Top 6 MCP Servers for Analytics & Monitoring in 2026

Observability is where AI agents earn their keep. An assistant that can pull a stack trace, run a PromQL query, or read a CloudWatch alarm can move from guessing about a production incident to reasoning against real signals. That is exactly what the analytics and monitoring category of the Model Context Protocol (MCP) ecosystem unlocks: error trackers, metrics backends, and full observability platforms wired directly into your coding and ops workflows.

But an MCP server for monitoring often needs broad read access to your telemetry, your infrastructure API tokens, and sometimes your customer-facing error data. Star count tells you nothing about whether that code is safe to grant those permissions. So every server below has been scanned by MCP Trust Checker, our deterministic security engine, and carries a real MCP Trust Score — a letter grade from A to F and a number from 0 to 100 — derived from reading the actual published source.

How to read the grade: A (90–100) means a clean scan with no high-severity findings and tight capabilities; B–C signal issues worth reviewing; D–F mean stop and read the findings before you install. We also show how many high-severity findings each server has and its capability level, because a powerful server that touches your network and secrets deserves more scrutiny than a read-only one. Here are the six that lead the category.

1. Sentry MCP

The official Sentry server is the reference implementation for error monitoring over MCP. It lets an assistant list and search issues and events, pull stack traces and trace context, query across projects, and invoke Sentry's Seer AI for root-cause analysis — over both stdio and remote transports. During an incident it is the fastest way to let an agent triage an exception and correlate it across releases without you leaving your editor. Vendor-official and widely adopted, it is the default choice for teams already on Sentry.

Our scan grades it A, but note it flags two high-severity findings and requests a high capability level — unsurprising for a server with remote transport and broad API reach. Review those findings on its registry page before granting it access to sensitive error data.

MCP Trust Score: A (94/100) · GitHub · npm

2. Datadog MCP (winor30)

This is the most widely adopted installable Datadog server, exposing tools for incidents, monitors, logs, dashboards, metrics, traces, hosts, and RUM through the Datadog API. It fills a real gap: Datadog's own MCP is offered only as a managed remote service with no downloadable package, so this community server is the practical, self-hostable way to put your Datadog account behind an agent. If you want to ask an assistant "which monitors are alerting right now?" and get a grounded answer, this is where you start.

It earns a perfect score in our scan — a clean bill of health with zero high-severity findings and a minimal capability footprint, which is exactly what you want from a third-party server touching production telemetry.

MCP Trust Score: A (100/100) · GitHub · npm

3. Prometheus MCP

Prometheus MCP puts a raw metrics backend directly behind an agent. It executes instant and range PromQL queries, discovers metrics and label metadata, and analyzes time-series data, with support for multiple auth methods so it works against self-hosted or managed Prometheus. If your stack is Prometheus-native, this is the de-facto way to let an assistant explore your metrics and reason about trends without you hand-writing every query. It is actively maintained and one of the most-referenced servers in the category.

Our scan gives it an A with no high-severity findings and a minimal capability level — a clean, low-risk read path into your metrics.

MCP Trust Score: A (98/100) · GitHub · PyPI

4. AWS CloudWatch MCP

Part of the official AWS Labs MCP monorepo, this server gives agents alarm-based troubleshooting, log anomaly and error-pattern analysis, metric data retrieval with metadata, best-practice alarm recommendations, and PromQL queries against CloudWatch. For teams running on AWS it collapses a lot of console tab-switching into a single conversational interface when you are diagnosing a misbehaving service. Being vendor-official from AWS Labs, it is the standard choice for AWS-native observability.

It scores an A with zero high-severity findings and a minimal capability level in our scan — a well-scoped, trustworthy path into CloudWatch.

MCP Trust Score: A (98/100) · GitHub · PyPI

5. Pydantic Logfire MCP

Logfire MCP, from the Pydantic team, exposes OpenTelemetry traces and metrics so an assistant can inspect distributed traces, run arbitrary queries over telemetry, and analyze exceptions. It is a strong fit for Python and OTel-based stacks where you want tracing data in the agent loop. One caveat worth knowing before you commit: the stdio package is now in maintenance mode as Pydantic shifts toward a hosted remote MCP, so weigh long-term direction if you are standardizing on it.

The scan returns an A with no high-severity findings and a minimal capability level — a clean, OpenTelemetry-native analytics server.

MCP Trust Score: A (98/100) · GitHub · PyPI

6. Last9 MCP

Last9's official server connects an assistant to production observability data — logs, metrics, traces, exceptions, slow database queries, alerts, and deployments — with the explicit goal of bringing real production signals into the coding loop so an agent debugs against reality rather than assumptions. It runs as hosted HTTP or self-hosted stdio and shows up regularly in observability-MCP roundups. If you are already a Last9 customer, it is a natural fit.

Being honest about the grade: this is the lowest-scoring server on the list. Our scan gives it a B (81/100) and flags two high-severity findings, alongside a high capability level. That is not a reason to avoid it, but it is a reason to read the findings on its registry page and understand what the server can touch before you grant it access to production data.

MCP Trust Score: B (81/100) · GitHub · npm

How we graded these

Every score above comes from the MCP Trust Checker engine, which is deterministic and open-source. It reads the actual published source from npm or PyPI and applies a fixed set of security rules — capability-flow analysis, toxic-flow detection, and known-risk patterns — to produce a letter grade and a 0–100 score. There is no language model in the loop and no vibes: the same package version always yields the same score, and anyone can reproduce it. High-severity findings and capability levels are surfaced so you can judge risk in context, not just trust a headline grade. You can read more about the methodology at /registry/security.

Want the full picture? Browse every scanned observability server, ranked and filterable, in the Analytics & Monitoring category of the registry — and if the MCP server you are eyeing is not listed yet, scan any npm or PyPI package for free through our public API before you install it.

npx mcptrustchecker