Robotics & Spatial Vision MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| BASE_RPC_URL | Yes | Base L2 RPC URL used for payment settlement. | |
| PAYMENT_WALLET | Yes | Recipient payout wallet address for x402 Base L2 micropayments. |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| pointcloud_icp_rigid_registrationB | Iterative Closest Point (ICP) algorithm aligning two unorganized 3D point clouds with robust Tukey loss weighting to filter noisy scan outliers. (0.045 USDC on Base L2) |
| navmesh_recast_baking_validatorA | Generates and audits Recast 3D navigation meshes for autonomous agents, checking maximum slope climbing, step-height clearance, and unreachable islands. (0.040 USDC on Base L2) |
| robot_arm_inverse_kinematics_ccdB | Cyclic Coordinate Descent (CCD) and FABRIK solver computing 6-DOF robotic manipulator joint angles with strict joint-angle limit enforcement. (0.040 USDC on Base L2) |
| lidar_ground_plane_ransac_filterA | RANSAC plane segmentation isolating ground terrain from foreground obstacles and vehicles in dense unorganized LIDAR point clouds. (0.035 USDC on Base L2) |
| camera_intrinsics_projection_matrixB | Computes 4x4 perspective and orthographic camera projection matrices, mapping FOV, focal length, sensor dimensions, and near/far clipping planes. (0.020 USDC on Base L2) |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a clearly distinct algorithmic task: point cloud registration, navmesh baking, inverse kinematics, ground plane filtering, and camera projection matrices. There is no overlap in purpose and an agent can select unambiguously from names alone.
All names are consistently snake_case and descriptively compound (domain + algorithm/method), so style is uniform. The only weakness is that they are noun phrases rather than a predictable verb_noun pattern, and the internal ordering of qualifiers varies slightly across tools.
Five tools is well within the ideal range and each maps to a distinct spatial-vision capability, so nothing feels padded or redundant. The set is tightly scoped to a specialized robotics domain.
The surface covers several core spatial perception tasks (registration, segmentation, kinematics, projection, navigation), giving a usable toolkit. However a broad 'Robotics & Spatial Vision' domain suggests missing operations like point-cloud I/O, coordinate transforms, forward kinematics, or path planning, which agents would need to work around.