A Technical Deep Dive into Reltio MCP Server Architecture and APIs.

Bridging LLMs and enterprise data with a secure, extensible, production-ready gateway.

The Model Context Protocol (MCP) emerged as a lightweight open standard to let LLMs and agentic systems call out to external tools and data stores safely and with structured context. Reltio’s MCP Server—offered both as a managed service (AgentFlow / AgentFlow MCP Server) and a developer edition—implements that pattern specifically for the Reltio Data Cloud, turning your unified, governed enterprise graph into a first-class, permission-aware data tool for AI agents. This post walks through the architecture, runtime components, API surfaces, security model, and practical usage patterns so engineers and architects can integrate agentic AI into MDM workflows with confidence. (modelcontextprotocol.io)

1 — Why an MCP server for enterprise MDM?

LLMs are powerful at reasoning and language, but they must be fed accurate, current, and governed context to make safe--and auditable--decisions in enterprises. An MCP server sitting in front of your MDM:

• Exposes resolved, trusted entity graphs and provenance in a form agents can consume in real time.
• Enforces access control, audit logging, and governance for every agent interaction.
• Provides a plugin-able tool surface so agents can ask domain-aware operations (e.g., “merge these two customer records”, “suggest a steward for this golden record”) rather than raw SQL or opaque payloads. (Reltio)

Reltio positioned its MCP Server to be that secure, policy-aware gateway—integrating Reltio’s entity model, record resolution, and stewardship workflows directly into agentic pipelines. It became generally available in mid-2025. (Reltio)

2 — High-level architecture

At a high level, Reltio’s MCP Server acts as an MCP-compliant gateway that translates MCP tool requests into calls against Reltio platform APIs and, where needed, to local/third-party services (e.g., model hosts). Key architectural pieces are:

1. MCP Front Door (REST / WebSocket transport):
   • Accepts MCP connections from LLMs/agent frameworks (clients may be gateways such as AgentFlow, Strands, or custom MCP clients).
   • Implements the MCP message semantics and tool invocation lifecycle. (docs.reltio.com)
2. Plugin/Tool Engine (Plugin-based core):
   • Tools implement domain operations (read entity, search matches, create stewardship tasks, perform merges, fetch lineage).
   • Plugins isolate logic, so new operations can be added without changing core server code. Developer edition and managed service both rely on this extensibility model. (GitHub)
3. Reltio Adapter Layer:
   • Translates plugin/tool calls into Reltio Data Cloud API calls, handling paging, query shaping, enriched responses (linked entities, attributes, confidence scores), and provenance metadata.
4. Security & Governance Layer:
   • OAuth2 / token validation (Reltio platform credentials), tenant and role checks, fine-grained permission checks, and detailed audit logging for every agent action.
5. Model / Host Integrations (optional):
   • Local or managed LLMs may be co-hosted or proxied (examples in docs show connecting local models like Ollama or external providers), enabling supervised model-assisted matching and suggestions. (docs.reltio.com)
6. Observability & Stewardship UI integration:
   • Telemetry and optional UI hooks to map agent-suggested changes to human stewardship workflows in Reltio.

Diagrammatically: Agent → MCP Client → Reltio MCP Server (plugins + adapters + auth) → Reltio Data Cloud APIs / Model hosts.

3 — Editions & deployment options

• Managed (AgentFlow / AgentFlow MCP Server): Reltio-hosted, production-grade, permission-aware service meant for rapid enterprise adoption with SLA, built-in integrations, and Reltio governance baked in. Ideal for teams that want a managed integration with enterprise support. (docs.reltio.com)
• Developer Edition / Open GitHub samples: Lightweight, plugin-based server for local testing, prototyping, and custom extensions. Useful when you want to experiment with custom tools, local LLM integration, or embed MCP into CI/automation. The project repository includes example clients and strands/agent clients. (GitHub)

4 — Core APIs and tool surfaces

Reltio’s MCP Server exposes two complementary APIs to integrate with agents and external clients:

A. MCP Tool Registry / Discovery

Agents first discover the set of tools available—each tool is a domain operation (e.g., get_entity, search_matches, create_steward_task). The registry provides:

• Tool names, descriptions, input schema, output schema, and usage hints.
• Permission metadata so clients can decide whether to surface a tool to a particular agent. (docs.reltio.com)

B. Tool Invocation / Execution

Tool calls follow MCP semantics: a tool is invoked with a structured payload, the server runs the plugin, and the tool returns structured results (or streaming partial responses when operations are long-running). Typical tool categories for MDM:

• Read / Query Tools: fetch entity by ID, fetch related records, search by attributes, fetch golden record with lineage & attribute confidence.
• Matching / Suggestion Tools: return candidate matches for de-duplication, each with similarity scores and contributing attributes.
• Action Tools: create/update/merge records, create stewardship tasks, apply data quality fixes (often gated and logged).
• Workflow Tools: create/reassign steward tasks, query stewardship queue status, approve/reject automated merges.

