD generates enhanced filename:
PET-{date}_APP-{app}_PAT-{patent}_{description}.pdfFPD registers document with PFW:
POST /register-fpd-document(includes enhanced filename)PFW stores metadata in database (petition_id, download_url, api_key, enhanced_filename)
FPD returns download link:
http://localhost:8080/download/{petition_id}/{doc_id}User clicks link → PFW fetches from USPTO → streams PDF with enhanced filename
Link persists for 7 days and works across MCP restarts
Standalone Mode:
Without PFW: FPD uses local proxy (port 8081) for immediate session-based downloads
Enhanced filenames still work (same generation logic used locally)
Graceful fallback ensures FPD works independently with full filename functionality
📈 Performance Comparison
Method | Response Size | Context Usage | Features |
Direct curl | ~100KB+ | High | Raw API access |
MCP Balanced | ~10KB | Medium | Key fields for analysis |
MCP Minimal | ~2KB | Very Low | Essential data only |
🧪 Testing
Automated Suite
uv run pytestRuns offline (network boundaries are mocked). The live-API tests in
tests/test_integration.py skip automatically unless USPTO_API_KEY is set,
and the key-management/storage tests that touch real encrypted key storage
are excluded via addopts in pyproject.toml (gated by
FPD_RUN_KEY_TESTS=1).
Core Tests (Essential)
With uv (Recommended):
# Test core functionality and field configuration
uv run python tests/test_basic.py
# Expected: ALL TESTS PASSED!With traditional Python:
python tests/test_basic.pyExpected Outputs
test_basic.py:
[OK] Settings imported successfully
[OK] FieldManager imported successfully
[OK] FPDClient initialized successfully
ALL TESTS PASSED!See tests/README.md for comprehensive testing guide.
📁 Project Structure
uspto_fpd_mcp/
├── field_configs.yaml # Root-level field customization
├── .security/ # Commit-time security scanning components
│ ├── prompt_injection_detector.py # Base prompt injection detection
│ ├── fpd_prompt_injection_detector.py # FPD-specific detection patterns
│ └── check_prompt_injections.py # Standalone scanning script (pre-commit hook)
├── src/
│ └── fpd_mcp/
│ ├── main.py # Composition root: FastMCP server, 9 tools (8 registered by default; FPD_manage_users needs FPD_ENABLE_USER_MANAGEMENT), OAuth wiring
│ ├── __main__.py # Entry point for -m execution
│ ├── runtime.py # Settings/logging bootstrap + service singletons
│ ├── server_bootstrap.py # Transport startup + proxy lifecycle
│ ├── middleware.py # HTTP auth / security-header middleware
│ ├── validators.py # Input validation (petition IDs, dates, identifiers)
│ ├── shared_secure_storage.py # Cross-MCP encrypted API key storage
│ ├── tools/ # Tool implementations (registered by main.py)
│ │ ├── petitions.py # 5 search/details tools
│ │ ├── documents.py # Download + content-extraction tools
│ │ ├── guidance.py # FPD_get_guidance
│ │ └── admin.py # FPD_manage_users (registration-gated)
│ ├── config/
│ │ ├── field_manager.py # Configuration management
│ │ ├── settings.py # Environment configuration
│ │ ├── tool_reflections.py # Sectioned LLM guidance (80-95% token reduction)
│ │ ├── log_config.py # Logging setup (sanitizing filter on every handler)
│ │ ├── api_constants.py # API configuration constants
│ │ ├── feature_flags.py
│ │ └── storage_paths.py # Storage path management
│ ├── prompts/ # 10 prompt templates (opt-in via FPD_ENABLE_PROMPTS)
│ ├── api/
│ │ ├── fpd_client.py # FPD API client (retries, circuit breakers, cache)
│ │ ├── docling_client.py # Self-hosted Docling OCR client
│ │ └── field_constants.py # Field name constants
│ ├── proxy/
│ │ ├── server.py # HTTP proxy for secure downloads
│ │ ├── secure_link_cache.py # 7-day encrypted persistent links
│ │ ├── centralized_integration.py # PFW centralized-proxy mode
│ │ └── rate_limiter.py # USPTO rate limiting compliance
│ ├── auth/ # OAuth 2.1 provider (Google + Entra dual IdP)
│ ├── shared/
│ │ ├── injection_scan.py # Runtime detection-only injection scanner + provenance note
│ │ ├── error_utils.py # Error handling utilities
│ │ ├── circuit_breaker.py # Circuit breaker pattern
│ │ ├── internal_auth.py # Internal authentication
│ │ ├── log_sanitizer.py # Sink-level log sanitization
│ │ ├── uspto_shared_rate_limiter.py # Cross-process shared rate limiter
│ │ ├── structured_logging.py
│ │ └── security_logger.py # Security event logging
│ ├── services/
│ │ ├── fpd_service.py # Core business logic layer
│ │ └── document_extraction.py # pypdf -> Mistral OCR -> Docling pipeline
│ ├── ui/ # MCP App HTML view resources
│ └── util/
│ ├── database.py
│ ├── identity.py # Per-caller viewer keys for the downloads page
│ └── secure_logger.py # Secure logging functionality
├── deploy/
│ ├── linux_setup.sh # Linux deployment script
│ ├── windows_setup.ps1 # PowerShell deployment script
│ ├── manage_api_keys.ps1 # API key management utilities
│ ├── Validation-Helpers.psm1 # PowerShell validation module
│ └── Validation-Helpers.sh # Bash validation helpers
├── docs/
│ └── CONTENT_PROVENANCE.md # Retrieved-text handling / provenance posture
├── tests/ # Automated pytest suite (see tests/README.md)
│ ├── conftest.py # mock_runtime fixture (mocked client, real service layer)
│ ├── test_basic.py # Core functionality test
│ ├── test_integration.py # Live-API tests (skip without USPTO_API_KEY)
│ ├── test_injection_scan.py # Runtime injection-scan unit + wiring tests
│ ├── TEST_SUITE.md # Manual end-to-end suite
│ └── README.md # Testing documentation
├── reference/
│ ├── Document_Descriptions_List.csv
│ ├── FinalPetitionDecisions_swagger.yaml
│ ├── petition-decision-schema.json
│ └── README.md
├── documentation_photos/ # Visual documentation
├── pyproject.toml # Package configuration
├── README.md # This file
├── INSTALL.md # Comprehensive installation guide
├── USAGE_EXAMPLES.md # Function examples and workflows
├── CUSTOMIZATION.md # Field configuration and optimization guide
├── PROMPTS.md # Prompt templates documentation
├── SECURITY_GUIDELINES.md # Security best practices
