This list is part of our best-of MCP lists, within The MCP Guide. Entries are checked against each vendor's own pages; inclusion is editorial and not paid.
The best MCP observability tools in 2026 are Sentry, Datadog, Langfuse, Arize Phoenix, Pydantic Logfire, New Relic and AgentCat. Each one records what happens when an AI client calls your MCP server: which tool ran, with what arguments, how long it took and whether it failed.
That matters once an MCP server is in real use. Without traces, a slow or failing tool shows up only as a confused answer in Claude or ChatGPT. With traces, you can see the exact call that broke.
This list covers tools that observe MCP traffic. It is not a list of MCP servers that let AI read your monitoring data; for that, see our observability MCP servers list. We checked each tool against its own docs and pricing page on 28 October 2026. eSEOspace did not test these products.
Key Takeaways
- MCP observability means tracing tool calls, resource reads and errors between an AI client and an MCP server.
- Sentry, Datadog and Logfire can instrument the official MCP SDKs automatically, so you add a few lines instead of wrapping every tool.
- Linking the client trace to the server trace usually means passing trace context through MCP's
_metafield, as Langfuse and Phoenix document. - Langfuse is MIT-licensed and Phoenix is source-available under the Elastic License 2.0, so both can be self-hosted.
- Watch what gets logged: Sentry treats tool results as personal data by default and drops them, while arguments are still sent unless you turn them off.
How we chose
We included a tool only if it passed these tests:
- Documented MCP support. The vendor's own docs describe tracing MCP clients, MCP servers or both, and we opened that page.
- Published pricing. The official pricing page lists plans, or the tool is open source.
- Security basics. The docs explain what data is captured, so you can judge what leaves your system.
- Live product. The company and product exist today.
We left out general logging products that do not mention MCP. We also left out gateways, which log traffic as a side effect; our MCP gateways list covers them. For the protocol's own logging feature, see our MCP logging utility explainer.
Comparison table
| Name | Best for | Pricing | Notable |
|---|---|---|---|
| Sentry | Errors and tool tracing in Python or JS servers | Free Developer plan; Team from $26/mo | MCP monitoring on every plan |
| Datadog | Teams already on Datadog | Agent Observability from $160/mo (annual) | Auto-instruments official Python and JS SDKs |
| Langfuse | Open-source LLM tracing with MCP context | Free Hobby; Core $29/mo; self-host free | MIT core license |
| Arize Phoenix | Linking client and server spans in one trace | Phoenix open source; AX Pro $50/mo | openinference-instrumentation-mcp |
| Pydantic Logfire | Python teams using FastMCP or Pydantic AI | Free Personal; Team $49/mo | One call instruments client and server |
| New Relic | Teams on New Relic APM with FastMCP | Free 100 GB/mo ingest | Python agent supports FastMCP 2.3.3+ |
| AgentCat (formerly MCPcat) | Product analytics for Claude connectors and ChatGPT apps | Free 500 sessions/mo; Growth $160/mo | Session replay and agent goals |
1. Sentry: MCP monitoring with error tracking
What it is. Sentry's MCP monitoring collects "MCP server connections, resource access, tool executions, and errors." The Python integration supports both FastMCP and the low-level server API in the MCP Python SDK.
It records tool arguments, results, prompts, resource URIs, session IDs and transport type. By default Sentry treats tool results and prompt content as personal data and does not send them. The Python integration page says it "is in its beta phase."
Best for: Teams that want MCP traces next to their normal error alerts.
Pricing: The Sentry pricing page lists Developer free for one user, Team at $29 per month ($26 billed annually) and Business at $89 per month ($80 billed annually). It lists MCP monitoring and agent tracing on all plans. Our Sentry MCP server profile covers Sentry's separate server for AI clients.
Pros
- MCP monitoring is included on the free plan.
- Tool results are kept out of Sentry by default.
- Errors and slow tools show up in one place.
Cons
- The Python integration is labeled beta.
- Tool arguments are still sent unless you change a setting.
