Chainlit MCP Server
Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.
Vinkius AI Gateway supports streamable HTTP and SSE.
Works with every AI agent you already use
…and any MCP-compatible client
Chainlit MCP Server: see your AI Agent in action
Built-in capabilities (6)
get_stats
Retrieve explicit analytics statistics representing traffic boundaries and resource consumptions over native projects
get_thread
Retrieve the exact payload for a specific conversational thread locating exact node topologies
list_feedbacks
List absolute user review feedbacks rating explicitly conversational accuracy and value across deployments
list_projects
List explicit globally configured Chainlit Cloud projects managing independent app tracking spaces
list_steps
List raw programmatic interaction steps explicitly defining prompts and generations inside a single thread
list_threads
List conversational threads identifying user interaction boundaries inside a specific deployed project
What this connector unlocks
Connect your Chainlit Cloud projects to any AI agent and embrace a new paradigm of conversational observability. Analyze your AI app traffic directly from your terminal or chat.
What you can do
- Project Analytics — Trigger detailed data fetches mapping global traffic statistics, distinct user adoptions, and absolute utilization figures across your AI portfolio.
- Thread Introspection — Query explicit interaction boundaries isolating full chronological conversations from users securely and swiftly.
- Trace Logic Steps — Extrapolate internal logic jumps identifying explicit prompts, outputs, tool executions, and retrieval boundaries used per interaction.
- Qualitative Feedback — Automatically extract lists capturing precise thumbs up/down, implicit ratings, and explicit textual user reviews targeting your bot responses.
How it works
1. Subscribe to this server
2. Introduce your Chainlit Cloud URL and Project API Key
3. Start fetching and diagnosing chat failures directly using Claude, Cursor, or compatible AI layers.
Who is this for?
- AI Developers — Instantly diagnose why a model failed in production by demanding the exact logical sequence and parameter stack used on a specific bad output.
- Product Teams — Monitor the absolute sum of positive feedbacks vs. negative outcomes, prompting your LLM to summarize the worst chats automatically.
- QA Specialists — Periodically poll new conversations evaluating tone, relevance, and compliance parameters blindly spanning hundreds of hours without reading logs manually.
Frequently asked questions
Give your AI agents the power of Chainlit
Access Chainlit and 2,000+ MCP servers — ready for your agents to use, right now. No glue code. No custom integrations. Just plug Vinkius AI Gateway and let your agents work.
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