├── SECURITY_SCANNING.md # Automated secret detection guide
└── LICENSE # MIT License🔍 Troubleshooting
Common Issues
API Key Issues
For Claude Desktop: API keys in config file are sufficient
For test scripts: Environment variables must be set
Setting USPTO API Key:
Windows Command Prompt:
set USPTO_API_KEY=your_keyWindows PowerShell:
$env:USPTO_API_KEY="your_key"Linux/macOS:
export USPTO_API_KEY=your_key
Setting Mistral API Key (for OCR):
Windows Command Prompt:
set MISTRAL_API_KEY=your_keyWindows PowerShell:
$env:MISTRAL_API_KEY="your_key"Linux/macOS:
export MISTRAL_API_KEY=your_key
uv vs pip Issues
uv advantages: Better dependency resolution, faster installs
Mixed installation: Can use both
uv syncandpip install -e .Testing: Use
uv runprefix for uv-managed projects
Fields Not Returning Data
Cause: Field name not in YAML config
Solution: Edit
field_configs.yamlto include desired fields
Authentication Errors
Cause: Missing or invalid API key
Solution: Verify
USPTO_API_KEYenvironment variable or Claude Desktop config
MCP Server Won't Start
Cause: Missing dependencies or incorrect paths
Solution: Re-run setup script, restart all PowerShell windows, restart Claude Desktop (or other MCP Client) and verify configuration
If problems persist: Reset the MCP installation (see "Resetting MCP Installation" below)
Virtual Environment Issues (Windows Setup)
Symptom: "No pyvenv.cfg file" errors during
windows_setup.ps1Cause: Claude Desktop locks
.venvfiles when running, preventing proper virtual environment creationSolution:
Close Claude Desktop completely before running setup script
Remove
.venvfolder:Remove-Item ./.venv -Force -Recurse -ErrorAction SilentlyContinueRun
.\deploy\windows_setup.ps1again
Resetting MCP Installation
If you need to completely reset the MCP installation to run the Windows Quick installer again:
# Navigate to the project directory
cd C:\Users\YOUR_USERNAME\uspto_fpd_mcp
# Remove Python cache directories
Get-ChildItem -Path ./src -Directory -Recurse -Force | Where-Object { $_.Name -eq '__pycache__' } | Remove-Item -Recurse -Force
# Remove virtual environment
if (Test-Path ".venv") {
Remove-Item ./.venv -Force -Recurse -ErrorAction SilentlyContinue
}
# Remove database files (if any)
Remove-Item ./proxy_documents.db -Force -ErrorAction SilentlyContinue
Remove-Item ./petition_links.db -Force -ErrorAction SilentlyContinue
# Now you can run the setup script again
.\deploy\windows_setup.ps1Linux/macOS Reset:
# Navigate to the project directory
cd ~/uspto_fpd_mcp
# Remove Python cache directories
find ./src -type d -name '__pycache__' -exec rm -rf {} + 2>/dev/null || true
# Remove virtual environment and database files
rm -rf .venv
rm -f proxy_documents.db petition_links.db
# Run setup script again
./deploy/linux_setup.shGetting Help
Check the test scripts for working examples
Review the field configuration in
field_configs.yamlVerify your Claude Desktop configuration matches the provided templates in INSTALL.md
🛡️ Security & Production Readiness
Enhanced Error Handling
Retry logic with exponential backoff - Automatic retries for transient failures (3 attempts with 1s, 2s, 4s delays)
Smart retry strategy - Doesn't retry authentication errors or client errors (4xx)
Structured logging - Request ID tracking for better debugging and monitoring
Production-grade resilience - Handles timeouts, network issues, and API rate limits gracefully
Configurable timeouts - USPTO_TIMEOUT and USPTO_DOWNLOAD_TIMEOUT environment variables for API request tuning
Security Features
🔐 Windows DPAPI Secure Storage - API keys encrypted with Windows Data Protection API (user-specific encryption)
🛡️ Safe Logging with Auto-Sanitization - All logging automatically masks API keys, tokens, and sensitive data; file-based audit trail with rotation in
~/.uspto_fpd_mcp/logs/Environment variable API keys - No hardcoded credentials anywhere in codebase
Zero plain text API keys - Secure storage option eliminates API keys from Claude Desktop config files
Cross-platform security - Automatic fallback to environment variables on non-Windows systems
Secure test patterns - Test files use environment variables with fallbacks
Comprehensive .gitignore - Prevents accidental credential commits
Security guidelines - Complete documentation for secure development practices
Automated secret scanning - CI/CD and pre-commit hooks prevent API key leaks (detect-secrets)
20+ secret types detected - AWS keys, GitHub tokens, JWT, private keys, API keys, and more
Prompt injection detection - 70+ pattern detection system protects against AI-specific attacks
Baseline management - Tracks known placeholders while catching real secrets
Field name constants - Eliminates magic strings, reduces typo-based security issues
Content Provenance & Injection Annotation
Retrieved petition text is served verbatim — nothing is stripped or rewritten, because verbatim fidelity of legal text is the product. The defense against prompt-injection-shaped content inside retrieved documents is labeling and detection, not mutation:
Every successful
FPD_get_document_content_with_ocrresponse carries aprovenance_notestating that extracted/OCR text is quoted data from USPTO petition documents, never instructions to the consuming model, and that petitioner- or office-drafted characterizations should be presented as attributed positions.A detection-only scanner (
src/fpd_mcp/shared/injection_scan.py, stdlib only) checks extracted text for instruction-override, prompt-extraction, and encoding-evasion language plus invisible-Unicode steganography density. On a hit, the response gains aninjection_scanannotation naming the petition, document identifier, and pattern kind — never the matched text. The key is absent entirely when the text is clean.The server instructions include a matching provenance-posture paragraph, and the five petition search/details tools return structured metadata only (no free-text passages), so they carry no annotation.
