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Build the query layer as a governed data product—not as an agent’s unrestricted SQL connection. Put a narrow, versioned tool interface between agents and distributed data; ground requests in approved business definitions; carry an end-user or workload identity to the data enforcement point; and keep source-system permissions decisive. Start with bounded, read-only tools, test policies in audit or inspection mode, and enable blocking only after the expected decisions appear in logs.
What the query layer should control
A federated query layer lets an agent retrieve information from more than one data system without requiring every source to be copied into one warehouse. Federation can simplify access, but connectivity alone does not decide whether a particular agent should see a particular row, column, or destination.
Design the layer to control both the agent’s available actions and the identity under which data is actually queried. Keep authorization at the source data platform, and use gateways and tool policies as additional boundaries—not substitutes for source permissions. This distinction is central whether sources are queried live, connected through managed connectors, or served through a lakehouse. Google Cloud’s borderless open data lakehouse architecture illustrates a governed serving path across distributed cloud data and live operational systems.
Reference architecture: seven layers
1. Data sources and native controls
Inventory warehouses, operational databases, object stores, and external data products before connecting an agent. For each source, record its identity model, authorization rules, row and column controls, masking, network boundary, data geography, and query interface. The source’s own enforcement point should still apply its normal permissions to federated reads.
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2. Catalog, lineage, and business meaning
Make it possible to discover what data means and who is accountable for it. Maintain descriptions, owners, sensitivity classifications, lineage, approved metrics, dimensions, relationships, and time semantics. Metadata helps an agent find objects; it does not, by itself, explain business meaning or authorize access. Snowflake describes Horizon Context as bringing catalog metadata, semantic definitions, and lineage context together, while query-time controls remain in the data layer.
3. Federation and query execution
Use source-native federation or managed connectors when they meet the required latency, freshness, residency, and governance constraints. Give each connector only the grants it needs. Document whether a source is queried live, replicated, virtualized, or mediated through a lakehouse; those choices affect freshness, availability, and the location where controls can be enforced.
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4. Agent-facing tool contract
Expose a small, versioned set of tools with unambiguous descriptions and typed parameter schemas. Define read/write boundaries, allowed destinations, timeouts, result limits, and predictable errors. For frequent tasks, prefer curated semantic views or parameterized operations over asking the model to invent SQL. If general SQL is genuinely needed, validate it and bound its cost and results.
The BigQuery MCP server documentation describes tools for metadata discovery and queries, along with authentication and required IAM permissions. Google states that execute_sql is the only MCP tool that is not read-only and documents a deny policy for restricting read-write MCP tool use. MCP standardizes a tool interface; it does not, on its own, establish a complete authorization model.
5. Identity and authorization
Choose explicitly whether calls act on behalf of an end user or under an autonomous workload identity. In the delegated model, the result should reflect the requesting person’s grants. In the autonomous model, use a dedicated, restricted identity with a named owner and approved purpose. In either case, make sure the effective identity reaches the system that enforces data permissions and can be traced in logs. Snowflake documents delegated and autonomous patterns, as well as identifying agent sessions so policies can restrict sensitive access even when an invoking user has broader rights; see Agent identity.
6. Gateway and policy enforcement
Apply allowlists for tools, MCP servers, and destinations, plus least-privilege grants and data-classification controls. Where appropriate, inspect prompts and tool responses for policy violations or leakage signals. A gateway can govern traffic that passes through it, but source authorization remains decisive for reads. Google Cloud’s agent governance guidance and Agent Gateway overview describe gateway ingress and egress paths, runtime access policies, optional inspection, and audit and observability controls.
7. Audit and operations
Capture enough context to reconstruct a decision: actor, agent, tool, destination, effective identity, policy decision, query identifier, timing, and result metadata. Monitor denials, unusual query volumes, expensive requests, policy changes, and potential leakage. Preserve traceability from source object through semantic definition to agent response where the platform supports it. Assign ownership for alerts, incident response, and changes to tools or policies before production rollout.
Build it in a controlled sequence
- Map data and access first. Classify the data, identify approved purposes and existing source controls, and document user identities, workload identities, network boundaries, and residency requirements. Do this before granting an agent connectivity.
- Select and document the identity model. Use delegated identity when answers must reflect the requester’s access. Use a dedicated workload identity for autonomous tasks, with a restricted role and accountable owner. Record how identity travels through the agent, connector, and source and how each hop is audited.
- Publish a small semantic contract. Start with a limited set of certified entities and metrics. Specify synonyms, relationships and join paths, time interpretation, freshness, owners, and sensitivity tags. Do not rely on catalog discovery alone to resolve ambiguous business terms.
