Documentation Index
Fetch the complete documentation index at: https://docs.kaireonai.com/llms.txt
Use this file to discover all available pages before exploring further.
KaireonAI ships an MCP (Model Context Protocol) server that exposes
the platform’s data, decisioning, and pipeline surfaces as tools any
MCP-aware AI assistant can call. From an external agent’s perspective,
KaireonAI is another set of tools — the same way dbt, Snowflake,
and Atlan expose theirs.
What’s exposed
The server registers 130 tools across six primitive modules + a
playbook layer. Counts verified against the actual MCP tool
registrations and the bundled playbook definitions.
| Module | Tools | Highlights |
|---|
| Flow | 11 | Create/update/run IR-native pipelines, version history, run-error inspection, replay, customer scoring |
| Data | 16 | Schemas (incl. inbound-FK probe), connectors, transform-type catalog, raw data ops |
| Studio | 46 | Offers, decision flows, qualification rules, channels, journeys, contact policies, creates + updates |
| Algorithms | 13 | Models, training, predictors, SHAP |
| Operations | 8 | Alerts, audit log, approvals, tenant settings |
| AI | 26 | AI configuration helpers, Pipeline Mode coordinator entry points |
| Playbooks | 10 | Multi-step workflows: bootstrap-new-offer-campaign, rebuild-offer-qualification, promote-challenger-if-winning, audit-tenant-data-health, run-shadow-experiment, explain-decision-chain, generate-dsar-export, simulate-weight-change, explain-algorithm-upgrade, arbitrate-policy-conflict |
Running the server
cd platform
npm install
npm run mcp
npm run mcp command.
Auth
The server reads KAIREON_API_KEY and KAIREON_TENANT_ID from the
environment and forwards them as X-API-Key + X-Tenant-Id on every
HTTP call to /api/v1/*. Provision a krn_* tenant key in
Settings → Integrations → API Keys and export it before launching.
Read-only by default in production
Write tools (createFlowPipeline, updateFlowPipeline, runFlowPipeline,
replayFlowRun, testFlowConnector, scoreCustomer, plus all writes
in other modules) are disabled in production unless MCP_ALLOW_WRITES=true
is set. Calling a blocked tool returns a structured error explaining
the gate. Read tools always work.
| Tool | Verb | Purpose |
|---|
createFlowPipeline | write | Create an IR-native pipeline. Body: { name, connectorId, schemaId, ir }. The server validates ir via parsePipelineIR before persistence. |
getFlowPipelineIr | read | Return the latest IR document for a pipeline. 409 if the pipeline is still in legacy node/edge format. |
updateFlowPipeline | write | Save a new IR version against an existing pipeline. Auto-promotes legacy pipelines to IR-native on first save. |
listFlowPipelineVersions | read | IR version history (desc by version) — version, authoredBy, comment, createdAt. |
runFlowPipeline | write | Trigger a run. IR-native pipelines dispatch in-process via runBatch; legacy pipelines go to the BullMQ worker queue. |
listFlowRuns | read | Recent runs for a pipeline. |
inspectFlowError | read | Error context for a specific run — supports AI-driven self-healing workflows. |
replayFlowRun | write | Re-run a pipeline from a previous run id. |
testFlowConnector | write | Test connection ping for a connector — verifies credentials + reachability. |
createYamlConnector | stub | Documented surface for Phase 5; currently returns { status: "deferred", phase: "5" }. |
scoreCustomer | write | Wraps POST /api/v1/recommend so external agents can score customers via MCP. |
Decisioning primitives (existing)
Already exposed by studio-tools.ts and reusable directly by external agents:
listOffers, createOffer, updateOfferlistDecisionFlows, createDecisionFlow, updateDecisionFlowlistQualificationRules, createQualificationRulelistChannels, listContactPolicies, listJourneys, getJourney,
listInteractions, plus more
This means an external agent has full read + (gated) write access to
the decisioning model — first-party MCP for Next-Best-Action.
Validation contract
Every IR-native write goes through three checks server-side:
- HTTP body validation — Zod schemas on each route.
parsePipelineIR — Phase 1 two-phase validator (Zod schema +
structural acyclic + ref-integrity).- Audit logging — every write writes an audit-log row. AI-authored
writes additionally land under
entityType='pipeline_ai_proposal'.
Invalid IR is rejected with HTTP 400 and a structured errors array
the agent can use to retry with a corrected proposal — same contract
the in-app AI Pipeline Mode (Phase 2a) uses.
Roadmap
| Phase | What this gets |
|---|
| 2b (this page) | 11 Flow tools live; existing decisioning tools documented as part of the unified surface. |
| 5 | YAML connector authoring lights up createYamlConnector for real. |
| Future | Streamable HTTP transport (currently stdio only); MCP Apps registry submission. |
