MCP server that gives AI coding assistants deep knowledge of the FTC Robot Controller ecosystem. Enables teams to "vibe code" their robots through natural language while producing correct, optimized, competition-ready Java code.
The problem: AI assistants hallucinate wrong Pedro Pathing APIs (training data is outdated), don't know the @Config + public static dashboard pattern, use wrong import paths, and can't see your project structure.
The fix: This MCP server injects 9,500+ lines of verified FTC documentation, API references, and working code examples directly into your AI assistant's context.
claude mcp add ftc -- npx ftc-mcpOr use the one-click button above.
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"ftc": {
"command": "npx",
"args": ["-y", "ftc-mcp"]
}
}
}
Or add to .vscode/mcp.json:
{
"servers": {
"ftc": {
"command": "npx",
"args": ["-y", "ftc-mcp"]
}
}
}Add to your Windsurf MCP config:
{
"mcpServers": {
"ftc": {
"command": "npx",
"args": ["-y", "ftc-mcp"]
}
}
}Drop this file in your project root:
{
"mcpServers": {
"ftc": {
"command": "npx",
"args": ["-y", "ftc-mcp"]
}
}
}git clone https://github.com/jackulau/ftcMCP.git
cd ftcMCP
npm install
npm run build
# Then add to your AI client:
claude mcp add ftc -- node /path/to/ftcMCP/build/index.jsContextual docs the AI pulls in when writing FTC code:
| Category | Resources | Coverage |
|---|---|---|
| Pedro Pathing 2.0 | 6 | Complete API (Follower, PathBuilder, PathChain, BezierLine/Curve), Constants builder pattern, coordinate system [0,144], auto FSM structure, TeleOp drive, callbacks |
| FTC Dashboard | 6 | @Config + public static pattern, copy semantics pitfall, MultipleTelemetry, TelemetryPacket, Canvas field overlay API, camera streaming, setup |
| Panels (by Lazar) | 8 | Overview & comparison with FTC Dashboard, setup & Gradle config, @Configurable live tuning, PanelsTelemetry, PanelsField canvas drawing, Limelight proxy, plugin architecture, gamepad support |
| Gradle | 5 | Project file structure, adding libraries step-by-step, exact Maven coordinates for every library, common issues (compileSdk 34 for Pedro), build process |
| Hardware | 17 | DcMotor/DcMotorEx full API, RunModes, motor specs (every goBILDA/REV CPR), servos, IMU, distance/color/touch sensors, encoders (port 0+3 vs 1+2), GoBilda Pinpoint, SparkFun OTOS, REV Hub internals, bulk reads, CachingHardware, custom wrapper patterns, VisionPortal + Limelight |
| Core SDK | 5 | OpMode lifecycle (iterative vs linear), hardwareMap patterns, gamepad API, best practices |
| Road Runner | 1 | Actions API, TrajectoryActionBuilder |
| FTCLib | 1 | Command-based framework, GamepadEx, triggers |
| Tool | What It Does |
|---|---|
scan_ftc_project |
Reads your TeamCode directory -- detects SDK version, installed libraries, existing OpModes, hardware config, Pedro Constants. Call at the start of every session. |
scan_hardware_config |
Parses all hardwareMap.get() calls to extract every hardware device name and type in your project. |
search_ftc_docs |
Full-text search across the entire knowledge base. Filter by category: pedro, dashboard, gradle, hardware, sdk, etc. |
get_ftc_example |
Returns a complete, compilable Java file for any of 10 topics (see below). |
get_hardware_reference |
Deep API reference for any device: DcMotorEx, Pinpoint, OTOS, bulk-reads, caching-hardware, etc. |
validate_ftc_code |
Checks code for common FTC mistakes: missing follower.update(), @Config with final, Thread.sleep in iterative OpMode, wrong Pedro v1 imports, copy semantics bugs. |
Every example is a full, compilable Java file with package declaration, all imports, and working code:
| Topic | Description |
|---|---|
pedro-auto |
Pedro Pathing autonomous with FSM state machine, path callbacks, @Config tunable poses, field overlay |
pedro-teleop |
Pedro TeleOp with setTeleOpDrive(), slow mode, bulk reads, loop timer |
pedro-constants |
Complete Constants.java with FollowerConstants, MecanumConstants, PinpointConstants builders |
dashboard-config |
@Config demonstration with correct/wrong copy semantics examples |
bulk-reads |
Optimized OpMode with LynxModule MANUAL + CachingHardware |
