Posted on Jul 13 • Edited on Jul 14
The fastest way to make an analytics agent impressive is to let it answer broad natural-language questions. The fastest way to make it dangerous is to give the same path export, mutation, or deployment authority.
Ship one narrow read-only slice first.
type MetricRequest = {
tenantId: string;
metric: "daily_active_users" | "activation_rate";
from: string;
to: string;
dimensions: Array<"plan" | "country">;
requestId: string;
};
type MetricResponse = {
definitionVersion: string;
dataThrough: string;
rows: Array<Record<string, string | number>>;
sourceQueryId: string;
warnings: string[];
};
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Validate dates, maximum range, dimensions, and metric names. Derive tenantId from authenticated server context rather than trusting the model. The query layer maps enums to pre-reviewed SQL or a semantic model; it never accepts model-generated SQL.
Make evidence travel end to end
The backend returns definition version, data freshness, query ID, and warnings. The tool adapter preserves them. The UI renders them beside the answer and offers a link to the source chart or query record.
Required UI states are loading, partial result, stale data, no data, forbidden, rate limited, cancelled, and evidence unavailable. “No rows” is different from “the warehouse failed.”
Cache only after authorization and canonicalization. Include tenant, metric, dimensions, date range, and definition version in the cache key. Do not cache user-level rows in shared infrastructure.
Test the seams
Write contract tests for unknown metrics, an unauthorized tenant, reversed dates, excessive range, unsupported dimensions, stale data, duplicate request ID, timeout, and a metric definition change during cache lifetime. Verify the browser cannot change tenant identity by editing tool arguments.
Only after the read path is observable and correctly authorized should you consider actions. Put writes in separate tools with separate credentials, previews, idempotency keys, and explicit approvals.
The public MonkeyCode repository describes model management, AI tasks, development environments, and team collaboration. It could be evaluated as a development workspace for this kind of integration, but this article does not claim an analytics or MCP integration with MonkeyCode.
Disclosure: I’m a MonkeyCode user, not affiliated with the project. The architecture is tool-independent; product context comes from public documentation.
Read-only is not a toy milestone. It is where the team proves identity, evidence, freshness, and failure semantics before side effects make every mistake more expensive.
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