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  3. Orca MCP Server
Orca MCP Server logo
Health: ActiveRecent health check succeeded.Last checked 9/7/2026, 8:43:54 PM

Orca MCP Server

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository14 GitHub StarsTotal stargazers on GitHub for the source repository (14 stars).Visit Website

Go from natural language to verified finite state machines β€” topology bugs caught before code runs.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "orca-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@orcalang/orca-mcp-server"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

Orca

CI npm Node 20+ Ask DeepWiki

Orchestrated State Machine Language β€” a two-layer architecture for reliable LLM code generation.

The core insight: LLMs generate flat transition tables reliably, but they struggle to guarantee topology correctness on their own. Orca separates program structure (state machine topology) from computation (action functions), then verifies the structure automatically before any code runs.

Machines are written in plain Markdown β€” a format LLMs can read and write natively.


What it looks like

markdown
# machine PaymentProcessor

## context

| Field       | Type    | Default |
|-------------|---------|---------|
| order_id    | string  |         |
| amount      | decimal |         |
| retry_count | int     | 0       |

## events

- submit_payment
- payment_authorized
- payment_declined
- retry_requested
- settlement_confirmed

## state idle [initial]
> Waiting for a payment submission

## state authorizing
> Waiting for payment gateway response
- on_entry: send_authorization_request

## state declined
> Payment was declined

## state settled [final]
> Payment fully settled

## transitions

| Source      | Event                | Guard      | Target      | Action           |
|-------------|----------------------|------------|-------------|------------------|
| idle        | submit_payment       |            | authorizing |                  |
| authorizing | payment_authorized   |            | settled     |                  |
| authorizing | payment_declined     |            | declined    |                  |
| declined    | retry_requested      | can_retry  | authorizing | increment_retry  |
| declined    | retry_requested      | !can_retry | settled     | record_failure   |

## guards

| Name      | Expression              |
|-----------|-------------------------|
| can_retry | `ctx.retry_count < 3`   |

## actions

| Name                       | Signature                                | Effect      |
|----------------------------|------------------------------------------|-------------|
| send_authorization_request | `(ctx) -> Context`                       | AuthRequest |
| increment_retry            | `(ctx) -> Context`                       |             |
| record_failure             | `(ctx) -> Context`                       |             |

## effects

| Name        | Input                              | Output                   |
|-------------|------------------------------------|--------------------------|
| AuthRequest | `{ order_id: string, amount: decimal }` | `{ token: string }`  |

The verifier checks this before anything runs: reachability, deadlocks, guard determinism, orphan declarations, and effect consistency.


Features

Language

  • States with [initial] / [final] markers, descriptions, on_entry / on_exit actions
  • Transitions as a flat table β€” the format LLMs generate most reliably
  • Guard expressions: comparisons, boolean logic, null checks
  • Hierarchical (nested) states
  • Parallel regions with all-final / any-final / custom sync strategies
  • Timeouts: timeout: 30s -> state_name
  • Ignored events: ignore: EVENT_NAME
  • Machine invocation: one machine calling another, with input mapping and completion events
  • Multi-machine files: multiple machines in one .orca.md separated by ---
  • ## effects section: named I/O schemas for external side effects
  • Decision tables: co-located conditional logic without guard explosion β€” verified for completeness, consistency, and cross-machine reachability

Verifier

  • Reachability: every state is reachable from the initial state
  • Deadlock detection: every non-final state has an outgoing transition
  • Completeness: every (state, event) pair is handled or explicitly ignored
  • Guard determinism: multi-transition guards are mutually exclusive
  • Property checking: bounded model checking with BFS β€” reachable, unreachable, passes_through, live, responds, invariant
  • Cross-machine: cycle detection, child reachability to final state, input mapping validation
  • Effect consistency: ORPHAN_EFFECT (declared but unused) and UNDECLARED_EFFECT (referenced but not declared)
  • Decision table checks: completeness, consistency, redundancy, coverage gap, dead guards, DT-constrained reachability β€” see DECISION_TABLES.md

Compilers

  • XState v5 createMachine() config
  • Mermaid stateDiagram-v2

Runtimes (standalone β€” no XState dependency)

  • TypeScript (@orcalang/orca-runtime-ts)
  • Python (orca-runtime-python)
  • Go (orca-runtime-go)

All three runtimes share the same feature set: guard evaluation, action registration, event bus (pub/sub + request/response), timeouts, parallel regions, snapshot/restore, machine invocation, persistence, and structured logging.


