MCP Server

The Remyx MCP server exposes ExperimentOps tools via the Model Context Protocol. Connect from Claude Code, Claude Desktop, Cursor, or any MCP-compatible client to manage experiments and discover relevant resources using natural language. The server also powers the managed Slack agent and the Claude Code implementation pipeline.

Architecture

The MCP server runs as a standalone service at mcp.remyx.ai. Connect using Streamable HTTP at https://mcp.remyx.ai/mcp, or SSE at https://mcp.remyx.ai/sse for clients that require it. Every tool call is logged and powers the activity feed in the experiment detail view.

Connecting

Claude Code

Add to your Claude Code MCP configuration (~/.claude/settings.json or project-level):

{
  "mcpServers": {
    "remyx": {
      "type": "http",
      "url": "https://mcp.remyx.ai/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_REMYX_API_KEY"
      }
    }
  }
}

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "remyx": {
      "type": "http",
      "url": "https://mcp.remyx.ai/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_REMYX_API_KEY"
      }
    }
  }
}

Other MCP Clients

Any client supporting the MCP Streamable HTTP transport can connect using the URL https://mcp.remyx.ai/mcp with a Bearer token in the Authorization header.

Get your API key at Account > API Access in the Remyx app, or at engine.remyx.ai/account.

Tools

Discovery Tools

get_digest

Get today’s recommended resources across all your Research Interests.Parameters: None required. Optionally pass period (today, week) and limit.Returns: Recommended resources grouped by Research Interest, with titles, abstracts, relevance scores, and links.Example prompt: “Show me today’s research digest”

list_interests

List all of your configured Research Interests.Parameters: None.Returns: Array of interests with name, context, daily paper count, and status (active/paused).Example prompt: “What Research Interests do I have set up?”

refresh_interests

Trigger a fresh resource ranking for one or all Research Interests.Parameters: interest_name (optional — refresh all if omitted).Returns: Confirmation that the refresh job was started. Results are available via get_digest after processing.Example prompt: “Refresh my Retrieval Systems interest”

get_papers

Get resources for a specific Research Interest.Parameters: interest_name (required), period (optional), limit (optional).Returns: Resources matched to the specified interest with metadata, scores, and Docker availability.Example prompt: “Show me resources about spatial reasoning from this week”

Experiment Tools

create_experiment

Create a new experiment in Remyx.Parameters:

Parameter	Type	Required	Description
`name`	string	Yes	Experiment name
`source_type`	string	No	`paper`, `hypothesis`, `incident`, or `recommendation`
`source_ref`	object	No	Source details (paper ID, hypothesis text, etc.)
`target_metric`	string	No	Business metric this experiment aims to move
`tags`	array	No	Free-form tags for grouping
`initiative`	string	No	Initiative/project name
`resource_id`	string	No	Resource ID (for research-sourced experiments)

Returns: Created experiment with ID and all fields.Example prompt: “Create an experiment called ‘Hybrid search with re-ranking’ targeting resolution-rate, tagged retrieval and re-ranking, in the Customer Support AI initiative”

get_experiments

List experiments with optional filters.Parameters: initiative (optional), status (optional), tags (optional).Returns: Array of experiments with all ExperimentOps fields.Example prompt: “Show me all experiments in Customer Support AI”

update_experiment

Update fields on an existing experiment.Parameters:

Parameter	Type	Description
`experiment_id`	string	Required
`observed_delta`	float	Measured result
`delta_confidence`	string	`significant`, `inconclusive`, `not_tested`
`tags`	array	Updated tag list
`pr_url`	string	Link to implementation PR
`pr_status`	string	PR status
`ab_test_config`	object	A/B test configuration
`ab_test_results`	object	Test results
`status`	string	Experiment status

Returns: Updated experiment.Example prompt: “Update the hybrid search experiment with observed delta +3.1%, statistically significant”

log_decision

Record the team’s decision on an experiment. This is the key ExperimentOps primitive — capturing why, not just what.Parameters:

Parameter	Type	Required	Description
`experiment_id`	string	Yes	Which experiment
`decision`	string	Yes	The decision text with reasoning
`outcome`	string	No	`ship`, `iterate`, `abandon`

Returns: Updated experiment with decision, timestamp, and author.Example prompt: “Log decision on experiment 12: Ship to 100%. This is the synthesis experiment — it combined retrieval with few-shot selection and produced our best single-experiment improvement.”

get_experiment_context

Get the full implementation context for a research-sourced experiment, including resource metadata, Docker environment, repo structure, and implementation plan.Parameters: experiment_id (required).Returns: Launch context object with resource details, file tree, and implementation plan.Example prompt: “Get the implementation context for experiment 7”

run_experiment_implementation

Trigger the Claude Code implementation pipeline for a research-sourced experiment. This generates a PR in the target repository.Parameters: experiment_id (required).Returns: Implementation status and PR link when complete.Example prompt: “Run the implementation for experiment 3”

Validation Tools

get_validation_results

Get validation results for an experiment (A/B test outcomes, online metrics).Parameters: experiment_id (required).Returns: Validation data including control vs. treatment metrics.

Full validation integration with A/B testing platforms (Statsig, LaunchDarkly) is coming soon. Currently returns data recorded via update_experiment.

close_loop

Close the experiment loop — record final results and mark the experiment as validated.Parameters: experiment_id (required), final_results (object).Returns: Updated experiment in validated state.

Full close-loop automation (pulling results from external A/B platforms) is coming soon.

Slack Agent

The MCP server also powers the managed Slack agent. When you @mention the Remyx bot in Slack, it uses the same MCP tools to respond: fetching digests, listing experiments, logging decisions, etc. See Connectors > Slack for setup.

Implementation Pipeline

For research-sourced experiments with a launch context, Claude Code can run the full implementation flow:

Fetch the experiment context (resource metadata, Docker environment, repo structure, implementation plan)
Read the target repository
Generate code changes implementing the technique
Push a branch and open a PR
Link the PR back to the experiment

The PR status syncs via GitHub webhooks. When the PR is merged, the experiment status updates automatically. See the Quick Start for a walkthrough.

Next Steps

Connect Claude Code

Step-by-step setup guide

Connect Slack

Set up the managed Slack agent

Experiment Dashboard

See MCP tools in the experiment workflow

API Reference

REST API documentation

Get Started

Discover

Experiments

Manage

Admin

Concepts

Tutorials

MCP Server

MCP Server

Architecture

Connecting

Claude Code

Claude Desktop

Other MCP Clients

Tools

Discovery Tools

Experiment Tools

Validation Tools

Slack Agent

Implementation Pipeline

Next Steps

Connect Claude Code

Connect Slack

Experiment Dashboard

API Reference

Get Started

Discover

Experiments

Manage

Admin

Concepts

Tutorials

​MCP Server

​Architecture

​Connecting

​Claude Code

​Claude Desktop

​Other MCP Clients

​Tools

​Discovery Tools

​Experiment Tools

​Validation Tools

​Slack Agent

​Implementation Pipeline

​Next Steps

Connect Claude Code

Connect Slack

Experiment Dashboard

API Reference

MCP Server

Architecture

Connecting

Claude Code

Claude Desktop

Other MCP Clients

Tools

Discovery Tools

Experiment Tools

Validation Tools

Slack Agent

Implementation Pipeline

Next Steps