Checked against: Sentry MCP monitoring docs on 28 October 2026.
2. Datadog: MCP client monitoring in Agent Observability
What it is. Datadog's MCP client monitoring docs offer "Automatic instrumentation" for the official MCP Python SDK and MCP JavaScript SDK. Java clients, or clients not built on an official SDK, use manual instrumentation.
Manual setup wraps the session in a workflow span and each tool call in a tool span with inputs, outputs and the server name. Datadog has also written about MCP security risks and which of its products can flag them.
Best for: Companies that already run Datadog for infrastructure and APM.
Pricing: The Datadog pricing list prices this under Agent Observability, per LLM span. The base tier covers the first 100,000 LLM spans per month for $160 billed annually, $200 month-to-month or $240 on demand. Extra volume is $3.50 per 10,000 spans on annual billing. See our Datadog MCP server profile for its server side.
Pros
- Automatic instrumentation for the two main SDKs.
- MCP traces sit beside the rest of your Datadog data.
- Manual option covers Java clients.
Cons
- Priced per span, which adds up with busy agents.
- The docs focus on client-side tracing.
Checked against: Datadog MCP client monitoring docs on 28 October 2026.
3. Langfuse: open-source tracing with linked MCP traces
What it is. Langfuse is an open-source LLM observability platform. Its MCP tracing guide says clients and servers produce separate traces by default. To link them, you "Propagate trace context from client to server using MCP's _meta field," using the W3C Trace Context format.
The result is one trace from the client, through the server, to any outside APIs. The Langfuse repo is "MIT licensed, except for the ee folders."
Best for: Teams that want open-source tracing they can self-host.
Pricing: The Langfuse pricing page lists Hobby free with 50,000 units per month, Core at $29 per month, Pro at $199 per month and Enterprise at $2,499 per month. Paid plans include 100,000 units, then $8 per 100,000. The page says "you can self-host it for free."
Pros
- MIT-licensed core you can run yourself.
- Clear guide for joining client and server traces.
- Low entry price for the cloud version.
Cons
- Linking traces takes code on both sides.
- The MCP guide names no ready-made MCP package.
Checked against: Langfuse MCP tracing docs on 28 October 2026.
4. Arize Phoenix: OpenInference MCP instrumentation
What it is. Phoenix's MCP tracing docs say "Phoenix provides tracing for MCP clients and servers through OpenInference." The openinference-instrumentation-mcp package handles "context propagation between MCP clients and servers to unify traces."
It does not create telemetry on its own. You still set up OpenTelemetry tracing in both the client and the server.
Best for: Teams that want client and server spans in one trace without writing propagation code.
Pricing: The Arize pricing page lists Phoenix as open source. The hosted AX product has a Free plan with 25,000 trace spans per month, AX Pro at $50 per month with 50,000 spans, and custom Enterprise. The Phoenix repo uses the Elastic License 2.0 (ELv2).
Pros
- Ready-made package for MCP context propagation.
- Self-hostable on Docker or Kubernetes.
- Cheap hosted tier if you outgrow self-hosting.
Cons
- ELv2 is source-available, not an OSI open-source license.
- You still configure OpenTelemetry on both sides.
Checked against: Phoenix MCP tracing docs on 28 October 2026.
5. Pydantic Logfire: one call for Python MCP
What it is. Logfire's MCP integration traces "each client request and server response, the tool called, its arguments and result, call durations, and errors." With MCP Python SDK v1 you call logfire.instrument_mcp() in both processes.
With SDK v2 and fastmcp 4, the docs say logfire.configure() alone is enough because the SDK propagates trace context itself.
Best for: Python teams building with FastMCP or Pydantic AI.
Pricing: The Pydantic pricing page lists Personal at $0 with 10 million records per month, Team at $49 per month and Growth at $249 per month. Paid plans charge $2 per extra million records. Ingestion pauses at the cap on the free plan.
Pros
- Very little setup for Python servers.
- Client and server spans join into one trace.
- Generous free record allowance.
Cons
- Python-focused.
- Free plan stops collecting at the cap.