Full write-up: docs/CONTENT_PROVENANCE.md.
This runtime layer is separate from the commit-time .security/ codebase
scanners described in SECURITY_SCANNING.md.
Request Tracking & Debugging
All API requests include unique request IDs (8-char UUIDs) for correlation:
[a1b2c3d4] Starting GET request to petition/decisions/search
[a1b2c3d4] Request successful on attempt 1Documentation
SECURITY_GUIDELINES.md- Comprehensive security best practicesSECURITY_SCANNING.md- Automated secret detection and prevention guidetests/README.md- Complete testing guide with API key setupEnhanced error messages with request IDs for better support
📝 Contributing
Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass
Submit a pull request
📄 License
MIT License
⚠️ Disclaimer
THIS SOFTWARE IS PROVIDED "AS IS" AND WITHOUT WARRANTY OF ANY KIND.
Independent Project Notice: This is an independent personal project and is not affiliated with, endorsed by, or sponsored by the United States Patent and Trademark Office (USPTO).
The author makes no representations or warranties, express or implied, including but not limited to:
Accuracy & AI-Generated Content: No guarantee of data accuracy, completeness, or fitness for any purpose. Users are specifically cautioned that outputs generated or assisted by Artificial Intelligence (AI) components, including but not limited to text, data, or analyses, may be inaccurate, incomplete, fictionalized, or represent "hallucinations" (confabulations) by the AI model.
Availability: USPTO API and Mistral API dependencies may cause service interruptions.
Legal Compliance: Users are solely responsible for ensuring their use of this software, and any submissions or actions taken based on its outputs, strictly comply with all applicable laws, regulations, and policies, including but not limited to:
The latest Guidance on Use of Artificial Intelligence-Based Tools in Practice Before the United States Patent and Trademark Office (USPTO Guidance).
The USPTO's Duty of Candor and Good Faith (e.g., 37 CFR 1.56, 11.303), which includes a duty to disclose material information and correct errors.
The USPTO's signature requirements (e.g., 37 CFR 1.4(d), 2.193(c), 11.18), certifying human review and reasonable inquiry.
All rules regarding inventorship (e.g., each claimed invention must have at least one human inventor).
Legal Advice: This tool provides data access and processing only, not legal counsel. All results must be independently verified, critically analyzed, and professionally judged by qualified legal professionals.
Commercial Use: Users must verify USPTO and Mistral terms for commercial applications.
Confidentiality & Data Security: The author makes no representations regarding the confidentiality or security of any data, including client-sensitive or technical information, input by the user into the software's AI components or transmitted to third-party AI services (e.g., Mistral API). Users are responsible for understanding and accepting the privacy policies, data retention practices, and security measures of any integrated third-party AI services.
Foreign Filing Licenses & Export Controls: Users are solely responsible for ensuring that the input or processing of any data, particularly technical information, through this software's AI components does not violate U.S. foreign filing license requirements (e.g., 35 U.S.C. 184, 37 CFR Part 5) or export control regulations (e.g., EAR, ITAR). This includes awareness of potential "deemed exports" if foreign persons access such data or if AI servers are located outside the United States.
LIMITATION OF LIABILITY: Under no circumstances shall the author be liable for any direct, indirect, incidental, special, or consequential damages arising from use of this software, even if advised of the possibility of such damages.
USER RESPONSIBILITY: YOU ARE SOLELY RESPONSIBLE FOR THE INTEGRITY AND COMPLIANCE OF ALL FILINGS AND ACTIONS TAKEN BEFORE THE USPTO.
Independent Verification: All outputs, analyses, and content generated or assisted by AI within this software MUST be thoroughly reviewed, independently verified, and corrected by a human prior to any reliance, action, or submission to the USPTO or any other entity. This includes factual assertions, legal contentions, citations, evidentiary support, and technical disclosures.
Duty of Candor & Good Faith: You must adhere to your duty of candor and good faith with the USPTO, including the disclosure of any material information (e.g., regarding inventorship or errors) and promptly correcting any inaccuracies in the record.
Signature & Certification: You must personally sign or insert your signature on any correspondence submitted to the USPTO, certifying your personal review and reasonable inquiry into its contents, as required by 37 CFR 11.18(b). AI tools cannot sign documents, nor can they perform the required human inquiry.
Confidential Information: DO NOT input confidential, proprietary, or client-sensitive information into the AI components of this software without full client consent and a clear understanding of the data handling practices of the underlying AI providers. You are responsible for preventing inadvertent or unauthorized disclosure.
Export Controls: Be aware of and comply with all foreign filing license and export control regulations when using this tool with sensitive technical data.
Service Compliance: Ensure compliance with all USPTO (e.g., Terms of Use for USPTO websites, USPTO.gov account policies, restrictions on automated data mining) and Mistral terms of service. AI tools cannot obtain USPTO.gov accounts.
Security: Maintain secure handling of API credentials and client information.
Testing: Test thoroughly before production use.
Professional Judgment: This tool is a supplement, not a substitute, for your own professional judgment and expertise.
By using this software, you acknowledge that you have read this disclaimer and agree to use the software at your own risk, accepting full responsibility for all outcomes and compliance with relevant legal and ethical obligations.
Note for Legal Professionals: While this tool provides access to patent research tools commonly used in legal practice, it is a data retrieval and AI-assisted processing system only. All results require independent verification, critical professional analysis, and cannot substitute for qualified legal counsel or the exercise of your personal professional judgment and duties outlined in the USPTO Guidance on AI Use.
🔗 Related Links
💝 Support This Project
If you find this USPTO Final Petition Decisions MCP Server useful, please consider supporting the development! This project was developed during my personal time over many hours to provide a comprehensive, production-ready tool for the patent community.

