ChatGPTbefore your users do.From your first prompt to a continuous gate on every release, MCPJam shows what breaks across every AI client, and how to fix it.
Trusted by forward-thinking teams
After the coding agent. Before your users.
Make reliability evals, security, and protocol compliance required checks in your own pipeline. Your build ships when it clears the bar you set, and every run makes the next one stronger.
Catch failures as you build, see exactly what to fix, and block them before every release. Ship only what's proven, and every run makes the next one sharper.
Start where 50,000+ developers do. Free.
Reproduce any failure, compare up to three models side by side, and read the trace for the exact tool call, argument diff, and latency. The open-source inner loop.
ChatGPT
Every test runs against the real thing.
MCPJam mirrors the live behavior of ChatGPT, Claude, Gemini, Cursor, and more, kept current for you. No brittle harness to maintain.
ChatGPTGPT-5.5 · openai-mcp 1.0.0Client capabilities
Host capabilities
Claude · Fable 5Copilot · Gemini 3.5+ 6 more · every client kept current for you
Catch the failures a real user would hit, before they do.
Configure AI agent personas and let Swarm run them through multi-turn journeys at scale, or hand real testers a safe pre-production Chatbox that records every session with ratings and notes. Promote any session, agent or human, into your acceptance tests.
Stop guessing what to change.
Score tool choice, arguments, and completion across runs and clients. Insights pinpoint the root cause and the lift each fix buys, then gate the merge on every commit.
verdictProve it's safe to expose, not just that it works.
Make security, auth, and conformance required checks on every PR. Sensitive data stays redacted and every decision logged, so nothing ships until it's cleared.
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ChatGPT, Claude, Gemini, Cursor, and the rest each run your tools differently, and change constantly. MCPJam emulates behavior of all of them, so every test reflects what your users experience today.
Reproduce a real failure on demand instead of waiting for a user to hit it.
See which user intents the server actually satisfies, persona by persona.
Replace "we vibe-tested it" with continuous, reproducible acceptance evidence.
Run acceptance, evaluation, and security suites across a growing fleet of servers — without standing up human QA for each one.
“MCPJam is the official evaluation CLI for MCP servers. Before, we had no systematic way to test whether switching tool configurations would break agent behavior. Now we do.”
Bright Data team
Web MCP, Bright Data
“We use MCPJam every day. It's become essential for testing MCP servers locally. Instead of deploying and manually testing prompts across clients, we rely on MCPJam's local AI chat and CI/CD.”
Jau Chan
Senior Software Engineer, Asana
“MCPJam is the official evaluation CLI for MCP servers. Before, we had no systematic way to test whether switching tool configurations would break agent behavior. Now we do.”
Bright Data team
Web MCP, Bright Data
“We use MCPJam every day. It's become essential for testing MCP servers locally. Instead of deploying and manually testing prompts across clients, we rely on MCPJam's local AI chat and CI/CD.”
Jau Chan
Senior Software Engineer, Asana
Used daily for MCP testing
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In the pre-production layer between build and production, and we're SDK and framework agnostic, so we work with any MCP server however you built it. We don't instrument your code or your live traffic. We exercise your server the way real AI clients do, during dev, QA, beta, and CI/CD, because that's the window where every failure mode is still visible and cheap to fix. Once it ships into an external agent it's a black box, so the highest-value reliability work happens just before that line, which is exactly where we live.
Agent eval and observability tools (Datadog, Braintrust, LangSmith, Arize) measure the agent you built, in a system you control that already sees the user's prompt, context, and tool calls. MCP evals measure the other side of the handshake: how your software behaves when an external agent you don't control (ChatGPT, Claude, Copilot, Cursor) decides whether to call it, with what arguments, and how it uses the result. MCPJam sits outside your system and confirms your software is production-ready for every external agent, before your users ever interact with it.
The core is open source and free, forever: the client, Inspector, core CLI and SDK, local evals, and conformance checks, to run locally or in your own CI/CD.
Paid plans are for when your team is ready to go further:
No, we are strictly pre-production. There may be an ability in the future to import traces of production traffic to bolster session data and provide better insights for you, but for now we're strictly pre-production, where we believe you can get more reliable telemetry anyway given external agents unreliably offer you production user insights.
No, that's the point. ChatGPT, Claude, Gemini, Cursor, Slack and the rest each support different things and change constantly. MCPJam maintains the current behavior of all of them, so your tests reflect what your users experience today without your team tracking a single client.
With evaluation, not assertions. MCPJam scores whether the agent selected the right tool, sent the right arguments, and completed the job across runs and clients, then diagnoses why a score dropped and what to change, so erratic behavior becomes a metric you can gate on.
Acceptance testing run by AI agents that act like your users. You define personas; a swarm runs multi-turn journeys through your server across every client and surfaces where it fails the job, continuously, before launch.
Secure, isolated UAT environments: a shareable web client that mirrors the major AI hosts, so internal QA and beta testers can break things safely, and their real sessions become regression tests automatically.