MCP For Claude Marketplace
Chainlit

Chainlit MCP Connector for Claude

F

Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

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.
llm-observabilityconversational-aitelemetryai-analyticsmodel-trackingchat-logs

6 tools expose this connector's capabilities to your AI agent.

list_projects

List explicit globally configured Chainlit Cloud projects managing independent app tracking spaces

list_threads

List conversational threads identifying user interaction boundaries inside a specific deployed project

get_thread

Retrieve the exact payload for a specific conversational thread locating exact node topologies

list_steps

List raw programmatic interaction steps explicitly defining prompts and generations inside a single thread

list_feedbacks

List absolute user review feedbacks rating explicitly conversational accuracy and value across deployments

get_stats

Retrieve explicit analytics statistics representing traffic boundaries and resource consumptions over native projects

See how to talk to your AI agent using Chainlit.

Retrieve the analytics stats of my currently enabled Chainlit cloud project targeting traffic.

Statistics collected securely via the Project endpoints. Currently, your implementation 'SupportBot-Alpha' processes approximately 389 conversations accounting for nearly 41,000 internal generative tokens in this time boundary. Anything specific to narrow down?

Search my cloud instance for the recent recorded chat interactions (threads) to fetch what clients asked today.

Execution successful on your native environment. Found 8 active raw threads in the last few windows. Chiefly, Thread #d8s1_.. tracks questions involving "Pricing plans and multi-factor capabilities". Should I dive deeper reading the full step logic of this thread?

Gather all negative feedbacks users submitted across this AI project.

Negative feedback list acquired covering the whole node tree. I detected exactly 4 'thumbs_down' signals mapped explicitly against responses generated by standard API calls. Users complained specifically stating: "The table format is badly cropped on mobile." Would you like me to identify the trace of those failed responses?

Yes! The agent can dive into the `list_threads` and `get_thread` endpoints to retrieve comprehensive interaction logs from your deployed Chainlit apps. You can essentially command the agent to read past AI chats, summarize usage, or identify edge cases in the user input.

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