Your support helps maintain and improve this open-source tool for everyone in the patent community. Thank you!
Acknowledgments
USPTO for providing the Final Petition Decisions API
Model Context Protocol for the MCP specification
Claude Code for exceptional development assistance, architectural guidance, documentation creation, PowerShell automation, test organization, and comprehensive code development throughout this project
Claude Desktop for additional development support and testing assistance
Questions? See INSTALL.md for complete cross-platform installation guide or review the test scripts for working examples.
OAuth sign-in (optional)
Set FPD_AUTH_MODE=oauth to protect the HTTP endpoint with Google +
Microsoft sign-in (OAuth 2.1 with dynamic client registration — works as a
Claude.ai / Claude Desktop custom connector). Access is controlled by a local
SQLite user list; role admin unlocks the FPD_manage_users user-management
tool and its MCP App panel. The default (none) and STDIO are unchanged.
Full walkthrough: SSO_SETUP.md.
Admin-tool gating: The FPD_manage_users tool is registered only when
FPD_ENABLE_USER_MANAGEMENT=true (default off). In OAuth mode, the tool is
additionally gated behind the fpd:admin scope. Important: OAuth
deployments must set FPD_ENABLE_USER_MANAGEMENT=true in the compose/systemd
config or the admin tool will not appear in the tool list, even if the user
has admin role.
Internal authentication split: Headless clients (internal gateway services) send
Authorization: Bearer <token> to bypass the browser OAuth flow. The plain
FPD_AUTH_INTERNAL_TOKEN grants fpd:user scope only (read access); admin
operations require the separate FPD_AUTH_INTERNAL_ADMIN_TOKEN, which grants
both fpd:user and fpd:admin. Most deployments should leave the admin
token unset unless you have a machine caller that needs user management.
Shared USPTO rate limiting (multi-MCP deployments)
If you run all 4 USPTO MCPs (Citations, PFW, PTAB, FPD) as HTTP containers on the same box, serving multiple users, under one USPTO API key, each server's own in-process limiter can't see what the other 3 processes are doing — and USPTO's documented limits are per-key (burst=1, 4-15 req/sec depending on call type, plus weekly quotas), not per-process. Point all 4 containers at one bind-mounted directory and they share a single cross-process token bucket + a bounded pool of in-flight-request slots, arbitrated via POSIX file locks (crash-safe — a dead process's lock is released by the kernel). Single-MCP or STDIO deployments need nothing; the limiter is off unless the directory variable is set.
# docker-compose.yml (excerpt, all 4 USPTO MCP services)
volumes:
uspto-rate-limit: {}
services:
fpd-mcp:
volumes:
- uspto-rate-limit:/var/run/uspto-shared-rate-limit
environment:
USPTO_SHARED_RATE_LIMIT_DIR: /var/run/uspto-shared-rate-limit
USPTO_SHARED_RATE_LIMIT_RPS: "4" # default; total across ALL 4 MCPs
USPTO_SHARED_MAX_CONCURRENT: "2" # default; shared in-flight slotsOne token bucket and 2 concurrency slots are shared across every process mounting the directory — a heavier MCP naturally draws more of the budget under load, and a long PDF download occupies a slot for its full duration (not just connection setup), per USPTO's burst=1 guidance.
Available Tools
8 toolsFPD_get_document_content_with_mistral_ocrA
Extract full text from USPTO petition documents with intelligent hybrid extraction (PyPDF2 first, Mistral OCR fallback).
PREREQUISITE: First use fpd_get_petition_details to get document_identifier from documentBag. Auto-optimizes cost: free PyPDF2 for text-based PDFs, ~$0.001/page Mistral OCR only for scanned documents. MISTRAL_API_KEY is optional - without it, only PyPDF2 extraction is available (works well for text-based PDFs).
USE CASES:
Analyze petition legal arguments and Director's reasoning
Extract petition issues, CFR rules cited, statutory references
Detect patterns across multiple petitions (e.g., common denial reasons)
Correlate petition text with PTAB challenge strategies
Profile examiner behavior from supervisory review petitions
COST OPTIMIZATION:
auto_optimize=True (default): Try free PyPDF2 first, fallback to Mistral OCR if needed (70% cost savings)
auto_optimize=False: Use Mistral OCR directly (~$0.001/page)
Returns: extracted_content, extraction_method, processing_cost_usd, page_count
Example workflow:
fpd_get_petition_details(petition_id='0b71b685-...', include_documents=True)
fpd_get_document_content(petition_id='0b71b685-...', document_identifier='DSEN5APWPHOENIX')
Analyze extracted text for legal arguments, issues, and patterns
For document selection strategies and cost optimization, use FPD_get_guidance('cost').
| Name | Required | Description | Default |
|---|---|---|---|
| petition_id | Yes | ||
| document_identifier | Yes | ||
| auto_optimize | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the tool's behavior: the hybrid extraction process (PyPDF2 first, Mistral OCR fallback), cost optimization details (free vs. $0.001/page), dependency on MISTRAL_API_KEY, and return values (extracted_content, extraction_method, etc.). It also covers practical aspects like cost savings (70%) and fallback mechanisms, providing rich context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (PREREQUISITE, USE CASES, COST OPTIMIZATION, Example workflow) and front-loaded key information. However, it includes some redundancy (e.g., repeating cost details in multiple sections) and could be slightly more concise by integrating the example workflow more seamlessly. Overall, most sentences earn their place by adding value, but minor trimming is possible.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (hybrid extraction, cost optimization, prerequisites) and the absence of annotations, the description is highly complete. It explains the extraction process, cost implications, dependencies, use cases, and workflow integration. With an output schema present, it doesn't need to detail return values, but it still lists them (extracted_content, etc.) for clarity. This provides all necessary context for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It adds significant meaning for all parameters: petition_id is contextualized in the prerequisite and example workflow, document_identifier is explained as coming from documentBag via fpd_get_petition_details, and auto_optimize is detailed with its default (True) and effects on cost and extraction method. This goes beyond the schema's basic titles and types, clarifying how and why to use each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract full text from USPTO petition documents with intelligent hybrid extraction (PyPDF2 first, Mistral OCR fallback).' It specifies the verb ('extract'), resource ('USPTO petition documents'), and method ('hybrid extraction'), distinguishing it from sibling tools like FPD_get_document_download (which likely downloads files) and Get_petition_details (which retrieves metadata).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool, including prerequisites ('First use fpd_get_petition_details to get document_identifier from documentBag'), alternatives (e.g., using auto_optimize=False for direct OCR), and sibling tool references (e.g., 'For document selection strategies and cost optimization, use FPD_get_guidance('cost')'). It also lists specific use cases like analyzing legal arguments and detecting patterns, helping differentiate from other search or download tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
FPD_get_document_downloadA
Generate browser-accessible download URL for petition documents (PDFs) via secure proxy.
