DCP is an open standard for embedding AI operating instructions directly into documents. A DCP block sits at the top of a document and tells any AI tool (Microsoft 365 Copilot, ChatGPT, Claude, GitHub Copilot) what the document is, who it's for, what to check, and what standards to apply.
The block is plain text. It works in Word, Google Docs, markdown, or anything else that holds text. There's nothing to install.
When lawyers use AI with a document, they spend time explaining context: what kind of document it is, what checklist to apply, what tone to use, what to watch out for. That context is usually typed once, used once, and lost. It never gets shared with colleagues. The checklists and instincts that make an experienced lawyer valuable stay locked in individual heads. Governance standards and centralized policies get lost in the shuffle. DCP ensures this all travels with the document.
A DCP block is a structured section at the top of a document. Here's what one looks like in an NDA:
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DCP — DOCUMENT CONTEXT PROTOCOL
Document Type: Non-Disclosure Agreement
Audience: External counsel
Jurisdiction: Delaware
Confidentiality: Highly confidential
Review Checklist:
□ Verify mutual confidentiality obligations
□ Check definition of "Confidential Information" for overbreadth
□ Flag non-compete clauses exceeding 12 months
□ Confirm carve-outs for independently developed information
□ Ensure termination provisions include survival period
□ Verify governing law matches specified jurisdiction
Drafting Standards:
- Formal contractual tone
- Define all capitalized terms on first use
- Include standard boilerplate (severability, waiver, entire agreement)
- Number all sections and subsections
Policy Check: Before reviewing this document, verify that the
embedded policy is current if a policy server is available.
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When someone opens this document and asks AI to "review section 3" or "draft the indemnification clause," the AI reads the DCP block and applies the right context without being prompted.
The document carries its own instructions. AI applies the appropriate review criteria. The review checklist acts as built-in quality control: AI checks for missing provisions and flags non-standard terms against the team's actual standards, not generic ones. The team can centrally manage these underlying standards across a huge organization, at scale.
When you share the document, you share the expertise. Anyone using AI to work from a DCP-enabled template gets the benefit of senior-level review criteria from day one. The institutional knowledge that usually stays in people's heads ("always check for this in a DPA," "flag this pattern in vendor agreements") gets encoded in the document itself.
Because DCP blocks are plain text, they work with any AI tool that reads document content. No vendor dependency, no integration work.
DCP blocks are self-contained by design. But policies change, and when a team updates its centralized NDA checklist to add a new regulatory requirement, that update only reaches documents created after the change.
Three optional fields handle this: Policy Source, Policy Version, and Policy As-Of. These record where the embedded policy came from and when it was last synced. They're metadata, not dependencies. If an AI tool can reach the policy source, it can check whether the document's policy is current and alert the user. If it can't, the embedded DCP block works exactly as it always has.
The canonical policies themselves live in standalone .dcp files maintained centrally by the team. Batch propagation tooling can scan a document library, identify stale DCP blocks, and update them, with scoping rules that distinguish between drafts (update freely), documents under review (flag first), and executed agreements (leave alone). Document-specific additions (prefixed with "Additional") are never overwritten.
See the specification for full details on policy governance, layered DCP blocks, and batch propagation.
Pick a template from the templates/ directory that matches your document type. Customize the review checklist and drafting standards for your own priorities. Start your document below the DCP block. When you use any AI tool with the document, the DCP block informs the AI's behavior automatically.
For team-wide adoption, save customized templates as Word templates (.dotx) in your shared template library. When team members create new documents from these templates, the DCP blocks are already in place.
| Template | Description |
|---|---|
| Non-Disclosure Agreement | Mutual and unilateral NDA review and drafting |
| Data Processing Agreement | GDPR-aligned DPA with Article 28 checklist |
| Legal Memo | Internal legal analysis and recommendations |
| Executive Brief | High-level summaries for leadership decision-making |
| Privacy Review | Product and feature privacy assessments |
| Contract Review | Third-party agreement review and redlining |
| Vendor Security Assessment | Vendor risk evaluation and security review |
| Template | Description |
|---|---|
| Technical Specification | Engineering design documents and architecture proposals |
| Project Proposal | New project or initiative proposals for leadership approval |
| Incident Report | Post-incident documentation for outages, security events, and safety incidents |
| Policy Document | Organizational policies (IT, HR, security, compliance) |
| Compliance Audit | Regulatory and policy compliance audit reporting |
| RFP Response | Structured response to requests for proposal |
Pre-formatted Word (.docx) versions are available in templates/word/ for the most commonly used document types. Open them in Word, save as a Word template (.dotx) in your shared template library, and your team can start creating DCP-enabled documents immediately.
A Python script that checks whether a document's DCP block is well-formed. No dependencies beyond Python 3.
python tools/validate-dcp.py document.md
# Validate multiple files
python tools/validate-dcp.py templates/*.md
# Machine-readable output
python tools/validate-dcp.py --json document.md
# Exit code only (for CI pipelines)
python tools/validate-dcp.py --quiet document.md
The validator checks for correct delimiters, the required header line, required fields (Document Type and Audience), proper checklist notation, and structural issues like missing closing delimiters.
See the before/after comparison for a side-by-side look at what DCP changes in practice.
- Specification for the full block format, field definitions, and placement guidelines
- Customization guide for adapting templates to your team's standards
DCP is open to contributions. See CONTRIBUTING.md for guidelines.
DCP travels with your document. DCP-MCP propagates your policies and governance at scale and enables deviation tracking.
The dcp-mcp companion server exposes your DCP templates as live MCP resources. Connect any MCP-compatible AI client once, and your AI has authoritative, versioned access to every policy in your library, locally or across your whole team.