sop-mcp 0.7.0

An MCP server for guiding users through Standard Operating Procedures

sop-mcp

An MCP server that guides AI agents through Standard Operating Procedures (SOPs) step by step, using RFC 2119 requirement levels. Instead of dumping an entire procedure on the agent (which it will summarize or skip), sop-mcp feeds one step at a time and forces actual execution.

Quick Install

Kiro	Cursor	VS Code

Or add manually to any MCP client:

{
  "mcpServers": {
    "sop-mcp": {
      "command": "uvx",
      "args": ["sop-mcp"]
    }
  }
}

Why?

Agents tend to summarize or skip steps when given a full procedure. Feeding steps one at a time forces actual execution. Each SOP becomes a dedicated MCP tool (run_sop) that the agent discovers naturally in its tool list.

How It Works

Agent calls run_sop(sop_name="sop_creation_guide")           → gets step 1 + instruction to execute
Agent executes step 1 actions
Agent calls run_sop(sop_name="sop_creation_guide", current_step=1, step_output="...")  → gets step 2
  ... repeats ...
Agent calls run_sop(sop_name="sop_creation_guide", current_step=8, step_output="...")  → completion signal

Every response includes an instruction field that tells the agent to act, not just read.

Tools

Tool	Description
publish_sop	Publish a new or updated SOP with automatic semver bumping
submit_sop_feedback	Submit improvement feedback for a specific SOP
run_sop	Step-by-step execution of any SOP, with sop_name parameter

Discovering SOPs

SOPs are exposed as MCP resources, so agents can list and read them before starting execution.

Method	URI	Description
list_resources	—	Returns all available SOPs with name, version, step count, and overview
read_resource	sop://{sop_name}	Read the full latest SOP markdown
read_resource	sop://{sop_name}?version=1.0	Read a specific version

For clients that don't support the MCP resource protocol, resources are also exposed as tools automatically via ResourcesAsTools.

This lets agents load the full SOP content upfront if needed — for example, to understand scope before committing to a multi-step run.

Creating SOPs

The built-in sop_creation_guide SOP walks agents through the full authoring process (call run_sop with sop_name="sop_creation_guide"):

1. Prepare — gather process info, identify stakeholders, collect existing docs
2. Structure — define metadata, scope, parameters, and document skeleton
3. Document — write detailed step-by-step instructions with decision points
4. Apply RFC 2119 — classify each action as MUST, SHOULD, or MAY
5. Enrich — add troubleshooting, best practices, examples, and references
6. Review — validate with SMEs and end users, run through the checklist
7. Finalize — incorporate feedback, publish via publish_sop, notify stakeholders
8. Maintain — schedule reviews, collect feedback, keep the SOP current

After publishing, restart the server to register the new SOP.

The step_output Field

The run_sop tool accepts an optional step_output string parameter (required when current_step >= 1). This is where the LLM submits its concrete work product for the completed step — specific values, names, dates, and details rather than summaries.

The server accepts step_output but does not store or process it. The field exists purely to force the LLM to produce detailed output that lands in the conversation's tool-call history. When all steps are complete, the LLM can reference its own step_output submissions to compile a comprehensive final document. State lives entirely in the LLM's conversation context, keeping the server stateless.

Request/response flow

# Step 1: Initial call — no step_output needed
Agent calls run_sop(sop_name="my_sop")
→ Response: Step 1 instruction

# Step 2: Agent submits step 1 output
Agent calls run_sop(
    sop_name="my_sop",
    current_step=1,
    step_output="Registration: VALID, Number: BRN-2024-0738291"
)
→ Response: Step 2 instruction

# Step 3: Agent submits step 2 output
Agent calls run_sop(
    sop_name="my_sop",
    current_step=2,
    step_output="Insurance: Hartford Financial, Policy: HFS-GL-4829173"
)
→ Response: Step 3 instruction

# Completion: Agent submits final step output
Agent calls run_sop(
    sop_name="my_sop",
    current_step=3,
    step_output="Compliance: All checks passed, Certificate: CC-2024-9182"
)
→ Response: Completion signal

At completion, the LLM uses its conversation history of step_output submissions to compile the final document with all concrete values.

Storage Configuration

By default, SOPs are stored in the bundled src/sops/ directory (ephemeral — data may be lost if the package cache refreshes).

To persist SOPs, set SOP_STORAGE_DIR:

{
  "mcpServers": {
    "sop-mcp": {
      "command": "uvx",
      "args": ["sop-mcp"],
      "env": {
        "SOP_STORAGE_DIR": "/path/to/my/sops"
      }
    }
  }
}

Bundled SOPs are automatically seeded into the custom directory on first run.

Writing an SOP

Every SOP markdown file must include:

• A level-1 heading (# Title)
• A **Document ID**: field (lowercase, underscores, min 3 words)
• A **Version:** field (semver)
• An ## Overview section
• One or more ### Step N: sections

Use RFC 2119 keywords (MUST, SHOULD, MAY) to define requirement levels.

Publishing

Call publish_sop with the full markdown content and a change_type:

Type	Effect	Example
major	Breaking change	1.2.0 → 2.0.0
minor	New feature	1.2.0 → 1.3.0
patch	Bugfix	1.2.0 → 1.2.1

New SOPs always start at v1.0.0.

SOP Naming Convention

Element	Format	Example
Folder name	lowercase, underscores	sop_creation_guide
Document ID	same as folder name	sop_creation_guide
Tool name	run_sop with sop_name= folder name	run_sop(sop_name="sop_creation_guide")
Version file	v + semver	v1.0.0.md

Development

Requires Python 3.10+ and uv.

uv sync              # install dependencies
uv run pytest        # run tests
uv run sop-mcp       # start server locally

Architecture

sequenceDiagram
    participant Agent as AI Agent<br/>(Claude/Kiro)
    participant Server as sop-mcp<br/>Server
    participant Storage as Storage Backend<br/>(configurable)

    Note over Agent,Storage: Initialize
    Agent->>Server: run_sop(sop_name="sop_creation_guide")
    Server->>Storage: Load latest version
    Storage-->>Server: SOP content
    Server-->>Agent: Step 1 + overview + instruction

    Note over Agent,Storage: Execute Steps
    loop For each step
        Agent->>Agent: Execute step actions
        Agent->>Server: run_sop(sop_name="sop_creation_guide", current_step=N, step_output="...")
        Server-->>Agent: Step N+1 + instruction
    end

    Note over Agent,Storage: Complete
    Agent->>Server: run_sop(sop_name="sop_creation_guide", current_step=last, step_output="...")
    Server-->>Agent: completion signal

License

MIT