ALWAYS-ON PROXY (DEFAULT): Proxy server starts with MCP - download links work immediately.
Workflow:
fpd_get_petition_details(petition_id='uuid', include_documents=True) → get documentBag
fpd_get_document_download(petition_id='uuid', document_identifier='ABC123') → get download link
Provide download link to user
CRITICAL RESPONSE FORMAT - Always format with BOTH clickable link and raw URL:
📁 Download {DocumentType} ({PageCount} pages) | Raw URL: {proxy_url}
Why both formats?
Clickable links work in Claude Desktop and most clients
Raw URLs enable copy/paste in Msty and other clients where links aren't clickable
Document types:
Petition document: Original petition filed with USPTO
Decision document: Director's final decision
Supporting exhibits: Declarations, prior art, technical documents
Parameters:
petition_id: Petition UUID from search results
document_identifier: Document identifier from documentBag
proxy_port: Optional (defaults to FPD_PROXY_PORT env var or 8081)
generate_persistent_link: Generate 7-day persistent link (default: True)
True: Attempts persistent link via USPTO PFW MCP (works across MCP restarts)
False: Session-based link (works while MCP running, no PFW required)
| Name | Required | Description | Default |
|---|---|---|---|
| petition_id | Yes | ||
| document_identifier | Yes | ||
| proxy_port | No | ||
| generate_persistent_link | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure and does so effectively. It explains the proxy behavior ('ALWAYS-ON PROXY (DEFAULT): Proxy server starts with MCP'), link persistence options ('True: Attempts persistent link via USPTO PFW MCP', 'False: Session-based link'), and response format requirements ('CRITICAL RESPONSE FORMAT - Always format with BOTH clickable link and raw URL'). The only minor gap is lack of explicit mention about authentication requirements or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, proxy info, workflow, response format, document types, parameters) and front-loads the core purpose. While comprehensive, some sections like the detailed workflow and response format justification could be slightly more concise. Every sentence earns its place by adding value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, but with output schema), the description is remarkably complete. It covers purpose, workflow integration, behavioral details (proxy, persistence), parameter semantics, response formatting requirements, and document type context. The presence of an output schema means the description doesn't need to explain return values, and it focuses appropriately on the operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all parameters. It explains petition_id ('Petition UUID from search results'), document_identifier ('Document identifier from documentBag'), proxy_port ('Optional (defaults to FPD_PROXY_PORT env var or 8081)'), and generate_persistent_link with its two modes and implications. This goes well beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Generate browser-accessible download URL for petition documents (PDFs) via secure proxy.' This is a specific verb ('Generate') + resource ('download URL for petition documents') that clearly distinguishes it from sibling tools like Get_petition_details (which retrieves details) or FPD_get_document_content_with_mistral_ocr (which extracts content via OCR).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit workflow guidance: '1. fpd_get_petition_details(petition_id='uuid', include_documents=True) → get documentBag 2. fpd_get_document_download(petition_id='uuid', document_identifier='ABC123') → get download link 3. Provide download link to user.' It clearly positions this as step 2 in a sequence and distinguishes it from content extraction tools by focusing on URL generation rather than document analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
FPD_get_guidanceA
Get selective USPTO FPD guidance sections for context-efficient workflows.
🎯 QUICK REFERENCE - What section for your question?
🔍 "Find petitions by company/art unit" → tools 🚩 "Identify petition red flags" → red_flags 📄 "Download petition documents" → documents 🤝 "Correlate petitions with prosecution" → workflows_pfw ⚖️ "Analyze petition + PTAB patterns" → workflows_ptab 📊 "Citation quality + petition correlation" → workflows_citations 🏢 "Complete portfolio due diligence" → workflows_complete 📚 "Research CFR rules with Assistant" → workflows_assistant 🎯 "Ultra-minimal PFW + FPD workflows" → ultra_context 💰 "Reduce extraction costs" → cost
Available sections:
overview: Available sections and MCP overview (default)
workflows_pfw: FPD + PFW integration workflows
workflows_ptab: FPD + PTAB integration workflows
workflows_citations: FPD + Citations integration workflows
workflows_complete: Four-MCP complete lifecycle analysis
workflows_assistant: Pinecone Assistant + FPD research workflows
tools: Tool catalog, progressive disclosure, parameters
red_flags: Petition red flag indicators and CFR rules
documents: Document extraction, downloads, proxy configuration
ultra_context: PFW fields parameter + ultra-minimal workflows
cost: Cost optimization for document extraction
Context Efficiency Benefits:
80-95% token reduction (2-8KB per section vs 62KB total)
Targeted guidance for specific workflows
Same comprehensive content organized for efficiency
Consistent pattern with PFW MCP
| Name | Required | Description | Default |
|---|---|---|---|
| section | No | overview |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read-only operation (implied by 'get'), offers token reduction benefits (80-95%), and provides targeted guidance. However, it lacks details on potential errors, rate limits, or authentication requirements, which prevents a score of 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose and quick reference, but it includes repetitive or verbose elements, such as listing sections twice (in the quick reference and 'Available sections') and adding marketing-like benefits (e.g., 'Context Efficiency Benefits'). Some sentences could be trimmed without losing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no annotations, but with an output schema), the description is complete enough. It explains the purpose, usage, parameters, and benefits, and since an output schema exists, it doesn't need to detail return values. The description adequately covers what users need to know to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for its single parameter 'section', but the description compensates fully by listing all available sections (e.g., 'overview', 'workflows_pfw') with brief explanations. This adds significant meaning beyond the schema, making the parameter's purpose and options clear to users.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to 'Get selective USPTO FPD guidance sections for context-efficient workflows.' It specifies the verb ('get'), resource ('USPTO FPD guidance sections'), and benefit ('context-efficient workflows'). However, it doesn't explicitly distinguish this tool from its siblings (e.g., document-focused tools like FPD_get_document_content_with_mistral_ocr), which would be needed for a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives through the 'QUICK REFERENCE' section, which maps specific questions (e.g., 'Find petitions by company/art unit') to recommended sections (e.g., 'tools'). It also lists all available sections with brief descriptions, helping users select the appropriate one based on their workflow needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get_petition_detailsA
Get complete details for a specific petition by petition ID (UUID).
