Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Animal Map Vision MCP inspect map views of the polar-coast biome"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP local cho Google Antigravity và Gemini 3.6 Flash kiểm tra map Anima Engine bằng hai lớp bằng chứng:
Gemini nhận ảnh viewport thật qua MCP image content để nhìn lỗi hình ảnh.
Validator xác định kiểm tra geometry, navigation, panorama/alignment và độ hợp lý loài–biome–khí hậu–môi trường.
Server chỉ đọc file bên trong ANIMAL_MAP_ROOT, chặn traversal/symlink thoát root và
không có tool sửa hoặc xóa file.
Tool | Chức năng |
| Tìm screenshot, manifest, panorama, depth/normal, camera, navmesh và mesh/3DGS |
| Gửi tối đa 8 ảnh PNG/JPEG/WebP trực tiếp cho Gemini |
| Chạy quality gate geometry, navigation, pipeline và ecology |
| Tạo brief chuyên trách cho |
Related MCP server: Roblox AI Agents
Yêu cầu Node.js 20 trở lên.
git clone https://github.com/DuongNAD/mcp-vision.git
cd mcp-vision
npm ci
npm run setup:antigravity -- --project "D:\Projects\Anima-Engine"
npm run doctor -- --project "D:\Projects\Anima-Engine"Không copy cấu hình từ máy khác. setup:antigravity tự tạo đường dẫn tuyệt đối đúng
cho máy hiện tại, merge .agents/mcp_config.json, cài skill và thêm workflow bắt buộc
vào AGENTS.md. Có thể chạy lại lệnh sau khi di chuyển repository.
Khi doctor trả "ready": true:
Tạo/mở Antigravity Project chứa cả Anima-Engine và repo MCP.
Gõ /mcp, reload và kiểm tra animal-map-vision connected.
Chọn Gemini 3.6 Flash.
Gõ: Use animal-map-review and prepare a map team review.
Hướng dẫn đầy đủ cho Git, máy mới và kiểm tra agent nằm tại
docs/ANTIGRAVITY_SETUP.md.
Installer tạo ba lớp:
.agents/mcp_config.json để Antigravity khởi động MCP.
.agents/skills/animal-map-review/SKILL.md để model tự kích hoạt workflow cho nhiệm
vụ map, biome, ecology, terrain, water, navigation và lighting.
AGENTS.md bắt buộc agent gọi MCP trước khi xác nhận map hoàn tất.
Ngoài ra MCP tự công bố server instructions, mô tả tool và prompt
animal-map-team-review. Lệnh doctor bắt tay thật qua stdio và kiểm tra đủ tool/prompt.
Copy examples/anima-engine.manifest.json thành
Anima-Engine/animal-map.manifest.json, rồi nối nó với exporter thật.
Entity sinh học cần:
{
"species": "emperor_penguin",
"biomeId": "polar-coast",
"environment": {
"medium": "saltwater",
"submerged": true
}
}Loài riêng của dự án khai báo thêm ecology.allowedBiomes, allowedMedia và khoảng
temperatureC. Schema đầy đủ ở schemas/animal-map-manifest.schema.json.
Nên xuất các camera cố định:
overview.png
navigation.png
collision.png
lighting.png
spawn.png
water.png
biome-transition.png
ecosystem.png
Giữ seed, camera pose, prompt và export settings giống nhau giữa các iteration.
Chọn Gemini 3.6 Flash, gõ /teamwork-preview, rồi dùng
docs/TEAMWORK_PROMPT.md. Các workstream:
Geometry & Collision
Navigation & Gameplay
Ecology & World Coherence
Visual & Lighting
Integrator
Teamwork Preview dùng quota lớn; review theo từng biome/chunk thay vì giao toàn bộ lục địa trong một lượt.
WorldGen: panorama 360°, depth, mesh/3DGS.
Infinigen: procedural seed, camera và ground-truth buffers.
HY-World 2.0: panorama, obstacle-aware trajectory, world expansion và reconstruction alignment.
MCP không sao chép hoặc chạy các model nặng này; nó áp dụng quality gate phù hợp cho Three.js/React Three Fiber và Rust ECS của Anima Engine.
npm run check
npm run ci
npm run inspectcheck: build và chạy toàn bộ test.
ci: build, test và audit production dependencies.
inspect: mở MCP Inspector.
MCP nhìn ảnh đã capture; chưa tự điều khiển camera trong ứng dụng.
Manifest cần exporter cập nhật để phản ánh scene thật.
Navigation topology không thay thế kiểm tra slope/clearance bằng dữ liệu hoặc ảnh debug.
Server hiện dùng stdio local. Remote deployment cần Streamable HTTP và authentication.
