Claude Code plugin available

Context for AI-ready data

A portable decision layer for data automation, so agents can make smarter decisions.

retention:7ypii.sensitiveaudit:required
pii.maskedgdpr.consentanonymize:90d
gdpr.art6freshness:15mml.approved
weight:0.95inherits:orders.amountstale_after:5m
no_exportsync.prohibitedaccess:09-17_ET

Data Ops for data agents

MCP-based tools for adding any type of context to data workflows. Author policies, set conditions, enrich tables, validate uses, and expose that context for agents.

Persistent Context

Business logic, data contracts, policies, lineage, and access rules as structured, machine-readable context.

Programmable Rules

Agents evaluate dynamic business rules instead of relying on hardcoded branches or human interpretation.

Versioned & Portable

Context layers are versioned, allowing policies to evolve over time and work cross-platform.

MCP Server
Processing queries with instructions
Active instructions:8
Decisions/min:~24
Instructions Library
PII protection rules
90-day retention policy
Access tier requirements
Audit trail requirements
Service tier checks
Live and queryable
Live Decision Stream

Programmable decision layer

Policy-as-code

Instructions and policies are versioned, operational metadata. Plan/apply workflows like infrastructure-as-code.

Version-controlled policies

Track every change with Git-style versioning and rollback capabilities.

Plan before application

Preview changes before deployment with impact analysis.

Collaborative review

Pull request workflow for governance changes with team approval.

policy-workflow
1
metatate plan

Preview changes

+ Add policy
~ Update scope
→ 2 tables
2
Review

Team validation

Steward approved
Security reviewed
3
metatate apply

Deploy to production

✓ Policy deployed (v2.1.0) • ✓ 2 tables updated • ✓ Audit log created

Data context, always available

The rules governing data disappear the moment it crosses a system boundary. Keep those rules attached so every downstream system knows exactly what it received and how it can be used.

Policy Identity

Identity

v2.1.0
Name:customer-data-ai-access-policy
Status:
Active
Created:2024-08-15
Last Modified:2025-03-10
Description:Policy restricting AI model training on customer data, allowing agentic use with PII encryption/anonymization
Scope:All customer PII datasets
Tags:
ai-governancepiicustomer-data
Owner:
Data Governance
Domain:
Customer

Data logic that evolves as conditions change

Restriction is a moving target. A boundary crossed, a threshold exceeded, a deadline passed. Structured context recognizes each condition and adapts.

Geographic Boundary
EU
Within approved region
Volume Threshold
800K records
Within 1M limit
Retention Deadline
45 days old
Within 90-day window
approved
All conditions met → Operation approved

AI systems act before they understand consequences

Evaluate any data operation, rewriting what needs to change and preserving what’s permitted, before execution completes.

Safe Query Generation
Churn analysis · support + customer data
SELECT *
FROM support_tickets s
JOIN customer_profiles c
ON s.customer_id = c.customer_id
METATATE RUNTIME ANALYSIS
s.message_text
Customer interaction data · restricted
RESTRICTED
c.email
Contains PII · high exposure risk
HIGH RISK
shared_analytics
BI & reporting
APPROVED
Risk: Low · all fields permitted
churn_model_training
ML training pipeline
REQUIRES CHANGE
Unsafe join detected · rewriting query...
customer_targeting
Marketing activation
RESTRICTED
Access blocked · contains restricted data

Every decision, explained and recorded

Capture the why behind every action, preserve full context, and create an audit trail for compliance.

Decision Lineage
Link every outcome to the query, instructions, policies, and context that shaped it.
Impact Visibility
See how decisions propagate and what changes downstream.
Verifiable Audit Trail
Tamper-evident records of decisions, modifications, and results built for compliance and investigation.

Persistent context, consistent decisions

Context that stays with your data ensures agents and systems make the same decisions across data workflows. No schema or semantic drift.

Without persistent context

Context stored in separate systems

Passive documentation

Static schema and policies

Rigid implementations that break with updates

With persistent context

Structured, machine-readable context attached to data

Operational, decision-grade instructions

Adaptive decisions at runtime

Deploy once, version and evolve over time

Flexible deployment

Native warehouse integrations or cross-platform agent coordination. Adapts to how you work.

Native Controls

Deep native integrations means governance built into data development.

Warehouse
Discover data
allow
Decide data
deny
Inspect data
allow
Authorize data
modify
Validate data
allow
Explain data
deny

Multi-agent

Multiple agents query the shared decision layer before accessing data. Structured context becomes the coordination protocol between agents.

MCP Server
MCP Server
Sales Agent
modify
Analytics Agent
deny
MCP Server
Support Agent
allow
Finance Agent
deny
MCP Server
MCP Server
Marketing Agent
allow
MCP Server
Integrations

Where Metatate meets your tools

Your decision layer is open at the MCP layer. Any agent that speaks the protocol reads it through the same Metatate tools. Claude Code does it first-class today.

Featured · Available now

Claude Code plugin

Eight slash commands and a Claude skill that keep Claude grounded in your decision layer. Author policies, inspect rules, authorize use, validate queries — without leaving the editor.

# In Claude Code
/plugin install metatate@metatate-claude-plugins
In Claude Code
> /metatate:authorize-use
Can analyst export sales.revenue_q3?
CONDITIONAL · mask customer_id
decision_id: d_8k2p9x
What you get
  • Slash commands across discover, decide, and audit
  • Claude skill that grounds responses in your decision layer
  • Schema-aware tool calls — no guessing from table names
  • Every decision audit-logged with a stable decision_id
Coming soon
LangGraphOpenClawCodex+ many more
Any MCP-aware client works. Request early access →

Confidence in agent autonomy

Context that moves with your data. Agents and systems can coordinate, enforce policies, and validate compliance without manual oversight.

01

Agents understand meaning, not just structure

Agents combine data from multiple sources understanding calculations, relationships, and constraints without human translation.

02

Scale domain expertise to every decision

Systems apply complex business logic consistently across workflows.

03

Policy enforcement without bottlenecks

Governance happens automatically because agents understand constraints.