Monorepo structure

server.ts
packages/
  orca-lang/       Core: parser, verifier, XState/Mermaid compiler, CLI
  runtime-ts/      TypeScript runtime
  runtime-python/  Python async runtime
  runtime-go/      Go runtime
  demo-ts/         Text adventure game (uses runtime-ts)
  demo-python/     Agent framework scenarios (uses runtime-python)
  demo-go/         Ride-hailing coordinator β€” 5 machines (uses runtime-go)
  demo-nanolab/    nanoGPT training orchestrator β€” 5 machines (uses runtime-python)
  mcp-server/      MCP server exposing Orca tools to Claude and other agents

Setup

bash
# TypeScript packages
pnpm install
pnpm build

# Python packages (runtime + demos, requires Python >= 3.11)
pnpm run setup:python

# Go packages
pnpm run setup:go
pnpm run build:demo-go

CLI

bash
cd packages/orca-lang

# Verify a machine
npx tsx src/index.ts verify examples/payment-processor.orca.md

# Compile to XState
npx tsx src/index.ts compile xstate examples/payment-processor.orca.md

# Compile to Mermaid
npx tsx src/index.ts compile mermaid examples/text-adventure.orca.md

# Convert legacy .orca to .orca.md
# npx tsx src/index.ts convert <path-to-legacy.orca>

Language features

Parallel regions

markdown
## state processing [parallel]
> Payment and notification run concurrently
- on_done: -> completed

### region payment_flow

#### state charging [initial]
#### state paid [final]

### region notification_flow

#### state sending_email [initial]
#### state notified [final]

The machine transitions to completed when both regions reach their final state (all-final sync, the default).

Machine invocation

markdown
---

# machine OrderCoordinator

## state processing_payment
- invoke: PaymentProcessor
- on_done: payment_confirmed
- on_error: payment_failed

---

# machine PaymentProcessor

## state idle [initial]
## state settled [final]
...

The parent owns the child's lifecycle: starts it on entry, stops it on exit. The child's context is isolated from the parent's.

Timeouts

markdown
## state waiting_for_response
> LLM call in progress
- timeout: 30s -> timed_out

Snapshot and resume

All runtimes support saving and restoring machine state:

server.ts
// Save
const snap = machine.snapshot();
persistence.save('run-id', snap);

// Resume later (without re-running on_entry)
const snap = persistence.load('run-id');
await machine.resume(snap);

Structured logging

server.ts
import { MultiSink, FileSink, ConsoleSink, makeEntry } from '@orcalang/orca-runtime-ts';

const sink = new MultiSink(new ConsoleSink(), new FileSink('audit.jsonl'));

const m = new OrcaMachine(def, bus, {
  onTransition: (oldState, newState) => {
    sink.write(makeEntry({ runId, machine: def.name, from: oldState.toString(), to: newState.toString(), ... }));
  }
});

Using a runtime

TypeScript

server.ts
import { parseOrcaAuto, OrcaMachine, EventBus } from '@orcalang/orca-runtime-ts';

const def = parseOrcaAuto(source);
const bus = new EventBus();
const machine = new OrcaMachine(def, bus);

machine.registerAction('send_authorization_request', (ctx, event) => {
  return { ...ctx, payment_token: 'tok_123' };
});

machine.start();
machine.send({ type: 'submit_payment', payload: { order_id: 'ord_1', amount: 99.99 } });

Python

server.ts
from orca_runtime_python import parse_orca_auto, OrcaMachine, EventBus

def_ = parse_orca_auto(source)
bus = EventBus()
machine = OrcaMachine(def_, bus)

@machine.register_action('send_authorization_request')
async def send_auth(ctx, event):
    return {**ctx, 'payment_token': 'tok_123'}

await machine.start()
await machine.send({'type': 'submit_payment', 'payload': {'order_id': 'ord_1', 'amount': 99.99}})

Go

server.ts
import "orca-runtime-go/orca_runtime_go"

def, _ := orca_runtime_go.ParseOrcaAuto(source)
bus := orca_runtime_go.NewEventBus()
machine := orca_runtime_go.NewOrcaMachine(def, bus, nil, nil)

machine.RegisterAction("send_authorization_request", func(ctx map[string]any, event orca_runtime_go.Event) map[string]any {
    ctx["payment_token"] = "tok_123"
    return ctx
})

machine.Start()
machine.Send(orca_runtime_go.Event{Type: "submit_payment"})

Running the demos

bash
# Text adventure (TypeScript) β€” interactive CLI
cd packages/demo-ts && pnpm run cli

# Smoke test (non-interactive)
pnpm test:demo-ts

# Agent framework (Python)
pnpm run test:demo-python

# Ride-hailing coordinator (Go) β€” runs FareSettlement end-to-end
pnpm run test:demo-go
# With snapshot/resume:
cd packages/demo-go && ./trip --resume

# nanoGPT training orchestrator (Python, no torch required for tests)
pnpm run test:demo-nanolab

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Orca MCP Server

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "orca-mcp-server": { "command": "npx", "args": ["-y", "Orca MCP Server"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars14
GitHub Star CountTotal stargazers on GitHub representing community popularity (14 stars).
39Quality signal: Fair Β· 39/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools16/30
Adoption & activity3/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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