StateSet API Docs

New to MCP? Follow Manage Agents & Workflows with MCP for connection checks and first-task prompts. ResponseCX ships two Model Context Protocol servers with different jobs.

Server	Transport	For
ResponseCX remote	Streamable HTTP at `POST /api/mcp`	An external agent building and tuning ResponseCX agents
Response analytics	Local stdio	Claude Desktop reading analytics and reporting data

Remote agent-building server

Connect an external assistant to https://response.stateset.com/api/mcp using the deployment’s OAuth flow, a scoped rcx_ API key, or an enabled StateSet workspace-key connection. Workspace keys are verified against the onboarding service and require that integration to be configured on the Response deployment.

POST /api/mcp
Authorization: Bearer <YOUR_ACCEPTED_CREDENTIAL>

Authentication establishes the organization and available scopes. Inspect tools/list for the toolset your connection exposes; do not rely on a fixed tool count. Tool scopes apply on calls as well as discovery. A workspace key may permit writes, so a read-only prompt is not a substitute for scoped credentials and host approvals.

The server is stateless: each JSON-RPC POST builds a fresh server and transport, so there is no cross-request session state to leak between tenants. Tool output is capped at 60,000 characters and truncated with a marker beyond that.

Tools

Discovery — so an agent can orient before it writes anything:

Tool	Purpose
`describe_workspace`	What exists in this workspace
`describe_vocabulary`	The legal terms for rule conditions — stops the model guessing at grammar

Building:

Tool	Purpose
`provision_agent`	Build a complete agent — settings, rules, functions, knowledge — in one atomic call
`build_support_agent`	Opinionated support-agent scaffold
`create_agent` / `update_agent` / `get_agent` / `list_agents`	Agent lifecycle
`configure_agent` / `get_agent_settings`	Model and behavior settings
`add_rule` / `update_rule` / `list_rules`	Rules
`add_function` / `update_function` / `list_functions`	Functions
`add_knowledge` / `search_knowledge`	Knowledge base

Verifying:

Tool	Purpose
`test_agent`	Exercise the agent — closes the build → verify loop
`audit_agent`	Review an agent’s configuration

Reading:

Tool	Purpose
`list_responses` / `get_response`	Response history
`analytics_summary`	Aggregate analytics
`agent_analytics`	Per-agent volume and human-handled rate — finds which agent needs attention
`list_conversations`	Recent threads with channel, status, escalation flag and rating
`get_conversation`	One conversation in full, transcript in order — what the customer asked and how the agent answered

Grounding it in your stack — an agent grounded in nothing answers from nothing:

Tool	Purpose
`describe_integrations`	Whether Shopify and Gorgias are connected, with identifiers but never tokens
`connect_shopify`	Store a custom-app Admin API token, verified against the store live before it is saved
`connect_gorgias`	Same for a Gorgias domain, agent email and REST key
`import_shopify_knowledge`	Pull products, collections and policies into the knowledge base; near-duplicates update in place, so re-running refreshes rather than duplicates

Follow Your First Agent Evaluation for exact tool inputs, case read-back, grading outcomes, and partial-suite handling. The eval loop — how a review becomes a durable fix rather than a note:

Tool	Purpose
`create_eval`	Record what the agent should have said against a real `response_id`; becomes a regression case and fine-tuning data
`run_eval`	Replay one eval and grade on substance, not wording. `passed` is `null`, never `false`, when grading could not run
`run_evals`	Run the suite for a pass/fail count. Each costs a real generation, so runs are sequential and capped at 20
`list_evals` / `get_eval`	What has already been captured — check before writing a new one

Maintaining what you built:

Tool	Purpose
`get_rule` / `get_function`	Read one by id before patching, so an update starts from current state
`test_function`	Probe a tool: reachable, credentials still valid, answers in time. A broken tool fails silently mid-conversation
`update_knowledge`	Correct a document in place — adding a correction instead leaves the agent two contradictory sources
`delete_knowledge`	Remove an obsolete document. The only delete in the API, and not reversible
`list_changes`	The configuration change log: what changed, when, by whom — including changes made through this server
`switch_organization`	OAuth connections only: change which organization subsequent calls act on

Resources

Re-readable context a client can attach without spending a tool call — notably vocabulary and workspace.

The build → verify loop

The intended flow for an agent building an agent:

describe_workspace and describe_vocabulary to orient.
provision_agent to create the whole thing atomically.
test_agent to exercise it.
audit_agent, then update_rule / update_function to correct.

Errors come back as safe, model-readable messages — internals are never leaked.

Local analytics server

A stdio MCP server that lets Claude Desktop call the Response analytics and reporting API through a small authenticated bridge.

Run it

RESPONSE_API_BASE_URL=http://localhost:3000 \
RESPONSE_API_TOKEN=<cli-token> \
RESPONSE_ORG_ID=<org-id> \
npm run mcp:response-api

Optional:

RESPONSE_INTERNAL_SECRET (or INTERNAL_API_SECRET) adds x-internal-secret for internal-only routes.
RESPONSE_MCP_MAX_RESPONSE_CHARS caps large tool responses. Default 60000.

Claude Desktop

{
  "mcpServers": {
    "response-analytics": {
      "command": "node",
      "args": ["/path/to/scripts/response-api-mcp-server.mjs"],
      "env": {
        "RESPONSE_API_BASE_URL": "https://your-response-host",
        "RESPONSE_API_TOKEN": "<cli-token>",
        "RESPONSE_ORG_ID": "<org-id>"
      }
    }
  }
}

Tools

Tool	Returns
`response_analytics_overview`	The comprehensive reporting payload: response volume, ratings, channels, AI resolution rate, widget activity, commerce signals, question themes, support performance, funnel steps, response times
`response_widget_analytics`	The widget/chat subset: conversations, messages, widget events, funnel, handoff/containment, response time, commerce activity
`response_dashboard_stats`	Compact dashboard stats
`response_responses_created`	Recently created responses, filterable by date range, channel, rating, function-call status, and search
`response_response_analytics_export`	Response rows for analytics and export
`response_response_count`	Total response count for the authenticated organization
`response_chat_conversations`	Chat/widget conversations with manager reporting fields — latest customer/agent messages, escalation and review state, status, counts, linked response signals
`response_chat_thread`	One conversation transcript with recent messages, counts, attachments, and response history
`response_cx_reporting_bundle`	The core CX reporting dataset in one call
`response_api_health`	Checks `/api/health`
`response_reporting_api_request`	Operator/debug escape hatch for allowlisted `/api/...` routes
`response_mcp_servers`	Available MCP integrations for the organization

CX reporting flow

For a broad report, start with response_cx_reporting_bundle. For deeper evidence, use response_responses_created to pull filtered response rows and response_chat_conversations to find relevant threads. Use response_chat_thread when you need the actual transcript.

The analytics server only accepts /api/... paths and never writes logs to stdout, so the MCP JSON-RPC channel stays clean.

Last modified on September 20, 2026

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Remote agent-building server

Tools

Resources

The build → verify loop

Local analytics server

Run it

Claude Desktop

Tools

CX reporting flow

​Remote agent-building server

​Tools

​Resources

​The build → verify loop

​Local analytics server

​Run it

​Claude Desktop

​Tools

​CX reporting flow

​Related

Remote agent-building server

Tools

Resources

The build → verify loop

Local analytics server

Run it

Claude Desktop

Tools

CX reporting flow

Related