subsystem |
Hardware subsystem class with @Config positions, state methods |
pid-tuning |
Live PID tuning with dashboard-graphed error/output |
vision-pipeline |
VisionPortal + AprilTag processor with dashboard camera stream |
custom-pid-drive |
Encoder-based autonomous with IMU heading PID (no pathing library) |
field-centric-drive |
Field-centric mecanum TeleOp using IMU |
Structured instructions that guide the AI through complex FTC tasks:
| Prompt | Description |
|---|---|
setup-ftc-project |
Guided project init: choose pathing lib, configure Gradle, add dashboard |
create-autonomous |
Full auto creation: poses, paths, FSM, callbacks, dashboard telemetry |
create-teleop |
TeleOp: drive type, subsystems, gamepad bindings, slow mode |
create-subsystem |
Hardware subsystem with @Config tuning, state methods |
tune-pid |
PID tuning with dashboard live graphing |
optimize-performance |
Bulk reads, CachingHardware, loop timer, I2C reduction |
add-dashboard-tuning |
Add @Config live-tunable variables to existing code |
setup-gradle |
Configure Gradle deps for any combination of FTC libraries |
| Library | Version | Knowledge Depth |
|---|---|---|
| FTC SDK | 11.1.0 | Full hardware API, OpMode lifecycle, gamepad, telemetry |
| Pedro Pathing | 2.0.6 | Complete v2.0 API with builder patterns (NOT the outdated v1.x) |
| FTC Dashboard | 0.5.1 | @Config, MultipleTelemetry, Canvas, camera streaming |
| Panels | 1.0.12 | @Configurable, PanelsTelemetry, PanelsField, Limelight proxy, plugin architecture, gamepads, capture/replay |
| Road Runner | 1.0.x | Actions API, TrajectoryActionBuilder |
| CachingHardware | 1.0.0 | Write caching algorithm, drop-in wrappers |
| FTCLib | 2.1.1 | Command-based framework, GamepadEx |
Full API documentation and initialization patterns for:
- Motors: DcMotor, DcMotorEx, all RunModes, PIDF coefficients, every goBILDA/REV/NeveRest motor with exact CPR
- Servos: Servo, ServoImplEx (PWM range), CRServo, power pairing rules
- Sensors: REV IMU, Color Sensor V3, 2m Distance Sensor, Touch Sensor, Through Bore Encoder
- Localizers: goBILDA Pinpoint (full driver API, offsets, status enum), SparkFun OTOS (scalars, calibration)
- Vision: VisionPortal, AprilTagProcessor, Limelight 3A
- REV Hub: LynxModule bulk reads (OFF/AUTO/MANUAL), I2C timing, encoder port hardware vs software decoding
"Set up my project with Pedro Pathing and Dashboard"
AI calls
scan_ftc_project-> readsftc://gradle/all-library-coords-> editsbuild.dependencies.gradlewith exact repos and versions -> changescompileSdkto 34 -> creates Constants.java with builder pattern
"Create an autonomous that scores 3 samples"
AI reads
ftc://pedro/api-reference+ftc://pedro/auto-structure-> generates complete OpMode with @Config tunable poses, FSM state machine, path callbacks, MultipleTelemetry, field overlay
"My loop times are slow"
AI reads
ftc://hardware/bulk-reads+ftc://hardware/caching-hardware-> adds LynxModule MANUAL + CachingDcMotorEx + loop timer telemetry
"Add a dashboard variable so I can tune arm position"
AI reads
ftc://dashboard/config-pattern-> adds@Configclass withpublic static double ARM_POSITION = 0.5;-> warns about reading it fresh each loop (copy semantics)
ftc-mcp/
├── src/
│ ├── index.ts # Entry point (stdio transport)
│ ├── server.ts # McpServer setup
│ ├── knowledge/
│ │ ├── pedro.ts # Pedro Pathing 2.0 (1,550 lines)
│ │ ├── hardware.ts # Full hardware stack (1,479 lines)
│ │ ├── examples.ts # 10 complete code examples (1,396 lines)
│ │ ├── ftc-sdk.ts # SDK patterns (881 lines)
│ │ ├── dashboard.ts # FTC Dashboard (845 lines)
│ │ ├── panels.ts # Panels by Lazar — all-in-one dashboard
│ │ ├── ftclib.ts # FTCLib framework (636 lines)
│ │ ├── gradle.ts # Gradle build system (584 lines)
│ │ └── roadrunner.ts # Road Runner (478 lines)
│ ├── resources/registry.ts # 41 resource URI registrations
│ ├── tools/registry.ts # 6 tool implementations
│ └── prompts/registry.ts # 8 workflow prompts
├── package.json
└── tsconfig.json
npm install
npm run build # Compile TypeScript
npm run dev # Watch mode
npm start # Run the server- Node.js >= 18
- An MCP-compatible AI client (Claude Code, Cursor, VS Code Copilot, etc.)
MIT