⚠️ CRITICAL: Proxy URLs in documentBag require proxy server to be running! MANDATORY WORKFLOW when include_documents=True:
Call fpd_get_petition_details(petition_id=X, include_documents=True)
Call fpd_get_document_download(petition_id=X, document_identifier=DOC1) - starts proxy
NOW provide all document download links to user - proxy is ready
Use for: Deep dive into specific petition, document metadata access, full legal context review.
Returns:
All petition fields (no filtering)
Document metadata if include_documents=True (file names, page counts, identifiers)
Full legal context (all issues, CFR rules, statutes cited)
Complete timeline (petition filed → decision issued)
Document access:
Use documentIdentifier from documentBag with fpd_get_document_download for browser access
Typical documents: Petition PDF, Decision PDF, supporting exhibits
Parameters:
petition_id: Petition decision record identifier (UUID from search results)
include_documents: Include documentBag with file metadata (default True)
| Name | Required | Description | Default |
|---|---|---|---|
| petition_id | Yes | ||
| include_documents | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes critical behavioral traits: the proxy requirement for document URLs, the mandatory workflow steps when include_documents=True, and what the tool returns (petition fields, document metadata, legal context, timeline). However, it doesn't mention error conditions, rate limits, or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (critical warnings, workflow, use cases, returns, document access, parameters). Every sentence adds value, though the workflow section is somewhat lengthy. It's appropriately sized for a complex tool with critical dependencies.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (document access workflow, proxy requirement) and the presence of an output schema, the description is mostly complete. It covers purpose, usage, critical behaviors, and parameter semantics. The output schema handles return value details, so the description appropriately focuses on workflow and context rather than repeating output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for the schema's lack of parameter documentation. It adds meaningful semantics: petition_id is 'Petition decision record identifier (UUID from search results)' and include_documents 'Include documentBag with file metadata (default True).' This provides crucial context about parameter sources and effects, though it could elaborate more on the documentBag structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get complete details for a specific petition by petition ID (UUID).' It specifies the verb ('Get'), resource ('petition details'), and scope ('complete details for a specific petition'), distinguishing it from search-oriented sibling tools like Search_petitions_balanced.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Use for: Deep dive into specific petition, document metadata access, full legal context review.' It also distinguishes it from search tools by specifying it's for a 'specific petition' and outlines a mandatory workflow for document access, clearly indicating when to use sibling tools like fpd_get_document_download.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search_petitions_balancedA
Balanced search for Final Petition Decisions with comprehensive fields (10-20 results).
Balanced tier convenience parameters (14 total) - adds 5 advanced filters to minimal tier.
Use for: Detailed petition analysis after minimal search, cross-referencing with PFW/PTAB data, analyzing petition types and legal context. Returns: 18 key fields including petition type, art unit, technology center, prosecution status, legal issues, CFR rules cited, statutes cited, entity status, and invention title.
All Minimal Parameters (9) - same as Search_petitions_minimal:
applicant_name,application_number,patent_numberdecision_type,deciding_officepetition_date_start/end,decision_date_start/end
Additional Balanced Parameters (5):
petition_type_code: Petition type (e.g., '551' = revival, '182' = restriction)art_unit: Art unit number (e.g., '2128') - enables PFW cross-referencetechnology_center: Tech center (e.g., '21', '2100')prosecution_status: Status (e.g., 'During examination', 'Patented Case')entity_status: Entity type (e.g., 'Small', 'Large', 'Undiscounted')
Examples:
# Revival petitions (type 551) that were denied
fpd_search_petitions_balanced(petition_type_code="551", decision_type="DENIED", limit=20)
# Complex combination for quality analysis
fpd_search_petitions_balanced(
art_unit="2128", petition_type_code="551",
decision_type="DENIED", prosecution_status="During examination", limit=20
)Progressive Disclosure Workflow:
Discovery: fpd_search_petitions_minimal(decision_type='DENIED', limit=100)
User selects interesting petitions
Analysis: fpd_search_petitions_balanced with advanced filters (art_unit, petition_type_code)
Cross-reference: Use art_unit with PFW, use patentNumber with PTAB
Cross-MCP Integration:
applicationNumberText -> pfw_search_applications_minimal with fields parameter for targeted data
patentNumber -> ptab_search_proceedings_minimal(patent_number=X)
groupArtUnitNumber -> pfw_search_applications_minimal(art_unit=X, fields=[...])