- Expose minimal, read-only tools. Begin with metadata lookup and bounded query operations. Grant each required tool separately. If SQL execution is exposed, validate queries, deny mutation statements, and set cost or result limits and timeouts while retaining native platform permission checks.
- Constrain routes and destinations. Allow only approved servers and data destinations. If traffic uses a gateway, verify both the agent-to-gateway ingress path and the gateway-to-tool egress path, including the identity and policy applied at each point.
- Test policies in audit or inspection mode. Exercise permitted and forbidden cases, then inspect logs for the expected identity, destination, and decision. Google’s governance guidance documents a progression from dry-run or inspection settings to enforcement after log review.
- Enable enforcement after validation. Turn on blocking only after test cases pass and logs show the intended outcomes. Alert on repeated denials, privilege expansion, unexpected destinations, anomalous volumes, and changes to semantic definitions.
- Revalidate changes and treat writes separately. Repeat policy tests after connector, model, agent, or policy updates. If writes are required, make them a separate product decision: scope them to approved procedures or sandbox resources and add approval and idempotency controls rather than enabling writes by default.
How to choose among platform approaches
Compare designs against the same requirements rather than assuming that a federation feature or an MCP endpoint supplies governance automatically. The following review axes are useful whether you are evaluating one platform, combining platforms, or building a custom tool service.
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| Design axis | Questions to answer |
|---|---|
| Source coverage and federation | Which analytical and operational sources can be queried? Is access live, virtualized, replicated, or mediated through a lakehouse? What latency and freshness do users need? |
| Identity propagation | Can a connector pass a user identity, or does it use a workload identity? Can both be audited? What happens to access when a user loses a grant? |
| Enforcement location | Which decisions occur at the gateway, connector, catalog, and query engine? Can the source enforce row and column restrictions? |
| Semantic quality | Are metrics and relationships centrally versioned and reused, or repeated in prompts? Can owners certify, update, and retire definitions? |
| Tool surface | Are tools read-only by default? Can permissions be granted per tool? Are query cost, result size, and execution time bounded? |
| Operations | Are audit logs, traces, lineage, and denial reasons available? Who handles incidents, alerts, and policy changes? |
| Deployment constraints | Does the design meet residency, network isolation, compliance, cloud, and existing platform requirements? |
What the documented platform examples offer
These examples show implementation patterns, not a ranking or endorsement. Feature availability, regional support, service tiers, and security requirements can differ by account and deployment; verify them in the product documentation that applies to your environment.
| Platform example | Documented capabilities relevant to the design | Design point to verify |
|---|---|---|
| Google Cloud | The BigQuery remote MCP server documents authentication, required IAM permissions, metadata and query tools, and a deny policy for read-write MCP tool use. Google’s agent governance materials cover dry-run and inspection-only policy modes, log review, enforcement, and Agent Gateway paths. Its borderless lakehouse architecture is an example of serving distributed data through a governed analytics path. | Confirm the effective identity and IAM permissions for every tool and destination. Verify the gateway’s ingress and egress policy path and the behavior of any write-capable tool. |
| Snowflake | Horizon Context brings catalog metadata, semantic definitions, and lineage context together. Agent identity supports identifying agent sessions and restricting their access. Cortex Agents can combine structured queries through semantic views with unstructured retrieval through Cortex Search. The managed MCP server documents separate tool permissions and OAuth choices. | Confirm that source-side role, masking, and row-access policies remain in effect. Snowflake’s managed MCP documentation says Cortex Analyst supports semantic views, not semantic models; check that this matches the semantic assets your design uses. |
Relevant documentation: BigQuery MCP server, Google Cloud agent governance, Agent Gateway, Horizon Context, Agent identity, Cortex Agents, and Snowflake-managed MCP server.
Quick Recap
Common design failures to avoid
- Treating an MCP connection as an authorization system. The protocol gives agents a standardized way to call tools; it does not guarantee that the right user identity or source permissions are applied.
- Putting access rules only in prompts or a gateway. Prompts are not an enforcement boundary, and a gateway governs only traffic routed through it. Keep decisive data permissions at the source.
- Letting agents infer business definitions. Without certified metrics, join paths, and time semantics, technically valid queries can still answer the wrong business question.
- Using one broad identity for every agent and user. This obscures who was entitled to a result and makes revocation and audit harder. Select delegated or autonomous identity deliberately.
- Exposing broad SQL or writes before proving controls. Begin with bounded reads, make query limits explicit, and give write access a separate approval and control design.
- Enforcing policies before reviewing their outcomes. Dry-run or inspection logs help reveal identity and policy mismatches before a blocking policy disrupts a legitimate workload.
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