Official
Google-published MCP server for databases — one Go binary speaks Postgres, MySQL, BigQuery, Spanner, AlloyDB, Snowflake, ClickHouse, MongoDB, Redis, and more.
database, multi-database, google, postgres
Best Analytics & Data MCP Servers in 2026
Analytics and data MCPs for AI agents: query BI warehouses, build dashboards, and explore metrics from Claude, Cursor, and ChatGPT — verified for 2026.
Top Analytics & Data MCPs
- 1.Google MCP Toolbox—Google-published MCP server for databases — one Go binary speaks Postgres, MySQL, BigQuery, Spanner, AlloyDB, Snowflake, ClickHouse, MongoDB, Redis, and more.
- 2.Grafana—Query dashboards, Prometheus/Loki datasources, alerts, and incidents from an AI agent — official.
- 3.MongoDB—Query and inspect MongoDB collections from Claude, Cursor, and VS Code.
Ranked by top-mcps.com — data verified Sep 2026.
About Analytics & Data MCP servers
The best MCP servers for analytics and data connect AI agents to the warehouses, dashboards, and metrics surfaces a team already runs — BigQuery, Snowflake, ClickHouse, Elasticsearch, Grafana, and the Google marketing stack around them. The good ones expose the workflow a senior analyst would follow: list datasets, introspect schemas, draft SQL, run it with a row cap, and explain the result in plain language. With a good MCP wired in, 'what was conversion last week versus the four weeks before' stops being a ticket and becomes a question.
Choose by where the data sits. For warehouse SQL, BigQuery (OAuth 2.1, stdio) stands out because it can dry-run a query and estimate cost before executing; Snowflake (api-key, around fifteen minutes to configure) handles cross-database joins inside one account; ClickHouse (api-key, official) covers product analytics and log-scale rollups. For files on disk or S3, DuckDB needs no credentials at all — stdio, two minutes, SQL straight over CSV and Parquet, the lowest-risk entry point in this category. For operational databases, MongoDB (official) drafts aggregation pipelines and MySQL runs read-only SELECTs. If you need several engines behind one server, Google MCP Toolbox is a single Go binary that speaks Postgres, MySQL, BigQuery, Spanner, and more, with allowed queries declared in a tools.yaml file so the agent only runs what you wrote down. For dashboards and incidents rather than raw SQL, Grafana queries Prometheus and Loki through one MCP, and Sentry (OAuth, three minutes) summarizes error trends. For marketing analytics, Google Analytics, Google Ads, and Google Search Console are all OAuth 2.1 over stdio — budget ten to fifteen minutes of Google Cloud console setup for each.
Common mistakes: connecting with credentials that hold warehouse write access (an agent asked to 'fix' a metric can UPDATE the table that feeds it), skipping a row limit so a SELECT over a billion-row events table runs to completion and lands on the bill, and exposing every dataset when only a few are governance-approved — the agent will happily join tables your team never reconciled. Start with DuckDB over a local export: no credentials, no cost, and you learn how the agent writes SQL. Then point your warehouse MCP — BigQuery or Snowflake — at one read-only schema, prove a dashboard-equivalent question end to end, and expand from there.
All Analytics & Data MCPs
14 MCPs ranked by popularity. Filter by attribute or search by name.
14 of 14 MCPs
| # | MCP | Tags | Setup | Complexity | Labels | |
|---|---|---|---|---|---|---|
| 1 | Google MCP Toolbox Google-published MCP server for databases — one Go binary speaks Postgres, MySQL, BigQuery, Spanner, AlloyDB, Snowflake, ClickHouse, MongoDB, Redis, and more. | database, multi-database | 10 min | Medium | Official | |
| 2 | Grafana Query dashboards, Prometheus/Loki datasources, alerts, and incidents from an AI agent — official. | grafana, observability | 5 min | Medium | Official | |
| 3 | MongoDB Query and inspect MongoDB collections from Claude, Cursor, and VS Code. | database, nosql | 5 min | Low | Official | |
| 4 | ClickHouse Query ClickHouse and ClickHouse Cloud columnar warehouses from an AI agent. | database, warehouse | 10 min | Medium | Official | |
| 5 | Google Ads Query Google Ads campaigns, metrics, and account performance with GAQL — Google's official MCP. | google-ads, ads | 10 min | Medium | Official | |
| 6 | Elasticsearch Search indices, inspect mappings, and run queries against Elasticsearch from an AI agent. | database, search | 10 min | Medium | Official | |
| 7 | Sentry Triage errors, inspect traces, and query events from Sentry. | sentry, errors | 3 min | Low | Official | |
| 8 | Google Analytics Query GA4 reports, funnels, and realtime data from an AI agent. | analytics, ga4 | 15 min | Medium | Official | |
| 9 | DuckDB In-process analytical SQL over files and Parquet from an AI agent. | database, olap | 2 min | Low | ||
| 10 | MySQL Run read-only MySQL queries and inspect schemas from an AI agent. | database, sql | 5 min | Low | ||
| 11 | Google Search Console Query Search Console clicks, impressions, and queries from an AI agent. | seo, search-console | 15 min | Medium | ||
| 12 | BigQuery Google's own hosted BigQuery MCP server — explore datasets and run SQL under IAM, with a read-only tool by default. | database, warehouse | 10 min | Medium | Official | |
| 13 | Snowflake (official) Snowflake-managed MCP servers — Cortex Analyst, Cortex Search, and gated SQL, with RBAC and masking enforced by Snowflake. | snowflake, warehouse | 30 min | High | Official | |
| 14 | Snowflake (community) Inspect schemas and run SQL against a Snowflake account from an AI agent. | database, warehouse | 15 min | Medium |
Choose the right MCP
Quick decision guide based on your use case.