firstApplicantName -> Match parties across PFW/PTAB MCPs
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| limit | No | ||
| offset | No | ||
| applicant_name | No | ||
| application_number | No | ||
| patent_number | No | ||
| decision_type | No | ||
| deciding_office | No | ||
| petition_date_start | No | ||
| petition_date_end | No | ||
| decision_date_start | No | ||
| decision_date_end | No | ||
| petition_type_code | No | ||
| art_unit | No | ||
| technology_center | No | ||
| prosecution_status | No | ||
| entity_status | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a search operation (implied read-only), returns 10-20 results with 18 key fields, and provides specific examples of how to use parameters. However, it doesn't mention potential limitations like rate limits, authentication requirements, or error conditions, which keeps it from a perfect score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, usage, parameters, examples, workflow, integration) and uses bold formatting effectively. While comprehensive, it's somewhat lengthy; every sentence serves a purpose, but some redundancy exists (e.g., repeating parameter lists in different sections). The front-loaded purpose and usage are clear, making it efficient for understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (17 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is remarkably complete. It explains the tool's purpose, when to use it, all parameters with semantics, provides examples, outlines a workflow, and describes cross-tool integration. The output schema handles return values, so the description appropriately focuses on usage context rather than output details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 17 parameters, the description fully compensates by providing detailed parameter information. It clearly lists all 14 parameters (grouped as 9 minimal and 5 balanced), explains what each represents with examples (e.g., 'petition_type_code: Petition type (e.g., '551' = revival, '182' = restriction)'), and includes practical code examples showing parameter usage, adding substantial value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'balanced search for Final Petition Decisions with comprehensive fields (10-20 results)', specifying both the action (search) and resource (Final Petition Decisions). It explicitly distinguishes from the sibling 'Search_petitions_minimal' by explaining this adds 5 advanced filters to the minimal tier, making the differentiation clear and specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Use for: Detailed petition analysis after minimal search, cross-referencing with PFW/PTAB data, analyzing petition types and legal context.' It also includes a 'Progressive Disclosure Workflow' section that shows how this tool fits into a sequence with the minimal search tool, and mentions cross-MCP integration alternatives, giving comprehensive usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search_petitions_by_applicationA
Get all petition decisions for a specific application number.
Use for: Complete petition history, red flag identification, cross-referencing with PFW prosecution timeline.
Examples:
Basic petition check: fpd_search_petitions_by_application(application_number="17896175")
With documents: fpd_search_petitions_by_application(application_number="17896175", include_documents=True)
Red flag analysis:
Multiple petitions → Difficult prosecution (missed deadlines, examiner conflicts)
Revival petitions (37 CFR 1.137) → Application was abandoned
Examiner disputes (37 CFR 1.181) → Contentious relationship with examiner
Denied petitions → Unsuccessful arguments, potential prosecution quality issues
Cross-MCP integration:
Use pfw_search_applications_minimal with fields parameter for prosecution context
Compare petition dates with prosecution timeline (office actions, RCEs)
Identify if petitions correlate with examiner changes or specific prosecution events
If patented, check PTAB for post-grant challenges
Parameters:
application_number: USPTO application number (e.g., "17896175", "15/123,456")
include_documents: Include documentBag in response (default False)
| Name | Required | Description | Default |
|---|---|---|---|
| application_number | Yes | ||
| include_documents | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds significant behavioral context. It explains the tool's role in red flag analysis (e.g., multiple petitions indicate difficult prosecution) and cross-referencing workflows, though it doesn't explicitly mention rate limits, auth needs, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (Use for, Examples, Red flag analysis, Cross-MCP integration, Parameters). Every sentence adds value, though it's somewhat lengthy. The purpose is front-loaded, and information is efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is highly complete. It covers purpose, usage, examples, analysis context, integration with other tools, and parameter details, compensating for gaps in structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides detailed parameter semantics: application_number format examples ('17896175', '15/123,456') and include_documents behavior ('Include documentBag in response'). This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Get') and resource ('all petition decisions for a specific application number'), distinguishing it from siblings like Search_petitions_by_art_unit or Search_petitions_minimal. It precisely defines the scope of retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use for' section explicitly lists three scenarios: complete petition history, red flag identification, and cross-referencing. The 'Cross-MCP integration' section provides detailed alternatives and sequencing with other tools, including pfw_search_applications_minimal and PTAB checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search_petitions_by_art_unitA
Search petitions by art unit number for examiner/art unit quality analysis.
Use for: Art unit quality assessment, systematic petition patterns, examiner behavior analysis. Returns balanced field set for cross-referencing with PFW examiner data and PTAB challenge rates.
Example:
fpd_search_petitions_by_art_unit(art_unit="2128", limit=50)
fpd_search_petitions_by_art_unit(art_unit="2128", date_range="2020-01-01:2024-12-31")
Analysis patterns:
High petition frequency → Difficult examiners or challenging technology
Frequent revival petitions (37 CFR 1.137) → Docketing/procedural issues
Examiner disputes (37 CFR 1.181) → Communication/quality problems
Denied petitions → Weak prosecution practices
Cross-MCP integration:
applicationNumberText → pfw_search_applications_minimal with fields parameter for examiner names
Group petitions by examiner to identify individual patterns
patentNumber → PTAB MCP to correlate petition history with challenge success
Parameters:
art_unit: Art unit number (e.g., "2128", "3600")
date_range: Optional date range (format: "YYYY-MM-DD:YYYY-MM-DD")
limit: Maximum results (default 50, max 200)
| Name | Required | Description | Default |
|---|---|---|---|
| art_unit | Yes | ||
| date_range | No | ||
| limit | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining the tool's purpose, return format ('balanced field set for cross-referencing'), and integration patterns. It describes analysis patterns for interpreting results and cross-MCP workflows. However, it doesn't mention rate limits, authentication needs, or error conditions, leaving some behavioral aspects uncovered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, use cases, examples, analysis patterns, integration, parameters) and every sentence adds value. It's appropriately sized for a complex tool with integration needs, though slightly longer than minimal. The front-loaded purpose statement immediately clarifies the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, 0% schema coverage, no annotations, but with output schema), the description is remarkably complete. It covers purpose, usage guidelines, parameter semantics, analysis patterns, and cross-tool integration workflows. The output schema existence means return values don't need explanation, and the description provides everything else needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations: art_unit format examples ('2128', '3600'), date_range format specification ('YYYY-MM-DD:YYYY-MM-DD'), and limit details (default 50, max 200). It adds practical examples showing parameter usage, giving clear semantic meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches petitions by art unit number for examiner/art unit quality analysis, using specific verbs ('search', 'returns') and resources ('petitions', 'art unit number'). It distinguishes from siblings like Search_petitions_by_application by focusing on art unit rather than application, and from Search_petitions_balanced/minimal by specifying balanced field sets for cross-referencing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'Use for:' section explicitly lists specific scenarios (art unit quality assessment, systematic petition patterns, examiner behavior analysis). It provides clear alternatives by mentioning cross-MCP integration with pfw_search_applications_minimal and PTAB MCP, and distinguishes from siblings by focusing on art unit analysis rather than application-based or minimal searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search_petitions_minimalA
Ultra-fast discovery search for Final Petition Decisions (50-100 results).
