MCP for data teams: let analysts query your APIs through AI
Turn your internal data and metrics APIs into MCP tools so analysts and stakeholders can self-serve answers in natural language — without new dashboards or SQL access.
Data teams spend a surprising amount of time answering the same ad-hoc questions: "what's MRR this month?", "how many signups yesterday?", "pull the retention for this cohort." If those numbers already live behind an internal metrics or analytics API, you can expose them as MCP tools — letting stakeholders self-serve answers in natural language, without new dashboards, SQL access, or another interruption to your day.
- →Wrap your metrics/analytics API as MCP tools so people ask questions in plain language.
- →It cuts the ad-hoc request queue without granting raw database access.
- →Read-only by design: expose query and report tools, nothing that mutates data.
- →The server enforces the same access scope as your API — no new data exposure.
- →Every query is logged, so you see what people actually ask for.
The ad-hoc question problem
Dashboards answer the questions you anticipated. The rest land in your queue as Slack pings. Each one is small, but together they fragment focus and slow the team down. Giving raw SQL access to everyone isn't the answer either — it's a governance and accuracy nightmare. MCP offers a middle path: a curated set of trusted, parameterized queries that anyone can run through an AI assistant.
What "self-serve" looks like with MCP
Instead of a stakeholder asking you for a number, they ask their AI client, which calls your metrics API through MCP and returns the answer with the definition you control. Because the tools wrap yourendpoints, the logic and definitions stay governed — "active user" means what your data team says it means, every time.
Expose queries, not the warehouse
The safe pattern is to expose read-only query and report tools — never write access, never arbitrary SQL. Each tool maps to a defined, reviewed endpoint:
getMetric
/metrics/{name}
listMetrics
/metrics
runReport
/reports/{id}
getCohort
/cohorts/{id}
This keeps definitions consistent and prevents the "everyone computes the metric differently" problem. The AI can only ask the questions you've already blessed.
Governance and access
- Same scope as your API — the server inherits whatever the credential it uses can see. No new data is exposed.
- Read-only — expose
GETquery and report operations; leave anything that changes data off. - Audit trail — every tool call is logged with its parameters, so you know which metrics are in demand.
That last point is a hidden benefit: the logs tell you what people repeatedly ask for, which is a roadmap for the dashboards and metrics worth investing in next.
Getting started
Point Cast at your metrics or analytics API's OpenAPI spec, enable the read tools, configure the credential the server should use, and share the MCP URL with your stakeholders. They connect it to their assistant and start asking questions — and your ad-hoc queue gets shorter.
Let stakeholders self-serve your metrics
Turn your analytics API into safe, read-only MCP tools.
Try Cast freeFrequently asked questions
Does this give people direct access to our database?
Won't people compute metrics inconsistently?
What if we don't have a metrics API, only a warehouse?
Can we see what people are asking?