Oneiros MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| encode_observationA | Encode a 6D observation [x, y, vx, vy, goal_x, goal_y] into a latent vector. Returns the latent embedding produced by the trained JEPA encoder. The latent is the space the world model predicts in; downstream tools operate on these latents rather than on raw observations. |
| predict_rolloutA | Roll the learned latent dynamics forward from a latent over an action sequence. Given a starting latent and a list of 2D acceleration actions, applies the predictor g step by step and returns the latent trajectory. This is the model imagining a future without touching the real environment. |
| plan_to_goalA | Plan the next action that drives the agent toward a goal, via latent MPC. Encodes the current and goal observations, searches action sequences by rolling the world model forward in latent space (cross-entropy method), and returns the first action of the best plan. Call repeatedly (replanning each step) to follow a receding-horizon trajectory to the goal. |
| reset_envA | Reset the point-mass environment to a deterministic start for the given seed. Returns the initial observation, the agent state, and the goal position. |
| step_envA | Apply a 2D acceleration action to the environment and advance one timestep. Returns the new observation, reward (negative distance to goal), a done flag (agent reached the goal), and the distance to the goal. |
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 has a clear, distinct purpose: encoding observations, planning to a goal, predicting rollouts, resetting the environment, and stepping the environment. No two tools overlap in function.
All tool names follow a consistent verb_noun pattern (encode_observation, plan_to_goal, predict_rollout, reset_env, step_env) and use snake_case uniformly.
With 5 tools, the server covers the core operations for a point-mass control domain with latent dynamics perfectly. The count is well-scoped and each tool earns its place.
The tool set covers the main workflow: observation encoding, planning, prediction, environment reset and stepping. A minor gap is the lack of a tool to directly set or inspect the goal, but it is implicitly covered via encode_observation and plan_to_goal.