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Confidence has three MCP servers:
  • Documentation MCP: Search and retrieve Confidence documentation
  • Flags MCP: Create and manage feature flags
  • Experiments MCP: Explore and analyze experiments and their results

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Documentation MCP Server

The Documentation MCP provides AI assistants with access to Confidence documentation through semantic search and full-page reads, including the SDK integration guides.

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Integration Setup

No authentication required for the Documentation MCP server. The server uses streamable HTTP as the transport and the URL is https://confidence.spotify.com/docs/mcp.

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Available Tools

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search_confidence_documentation

Searches the Confidence documentation for concepts, guides, API references, and code examples. Returns relevant sections with titles and links to the documentation pages.

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query_docs_filesystem_confidence_documentation

Runs read-only commands, such as cat, head, rg, and tree, against the documentation pages. Use it to read a full page by its path, for example head -200 /docs/flags/audience.mdx, or to find exact keyword or regular expression matches.

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Example Usage

Once integrated, ask your AI assistant to:
  • “Search Confidence documentation for information about A/B testing”
  • “Show how to integrate Confidence with Python”
  • “Summarize the A/B test quickstart”

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Flag Management MCP Server

The Flag Management MCP provides AI assistants with tools to manage Confidence feature flags, including creation, modification, targeting rules, and testing.

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Integration Setup

The Confidence Flags MCP requires authentication. Allow access from your IDE or developer tool where you use the MCP server. The server uses streamable HTTP as the transport and the URL is https://mcp.confidence.dev/mcp/flags.
After adding, open Claude Code, run /mcp and select confidence-flags to authenticate.

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Available Tools

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listClients

Lists all available Confidence clients for flag operations.

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listFlags

Lists all active feature flags with summary information including names, schemas, and variants.

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getFlag

Retrieves detailed information about a specific feature flag, including its complete schema, variants, and targeting rules.

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createFlag

Creates a new feature flag with schema and variant definitions.

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addFlagVariant

Adds a new variant to an existing feature flag.

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updateFlagSchema

Updates the schema definition of an existing feature flag.

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createOverrideRule

Creates targeting rules to override flag behavior for specific entities.

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testResolveFlag

Tests flag resolution for specific clients and entities and explains why the system selects a particular variant.

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analyzeFlagUsage

Finds unused Confidence feature flags or flags fully rolled out, helping you clean up feature flag code.

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Example Usage

Once integrated, ask your AI assistant to:
  • “List all feature flags in Confidence”
  • “Create a new flag called ‘new-checkout-flow’ with a boolean schema”
  • “Add a variant to the ‘new-checkout-flow’ flag”
  • “Create an individual targeting rule for user ‘test-user’ to see the ‘variant-b’ of ‘experiment-flag’”
  • “Test flag resolution for ‘new-checkout-flow’ with user ‘john@example.com’”
  • “Analyze what flags you can clean up in this codebase”

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Experiments MCP Server

The Experiments MCP provides AI assistants with tools to explore and analyze Confidence experiments, including A/B tests and rollouts.

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Integration Setup

The Confidence Experiments MCP requires authentication. Allow access from your IDE or developer tool where you use the MCP server. The server uses streamable HTTP as the transport and the URL is https://mcp.confidence.dev/mcp/experiments.
After adding, open Claude Code, run /mcp and select confidence-experiments to authenticate.

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Available Tools

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list_experiments

Lists Confidence experiments (A/B tests and rollouts). Returns experiment names, display names, states, owners, and creation times. Supports two modes:
  • List mode (default): Use the filter parameter with Lucene query syntax (for example, state:live for running experiments) and orderBy to sort results.
  • Search mode: Provide a query parameter for full-text search. In search mode, results order by relevance and the filter and orderBy parameters have no effect.
You can infer the experiment type from the resource name (for example, workflows/abtest/... or workflows/rollout/...).

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get_experiment

Retrieves detailed information about a specific experiment. By default, returns a compact summary with key fields:
  • Name, state, owner, and creation time
  • Metrics with types, minimum detectable effect (MDE), and non-inferiority margin (NIM)
  • Decision outcome (for ended A/B tests)
  • Available analysis result names
Set summary=false for full details including configuration, checks, stats, and state history.

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get_results

Retrieves statistical analysis results for an experiment. By default, returns a summary with:
  • Relative effect estimates and confidence intervals
  • Significance status and status messages
  • Sample sizes per treatment
  • Shipping recommendations per treatment
Accepts either an experiment instance name (for example, workflows/abtest/instances/{id}) or a specific analysis result name. When given an instance name, the tool automatically resolves the primary analysis result. Set summary=false for the full detailed output.

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get_resource

Looks up details of Confidence resources referenced in experiment data. Supports:
  • metrics/* - Metric definitions
  • entities/* - Entity definitions
  • surfaces/* - Surface configurations
  • segments/* - Segment definitions
  • factTables/* - Fact table definitions
  • flags/* - Feature flag configurations
  • identities/* - User identities
  • assignmentTables/* - Assignment table definitions
  • clients/* - Client configurations

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Example Usage

Once integrated, ask your AI assistant to:
  • “List all running experiments”
  • “Search for experiments related to checkout”
  • “Show details about the homepage-redesign experiment”
  • “Get the results for the checkout-flow A/B test”
  • “What’s the status of the metrics in the onboarding rollout?”