Checked against: Logfire MCP integration docs on 28 October 2026.
6. New Relic: AI monitoring with FastMCP support
What it is. New Relic's AI monitoring runs through its APM agents. Its compatibility page lists "FastMCP versions 2.3.3 and above" for the Python agent, version 9.8.0 and above.
The compatibility page does not list MCP support for the Node.js agent. Check it before you plan a JavaScript rollout.
Best for: Teams already on New Relic APM with Python MCP servers.
Pricing: The New Relic pricing page lists a free tier with 100 GB of data ingest per month and one full platform user. Extra data costs $0.40 per GB. Full platform users on Standard cost $10 for the first and $99 for each extra (up to 5).
Pros
- Large free data allowance.
- MCP traces sit inside existing APM views.
- No separate MCP product to buy.
Cons
- Documented MCP support is FastMCP on Python only.
- Full platform seats get expensive on Pro.
Checked against: New Relic AI monitoring compatibility docs on 28 October 2026.
7. AgentCat (formerly MCPcat): analytics for connectors and apps
What it is. AgentCat describes itself as "Analytics and observability for Claude Connectors and ChatGPT Apps." A banner on the site says "MCPcat is now AgentCat," and mcpcat.io now redirects there.
It offers session replays of each tool call, grouping by "Agent Goals," error grouping and per-tool error rates. The site says its "SDKs are open-source and available on GitHub" and offers self-hosting.
Best for: Companies that publish an MCP server and want product analytics on how agents use it.
Pricing: The AgentCat site lists Free at $0 for 500 sessions per month, Growth at $160 per month for 2,000 sessions, and custom Enterprise. Extra sessions cost $40 per 1,000 on both paid tiers.
Pros
- Built for MCP server makers, not general apps.
- Session replay shows real agent behavior.
- Open-source SDKs and a self-host option.
Cons
- Session-based pricing climbs quickly with heavy use.
- The site names only a TypeScript SDK.
Checked against: AgentCat homepage and pricing on 28 October 2026.
How these tools differ
The seven tools fall into three groups. Sentry, Datadog and New Relic are general monitoring platforms that added MCP support, so they suit teams that already pay for one of them. Langfuse, Phoenix and Logfire are built around LLM and agent tracing, and they are the easiest to self-host or start free.
AgentCat is different again. It focuses on product analytics for companies that publish an MCP server, such as which tools agents use and where sessions fail. Many teams pair one tool from the first two groups for errors with AgentCat-style analytics for product decisions.
What to trace first
Start with three things for every tool call: the tool name, how long it took and whether it errored. Those three show most real problems.
Then decide what not to log. Tool arguments and results can hold customer data, so treat them like any other personal data. Our lesson on logging and monitoring and MCP audit logging guide cover what to keep and for how long.
Next step
Observability is easiest to add while a server is being built. If you need an MCP server built with tracing and audit logs from day one, see our MCP development services, or browse more lists in the MCP Guide's best-of section.
Frequently asked questions
What is MCP observability?
It is recording what happens between an AI client and an MCP server. That includes which tools were called, their inputs and outputs, timing and errors. It helps you debug failures and spot misuse.
Do I need a special tool, or will normal logs do?
Normal logs help, but tracing tools tie each tool call to the AI request that caused it. Several tools on this list do that automatically for the official MCP SDKs. That saves you writing the same wrapping code for every tool.
How do I link client and server traces?
Pass OpenTelemetry trace context from the client to the server. Langfuse documents doing this through MCP's _meta field, and Phoenix ships a package that does it for you. Logfire says MCP Python SDK v2 propagates context on its own.
Is it safe to log tool arguments?
Only if you treat them as sensitive. Arguments and results may hold names, emails or financial data. Sentry drops tool results by default for this reason, and you can turn off arguments too.
Which tool is cheapest to start with?
Sentry, Langfuse, Logfire, Phoenix, New Relic and AgentCat all have free tiers. Langfuse and Phoenix can also be self-hosted. Pick the one that fits the stack you already run.
