Decorator-based integration testing for LLM prompts
Most AI and media services work still happens through a UI a human drives. Prompt MCP server moves it into the conversation instead. Decorator-based integration testing for LLM prompts.
Decorator-based integration testing for LLM prompts. Wrap a test function with @prompt_run, and it sends your prompt to a real model, then hands you the response to assert on — with an LLM judge for the assertions that plain string checks can't express.
The server publishes 6 tools. What each one is for:
ask_all — many independent checks where cost matters and slight cross-contamination between questions is acceptableask_parallel — questions that must be fully isolated, without the latency of sequential ask callsask — a single question, or when you need to branch on the result before asking the nextMethod — API callsMethods — The Methods tool exposed by this serverFields — The Fields tool exposed by this serverConfiguration is passed through the environment: ANTHROPIC_API_KEY, GOOGLE_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
prompt-tester on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Prompt's toolset — ask_all, ask_parallel, ask and 3 more — is a fair guide to whether it matches your workflow.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| ask_all | many independent checks where cost matters and slight cross-contamination between questions is acceptable. |
| ask_parallel | questions that must be fully isolated, without the latency of sequential ask calls. |
| ask | a single question, or when you need to branch on the result before asking the next. |
| Method | API calls |
| Methods | The Methods tool exposed by this server. |
| Fields | The Fields tool exposed by this server. |
{
"mcpServers": {
"prompt-tester": {
"command": "uvx",
"args": ["prompt-tester"],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.