O'Process

AI-native process classification MCP Server. Query 2,325 processes and 3,910 KPIs from APQC PCF 7.4 + ITIL 4 + SCOR 12.0 + AI-era extensions.

Version: 0.3.0 | MCP Spec: 2025-11-25 | MUST: 11/11 | SHOULD: 12/12 | Coverage: 94.72%

What It Does

O'Process gives AI assistants (Claude, GPT, etc.) real-time access to enterprise process knowledge. Connect it as an MCP Server, then ask natural language questions — the AI will call the right tools automatically.

Core capabilities:

• Process Search — "采购流程有哪些?" → returns matching process nodes with hierarchy, description, and confidence score
• Process Tree Navigation — browse the 5-level taxonomy (L1 categories → L5 activities)
• KPI Recommendations — get metrics for any process node (name, unit, formula, direction)
• Role-Process Mapping — "HRBP manages which processes?" → ranked list with confidence scores
• Process Comparison — side-by-side diff of 2+ process nodes across all attributes
• Responsibility Document — generate complete job descriptions with provenance appendix

Why It Matters

Use Cases

Management Consulting — Process diagnostics. A manufacturing company's delivery cycle is 30% slower than competitors. Use search_process to locate SCOR Plan/Deliver/Make nodes, then get_kpi_suggestions to build a measurement framework.

HR Digital Transformation — Role-process mapping. CHRO needs to know what processes HR actually owns. Use get_process_tree on node 7.0 (Human Capital) to get the full L1→L4 hierarchy, then map_role_to_processes to map "HRBP" to standard processes.

Legal Due Diligence — Compliance audit. Cross-border M&A requires checking 12+ regulatory domains. Use search_process to locate relevant PCF nodes (corporate governance, tax, labor, environmental), then compare_processes to identify coverage gaps.

Internal Audit — KPI system design. Use get_kpi_suggestions for each process node, review coverage across efficiency/quality/cost/timeliness dimensions, identify missing metrics.

Quick Start

# Install
uv sync

# Run MCP Server (stdio — default)
uv run python -m oprocess.server

# Run with SSE transport
uv run python -m oprocess.server --transport sse --port 8000

# Run with streamable-http transport
uv run python -m oprocess.server --transport streamable-http --port 8000

Claude Desktop Configuration

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "oprocess": {
      "command": "uv",
      "args": ["run", "python", "-m", "oprocess.server"],
      "cwd": "/path/to/O-Process"
    }
  }
}

Tools

8 MCP tools with full input validation (Pydantic Annotated[..., Field(...)]):

All tools return ToolResponse JSON with result, provenance_chain, session_id, and response_ms.

Invalid inputs raise ToolError with descriptive messages. All tools are annotated with readOnlyHint and idempotentHint.

Prompts

3 guided prompt templates for common workflows:

Resources

6 MCP resources for direct data access:

Authentication

HTTP transports (SSE, streamable-http) support Bearer token authentication:

# Set API key
export OPROCESS_API_KEY="your-secret-key"

# Client requests must include:
#   Authorization: Bearer your-secret-key

When OPROCESS_API_KEY is not set, authentication is disabled (safe for stdio mode).

Environment Variables

No API key is required to run the server. All 8 tools work out of the box. Setting GOOGLE_API_KEY upgrades search_process and map_role_to_processes from text matching to semantic vector search.

Logging

Structured logging via Python logging module:

# Set log level (default: WARNING)
export LOG_LEVEL=INFO   # DEBUG, INFO, WARNING, ERROR

# Logs include: tool name, session ID, response time (ms)

Configuration

Server behavior can be tuned via [tool.oprocess] in pyproject.toml:

Rate limiting is enforced per-client via RateLimitMiddleware. Exceeding the limit returns HTTP 429.

Governance-Lite

Transparent governance layer (non-blocking):

• SessionAuditLog -- Append-only invocation log per session (idempotent via request_id)
• BoundaryResponse -- Structured fallback when semantic confidence is low (threshold: 0.45)
• ProvenanceChain -- Derivation trail attached to every tool response

Data Sources

Bilingual: Chinese (zh) + English (en).

Third-Party Attribution

APQC Process Classification Framework® (PCF) is an open standard developed by APQC, a nonprofit that promotes benchmarking and best practices worldwide. Used under APQC's royalty-free license for derivative works. To download the original PCF, visit apqc.org/pcf.

ITIL® is a registered trademark of PeopleCert group. This project references ITIL 4 practice names as industry-standard terminology. All descriptions are independently written and are not reproduced from ITIL publications.

SCOR® (Supply Chain Operations Reference) is a product of ASCM. This project references SCOR 12.0 process names as open-access industry-standard terminology. All descriptions are independently written.

Blueprint v1.0

The framework schema reserves five structural pillars for future enhancement:

These fields are present in schema.json as required empty defaults. Content will be populated in v2.0.

Development

# Install dependencies
uv sync

# Lint
ruff check .

# Test
pytest

# Full check (lint + test + benchmark)
ruff check . && pytest && pytest --benchmark-only

Project Structure

src/oprocess/
├── server.py              # FastMCP entry point (stdio/SSE/HTTP)
├── gateway.py             # ToolGatewayInterface + PassthroughGateway
├── auth.py                # Bearer token auth middleware
├── config.py              # pyproject.toml config loader
├── validators.py          # Input validation + sanitization
├── prompts.py             # 3 MCP prompt templates
├── tools/
│   ├── registry.py        # 7 tool registrations
│   ├── search.py          # search_process + map_role_to_processes
│   ├── resources.py       # 6 MCP resources
│   ├── export.py          # Responsibility document builder
│   ├── helpers.py         # Provenance + comparison utilities
│   ├── serialization.py   # ToolResponse → JSON
│   └── rate_limit.py      # Per-client rate limiter
├── governance/
│   ├── audit.py           # SessionAuditLog
│   ├── boundary.py        # BoundaryResponse
│   └── provenance.py      # ProvenanceChain
└── db/
    ├── connection.py       # SQLite + sqlite-vec connection
    └── queries.py          # All SQL queries

Tech Stack

• Runtime: Python 3.10+
• MCP Framework: FastMCP 2.x
• Validation: Pydantic 2.x (Annotated[..., Field(...)])
• Database: SQLite + sqlite-vec (optional vector search)
• Embeddings: gemini-embedding-001 (768-dim, optional — falls back to LIKE search)
• Packaging: uv + hatchling

License

MIT — applies to all source code and AI-era original content.

Third-party framework data (APQC PCF, ITIL, SCOR) is used under their respective licenses. See Third-Party Attribution for details.