Read-only inventory of map screenshots, manifests, and world-generation stage outputs inside the configured Animal Engine project root. Call this first.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint as true. The description adds concrete behavioral context by specifying it inventories 'screenshots, manifests, and world-generation stage outputs' and limits scope to the configured project root, which enhances transparency beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the purpose and scope, the second provides a usage directive. No extraneous words, all information is pertinent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and annotations cover safety, the description fully addresses what the tool does and when to use it. It provides enough context for an agent to invoke it correctly as a first step.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters and schema coverage is 100%. Per guidelines, baseline for 0 params is 4. The description adds overall context but there are no parameters to describe.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'read-only inventory' of specific artifacts (screenshots, manifests, stage outputs) within a defined scope (Animal Engine project root). It distinguishes from siblings by suggesting it be called first, implying a discovery step before inspection or validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Call this first,' providing clear guidance on when to use the tool. It lacks explicit 'when not to use' statements, but the context of siblings implies other tools are for subsequent steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns PNG/JPEG/WebP viewport captures as actual MCP image content so Gemini can visually inspect seams, holes, floating assets, navigation, collision, lighting, spawn safety, and consistency. Paths must come from discover_map_artifacts.
| Name | Required | Description | Default |
|---|---|---|---|
| focus | No | geometry, collision, navigation, lighting, spawn safety, and visual consistency | |
| paths | Yes | Relative image paths inside the configured map root. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds value by stating the output is image content and listing specific visual checks, which enhances transparency beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two focused sentences: the first describes the output and inspection capability, the second states the prerequisite. No wasted words; front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with two parameters and no output schema, the description covers the output format, prerequisite, and inspection purpose. It lacks detail about the focus parameter and error handling, but is sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (only 'paths' documented in schema). The description adds some context by listing inspection areas, hinting at the purpose of the 'focus' parameter, but does not explicitly explain parameter semantics or defaults, leaving room for ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns viewport captures as image content for visual inspection of specific map quality aspects like seams and collisions. It distinguishes itself from siblings by explicitly linking to discover_map_artifacts as the source of valid paths.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly requires paths to come from discover_map_artifacts, providing a clear prerequisite. While it does not list when to avoid using the tool, the context implies it is for visual inspection after discovering artifacts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Builds an evidence-first review brief with four parallel roles for Antigravity /teamwork-preview. It inventories artifacts and includes deterministic manifest issues when a manifest is available.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | Produce a playable, visually coherent, animal-safe map. | |
| manifestPath | No | Optional relative manifest path. Defaults to the first discovered manifest. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds behavioral context: 'It inventories artifacts and includes deterministic manifest issues when a manifest is available.' This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, each conveying essential information. The first sentence states the core action, and the second adds relevant detail. No superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no required parameters and no output schema, the description covers the main behavior. However, it lacks details about the output (e.g., what the review brief contains) and does not mention any prerequisites or subsequent steps. This is adequate but not fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50% (manifestPath has a description, goal does not). The description adds context about using a manifest and inventorying artifacts, which clarifies manifestPath's role. However, it does not explain the 'goal' parameter beyond its default value. This provides moderate added value but not full compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool builds an 'evidence-first review brief with four parallel roles' for a specific context ('Antigravity /teamwork-preview'). This is a specific verb-resource combination that clearly distinguishes it from sibling tools (discover_map_artifacts, inspect_map_views, validate_map_manifest).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given on when to use this tool versus its siblings. For example, it does not mention prerequisites (e.g., needing a manifest discovered) or scenarios where this tool is appropriate. The description only states what it does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Runs deterministic map quality gates: schema, bounds, unique ids, colliders, spawn-to-exit connectivity, navmesh coverage, 2:1 panorama, aligned depth/normals/cameras, and species-biome-climate-medium plausibility.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Relative manifest path inside the configured map root. | animal-map.manifest.json |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by listing the specific quality gates performed, providing richer behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently enumerates all validation checks without redundancy or filler. Every listed item earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains what the tool does comprehensively, but lacks information about the output or return value. Since there is no output schema, the agent may need to know whether results are detailed or a simple pass/fail.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single parameter 'path' with description. The tool description adds no additional semantic meaning to the parameter beyond what the schema already provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Runs deterministic map quality gates' and enumerates specific checks (schema, bounds, unique ids, etc.). The verb 'validate' in the name is reinforced, and the tool is distinct from siblings which focus on discovery, inspection, and review.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a manifest needs validation, but no explicit guidance on when to choose this over siblings or prerequisites. The agent must infer context from the sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0discover_map_artifactsinspect_map_viewsprepare_team_reviewvalidate_map_manifestScored across 4 tools
Each tool targets a distinct phase of map analysis: inventory, visual inspection, validation, and review preparation, with no overlap.
All tools follow a consistent verb_noun snake_case pattern, making the purpose clear from the name.
Four tools is well-scoped for a map review workflow, avoiding unnecessary bloat while covering key actions.
The set covers a complete workflow from discovery to review, with no obvious missing operations for the intended domain.
AI game assets for agents: consistent sprites, 2D animations, tiles, maps, music and engine exports.
Preflight QA for AI-agent deliverables with structured verdicts and repair guidance.
Build, validate, and deploy multi-agent AI solutions from any AI environment.
Browser-based QA for AI-built software. Test pages with real browsers via agents.
