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Documentation Index

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The Nebuly MCP Server lets AI assistants query Nebuly data through the Model Context Protocol. Use it to ask questions about interactions, conversations, aggregated data or to export interaction CSV files without leaving your assistant.
The Nebuly MCP Server is currently in beta and available to all SaaS users and self-hosted customers.SaaS users can connect directly using https://mcp.nebuly.com/mcp.Self-hosted customers should use the URL provided by the Nebuly team.

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Prerequisites

Before you configure the integration, make sure you have:
  • A Nebuly account with MCP access (all SaaS users, or a self-hosted environment with MCP beta enabled).
  • Your Nebuly MCP server URL, shown below as <NEBULY_MCP_URL>. SaaS users should use https://mcp.nebuly.com/mcp.
  • One of the supported MCP clients: Claude Desktop, Claude Code, or Cursor.

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Configure your MCP client

Detailed instructions for adding the Nebuly MCP server to Claude Desktop can be found in the Claude Desktop documentation.This typically requires organization-level configuration, so please reach out to your Claude administrator if you don’t have access to the settings described in the documentation.

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MCP Overview

Once connected, your assistant can use Nebuly tools to inspect and analyze data.

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Interactions and conversations

  • list_projects: List the Nebuly projects your assistant can access.
  • get_interactions: Query interactions in a project with filters. Results will be paginated and limited, this tool is designed for browsing and inspecting small sets of interactions, not for bulk data retrieval.
  • get_interaction_details: Retrieve the full record for a single interaction, based on an interaction ID.
  • get_conversation_detail: Fetch the full conversation history based on a conversation ID.

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Analytics

  • get_interaction_aggregates: Group interactions by one dimension, such as topic, sentiment, user intent, language, feedback, risk type, or custom fields.
  • get_interaction_multi_aggregates: Break down interactions across multiple dimensions, such as topic by sentiment or intent by feedback.
  • get_interaction_time_series: Build trends over time with minute, hour, day, week, month, or year buckets.
  • describe_aggregate_schema: Discover the supported filters, groupings, metrics, ordering fields, and time granularities.
The analytics tool gets best result when used from assistants that can handle tabular data and charts, such as Claude Desktop.

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Exports

  • get_interactions_csv: Start an async CSV export for a filtered set of interactions. By default the export will include timestamp, input and output. You can ask your assistant to include additional fields such as user, topic, sentiment or cost.
  • get_csv_export_status: Poll the status of a running export job. Returns pending, running, completed, or failed with a progress percentage.
  • prepare_csv_download: Once the export is complete, call this to obtain a time-limited download URL. The URL expires after 5 minutes.
The tools for CSV export work best from assistants that can handle file downloads, such as Cursor or Claude Code.

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Example questions

After defining a project to work with, your assistant can answer a wide variety of questions about your Nebuly data. Here are some examples to get you started: Try asking your MCP-enabled assistant:
  • “List the Nebuly projects I can access.”
  • “Show me the top 10 topics by interaction volume over the last 30 days for the PROJECT_NAME project.”
  • “Show me a chart of negative-sentiment interactions by topic and user intent for the last two weeks.”
  • “Show daily interaction volume and error-rate trends for the last quarter.”
  • “Which topics have the highest average latency this month?”
  • “Export January interactions with timestamp, user, input, output, topic, sentiment, and cost. Rome timezone”
Not all clients have access to your current position, to ensure the same results shown in the Nebuly UI provide your location/timezone in the question.
Your assistant may call describe_aggregate_schema before answering analytics questions. This helps it choose the right filters, metrics, and grouping dimensions for your Nebuly workspace.Your assistant may call list_projects to check which projects it can access and ask you to choose one before answering your question.