NEW: Minimal tier convenience parameters (9 total) - no query syntax needed!
Use for: High-volume petition discovery, finding petitions by applicant, decision type, or date range. Returns: 8 essential fields - petition ID, application number, patent number, applicant name, decision type, petition mail date, decision date, deciding office.
Convenience Parameters:
applicant_name: Company/party name (e.g., 'Apple Inc.')application_number: Application number (e.g., '17896175')patent_number: Patent number if granted (e.g., '11788453')decision_type: Outcome (e.g., 'GRANTED', 'DENIED', 'DISMISSED')deciding_office: Office that decided (e.g., 'OFFICE OF PETITIONS')petition_date_start/end: Filing date range (YYYY-MM-DD)decision_date_start/end: Decision date range (YYYY-MM-DD)
Examples:
# Denied petitions for company
fpd_search_petitions_minimal(applicant_name="TechCorp Inc.", decision_type="DENIED", limit=50)
# Hybrid: keywords + convenience
fpd_search_petitions_minimal(query="machine learning", decision_type="DENIED", limit=50)Progressive Disclosure Workflow:
Use THIS TOOL for discovery with minimal params (50-100 results)
Present top results to user for selection
Use fpd_search_petitions_balanced for detailed analysis (10-20 selected)
Balanced tier adds: petition_type_code, art_unit, technology_center, prosecution_status, entity_status
Use fpd_get_petition_details for complete petition data
Cross-MCP Integration:
{QueryFieldNames.APPLICATION_NUMBER} -> Links to Patent File Wrapper MCP
{QueryFieldNames.PATENT_NUMBER} -> Links to PTAB MCP for post-grant challenges
Use balanced tier to get {QueryFieldNames.ART_UNIT} for PFW cross-reference
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| limit | No | ||
| offset | No | ||
| applicant_name | No | ||
| application_number | No | ||
| patent_number | No | ||
| decision_type | No | ||
| deciding_office | No | ||
| petition_date_start | No | ||
| petition_date_end | No | ||
| decision_date_start | No | ||
| decision_date_end | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a search tool (implied read-only), specifies result limits ('50-100 results'), mentions 'ultra-fast discovery' (performance hint), and details the return format ('8 essential fields'). However, it lacks explicit information on permissions, rate limits, or error handling, which are important for a tool with 12 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'Use for:', 'Returns:', 'Convenience Parameters:', 'Examples:', and 'Progressive Disclosure Workflow,' making it easy to scan. However, it includes some verbose elements like 'NEW: Minimal tier convenience parameters (9 total) - no query syntax needed!' and extensive cross-MCP integration details that, while informative, could be trimmed for conciseness without losing core value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (12 parameters, no annotations, but has output schema), the description is highly complete. It covers purpose, usage guidelines, parameter details, return fields, examples, workflow integration with sibling tools, and cross-MCP references. The output schema exists, so return values need not be explained in depth, and the description provides enough context for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It adds significant meaning beyond the schema by explaining all 12 parameters in the 'Convenience Parameters' section with examples (e.g., 'applicant_name': Company/party name), clarifying data types and usage. It also describes the 'query' parameter in examples and mentions 'limit' and 'offset' in the schema, though less explicitly, but overall provides comprehensive parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'Ultra-fast discovery search for Final Petition Decisions' with '50-100 results,' specifying the verb (search), resource (Final Petition Decisions), and scope (minimal tier). It distinguishes from siblings by emphasizing 'minimal tier convenience parameters' and explicitly mentions the sibling tool 'fpd_search_petitions_balanced' for detailed analysis, making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs. alternatives. It states 'Use for: High-volume petition discovery' and outlines a 'Progressive Disclosure Workflow' that directs users to start with this tool for discovery, then use 'fpd_search_petitions_balanced' for detailed analysis, and 'fpd_get_petition_details' for complete data. This includes clear when-to-use and alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
8 tool updates
- First observed
FPD_get_document_content_with_mistral_ocr - First observed
FPD_get_document_download - First observed
FPD_get_guidance - First observed
Get_petition_details - First observed
Search_petitions_balanced - First observed
Search_petitions_by_application - First observed
Search_petitions_by_art_unit - First observed
Search_petitions_minimal
TDQS
Scored across 8 tools
Each tool has a clearly distinct purpose with no overlap: search functions target different scopes (minimal, balanced, by application, by art unit), get_petition_details retrieves full details, get_document_content extracts text, get_document_download provides download links, and get_guidance offers contextual help. The descriptions explicitly differentiate their use cases, preventing misselection.
All tool names follow a consistent 'fpd_' prefix with snake_case and descriptive verb_noun patterns (e.g., fpd_search_petitions_minimal, fpd_get_petition_details). The naming is uniform across all eight tools, making them predictable and easy to understand at a glance.
With 8 tools, the server is well-scoped for USPTO petition analysis, covering search (4 variants), detail retrieval, document access (content and download), and guidance. Each tool serves a specific role in the workflow, from discovery to deep analysis, without redundancy or bloat.
The tool set provides complete coverage for the domain: multiple search options for discovery, detailed petition retrieval, document extraction and download for full access, and guidance for workflow optimization. There are no gaps in the CRUD/lifecycle for petition analysis, enabling agents to handle end-to-end tasks without dead ends.
Maintenance
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