| If you need… | Start with |
|---|---|
| Your data is in local CSV or Parquet files, or on S3 | Use DuckDB |
| Your warehouse is BigQuery and query cost worries you | Use BigQuery and dry-run every query before executing |
| You need one server across Postgres, MySQL, BigQuery, and more | Use Google MCP Toolbox with a tools.yaml allowlist |
| Your warehouse is Snowflake | Use Snowflake with a read-only role and a dedicated small warehouse |
| You investigate incidents across metrics and logs, not SQL | Use Grafana; pair it with Sentry for error triage |
| You report on organic and paid marketing performance | Pair Google Analytics with Google Search Console; add Google Ads for spend |
| You run product analytics or log search on a columnar store | Use ClickHouse |
Top Analytics & Data MCPs ranked
Detailed cards with setup time, complexity, and key labels.
Official
Query dashboards, Prometheus/Loki datasources, alerts, and incidents from an AI agent — official.
grafana, observability, prometheus, loki
Official
Query and inspect MongoDB collections from Claude, Cursor, and VS Code.
database, nosql, mongodb, atlas
Official
Query ClickHouse and ClickHouse Cloud columnar warehouses from an AI agent.
database, warehouse, clickhouse, olap
Official
Query Google Ads campaigns, metrics, and account performance with GAQL — Google's official MCP.
google-ads, ads, ppc, marketing
Official
Search indices, inspect mappings, and run queries against Elasticsearch from an AI agent.
database, search, elasticsearch, elastic
Official
Triage errors, inspect traces, and query events from Sentry.
sentry, errors, monitoring, observability
Official
Query GA4 reports, funnels, and realtime data from an AI agent.
analytics, ga4, google-analytics, reporting
In-process analytical SQL over files and Parquet from an AI agent.
database, olap, duckdb, parquet
10
Run read-only MySQL queries and inspect schemas from an AI agent.
database, sql, mysql, mariadb
Query Search Console clicks, impressions, and queries from an AI agent.
seo, search-console, gsc, organic-search
12
OfficialGoogle's own hosted BigQuery MCP server — explore datasets and run SQL under IAM, with a read-only tool by default.
database, warehouse, bigquery, google-cloud
10 minMedium
Official
Snowflake-managed MCP servers — Cortex Analyst, Cortex Search, and gated SQL, with RBAC and masking enforced by Snowflake.
snowflake, warehouse, cortex, sql
30 minHigh
Inspect schemas and run SQL against a Snowflake account from an AI agent.
database, warehouse, snowflake, sql
Archived (historical reference)
1 Analytics & Data entry is archived — the upstream package was deprecated or pulled, or a documented security issue applies. The detail page is preserved for historical reference and migration guidance, but these are NOT current editorial picks.
Also in the ChatGPT Apps directory
20 Analytics & Data brands are available as one-click ChatGPT Apps. These are not ranked alongside the editorial picks above — they're listings from chatgpt.com/apps surfaced here for brand-search continuity.
- AieraChatGPT App
- BankrateChatGPT App
- CB InsightsChatGPT App
- CubeChatGPT App
- DaloopaChatGPT App
- Dow Jones FactivaChatGPT App
- FactSet AI-Ready DataChatGPT App
- HexChatGPT App
- LONA Trading AssistantChatGPT App
- MixpanelChatGPT App
- MotherDuckChatGPT App
- MT NewswiresChatGPT App
- PendoChatGPT App
- PitchBookChatGPT App
- QuartrChatGPT App
- S&P GlobalChatGPT App
- StatsigChatGPT App
- Third BridgeChatGPT App
- ThoughtSpot SpotterChatGPT App
- TickerSageChatGPT App
FAQ: Analytics & Data MCPs
Which analytics MCP should I use for local CSV or Parquet files?
DuckDB. It runs in-process over stdio with no credentials, sets up in about two minutes, and runs analytical SQL directly over CSV, Parquet, and S3 paths. It is the lowest-risk entry in this category because there is no warehouse to protect and no query bill to run up.
Is it safe to point an analytics MCP at my production warehouse?
Only with a read-only role scoped to the schemas the agent actually needs, plus a default row limit. BigQuery's dry-run cost estimation helps because the agent can price a query before executing it; on Snowflake, give the agent its own small warehouse so a runaway query cannot starve production workloads. Never reuse the credential your ETL jobs write with.
Do I need separate MCPs for MySQL, BigQuery, and the rest of my databases?
Not necessarily. Google MCP Toolbox is one Go binary that speaks Postgres, MySQL, BigQuery, Spanner, ClickHouse, MongoDB, and more, with allowed queries declared in a tools.yaml file — good governance, because the agent can only run what you wrote down. Dedicated servers like ClickHouse or MongoDB still win when you want engine-specific tools such as aggregation-pipeline drafting.
Can an AI agent build marketing reports from GA4 and Search Console?
Yes. The Google Analytics MCP queries GA4 reports, funnels, and realtime data; Google Search Console returns clicks, impressions, CTR, and position by query and page; Google Ads adds spend and campaign metrics via GAQL. All three are official Google servers using OAuth 2.1 over stdio, and each takes ten to fifteen minutes of console setup — the auth is the hard part, not the queries.
How do I stop an agent from running expensive warehouse queries?
Three controls: a read-only role, a row cap enforced by the MCP or the role, and cost preview where available — BigQuery's dry run estimates the scan before execution. Also watch loops: an agent that retries a failing query with small variations can run the same expensive scan a dozen times. Grafana and Sentry help precisely because they answer many questions from pre-aggregated data without touching the warehouse.