C. Reltio Platform API Adapter

Under the hood the tool implementations map to Reltio platform REST endpoints (entity GETs, advanced queries, delta feeds). The MCP Server assembles richer responses—embedding provenance, resolution metadata, and attribute confidence—so LLMs see not just raw fields but why those fields are authoritative. (docs.reltio.com)

5 — Authentication & Security model

Security is central for any agent-data integration. Reltio’s MCP Server enforces:

• OAuth2 / Client credentials: MCP clients authenticate using Reltio credentials or a configured OAuth flow. Tokens include tenant context and scopes.
• Tool-level authorization: Tools declare required scopes; the server enforces these before invoking the plugin.
• Audit trails and immutable logs: Every tool invocation, parameter set, and result is logged for compliance and post-hoc review.
• Least-privilege defaults: By design, tools that change data require explicit elevated scopes and explicit human approval flows (e.g., auto-suggested merges require steward approval unless a policy allows auto-merge). (docs.reltio.com)

6 — Typical integration patterns and flows

Entity resolution assistance (LLM-assisted matching):

1. Agent sends text/context describing potential duplicate or merge rationale.
2. Agent calls search_matches tool with candidate attributes.
3. MCP Server runs matching plugin (optionally augmenting with local LLM to weigh textual similarity), returns ranked candidates with confidence and explanation.
4. If a merge is chosen, agent can request create_merge_proposal → steward reviews in Reltio UI → apply merge via apply_merge.

Real-time augmentation for chatbots / assistants:

• A sales assistant agent retrieves a golden customer record (via get_entity) and supplies attribute provenance so the assistant can say “This billing address was last verified 3 days ago.” The MCP server ensures the assistant never exposes unauthorized attributes.

Automated stewardship agents:

• Periodic agents call find_stale_records, produce stewardship tasks, and place them into Reltio’s stewardship queue. Human stewards use Reltio’s UI to approve fixes. (Reltio)

7 — Developer ergonomics: plugins & clients

• Plugin model: Each tool is a plugin (node/python/etc.) that implements a standard interface. This makes adding a domain operation simple: implement input_schema, handler, and output_schema, and register the tool. Developer repo samples show environment variable configuration and connector wiring to Reltio credentials. (GitHub)
• Clients & samples: Reltio publishes example clients (e.g., Strands / Python clients) and docs for building custom MCP clients that can authenticate and call the AgentFlow MCP endpoints. These samples cover OAuth flows, request/response shapes, and example merge/resolve workflows. (GitHub)

8 — Example: calling a get_entity tool (curl-style pseudocode)

NOTE: adapt endpoints and tokens per your tenant and server docs. This is a conceptual example—see Reltio docs for exact schema.

# 1. Fetch OAuth token (client credentials)
curl -X POST "https://auth.reltio.example/token" \
  -d "grant_type=client_credentials&client_id=XXX&client_secret=YYY&scope=reltio.mcp" 

# 2. Invoke MCP tool (get_entity)
curl -X POST "https://mcp.reltio.example/tools/get_entity" \
  -H "Authorization: Bearer <ACCESS_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "entityId": "cust_12345",
    "include": ["goldenRecord","lineage","attributeConfidence"]
  }'

The response will include the unified entity payload plus provenance and resolution metadata the agent can use when answering or deciding. For full request/response schemas, consult the Reltio MCP docs. (docs.reltio.com)

9 — Observability, governance, and risk controls

• Explainability: Responses include why fields were chosen (provenance & confidence), which is crucial for downstream audit and human trust.
• Approval gates: Action tools that change data are subject to tenant policies—automatic vs human-approved merges are configurable.
• Monitoring: Tool usage, latency, and anomaly detection hooks are recommended so suspicious agent behavior (e.g., high-rate write attempts) triggers alerts.

10 — Best practices for architects & engineers

1. Model the UX for stewardship early. Agents should propose actions; humans should have efficient review workflows. Keep automated destructive operations rare and auditable.
2. Least privilege for agent identities. Use minimal scopes for agents; rotate client secrets and monitor tool-level usage.
3. Surface provenance & confidence to agents. If the LLM can see provenance, it will make fewer hallucinations and safer recommendations.
4. Start in dev with the developer edition. Prototype plugins locally (Reltio has sample repos) and test with local models before migrating to the managed MCP Server.
5. Design idempotent tools. Tool calls may be retried; ensure side-effect operations are idempotent or versioned. (GitHub)

11 — Where to look next (documentation & samples)

• Reltio MCP Server docs & “at-a-glance” pages (developer resources, API reference, custom client guides). (docs.reltio.com)
• Reltio blog posts and demos explaining AgentFlow and GA timeline (announced July 2025). (Reltio)
• Reltio GitHub repos with developer edition examples and sample clients (useful starting points for custom plugins and Strands clients). (GitHub)
• Model Context Protocol (MCP) specification and docs for general MCP semantics and client behavior. (modelcontextprotocol.io)

Conclusion

Reltio’s MCP Server is a practical realization of the promise of agentic AI for master data—bringing the structured, audited, and authoritative view of your enterprise graph to LLMs in a way that preserves governance and human oversight. Its plugin-based design, tight Reltio platform integration, and managed option let organizations stand up agentic workflows that are both powerful and compliant. For teams building AI-augmented MDM workflows—stewardship automation, LLM-assisted matching, or assistant UIs—MCP servers are a foundational piece that make agents useful and trustworthy in production.