posecode
OfficialThis server lets you author, validate, and render human movement animations described in the Posecode language, enabling AI agents to generate 3D kinematic movement visualizations.
Learn the Posecode language (
posecode_authoring_guide): Retrieve a complete guide covering the.posecodegrammar, available joints, movement actions, and examples — meant to be read before authoring any movement.Validate a Posecode document (
validate_posecode): Parse and check a.posecodedocument for syntax errors and range-of-motion (ROM) safety violations, returning any issues and applied safety clamps.Render a movement to a shareable link (
render_posecode): Validate a.posecodedocument and generate a permalink that opens an animated 3D figure in the Posecode playground, ready to share directly with a user.
Why Posecode?
Animation clips and generated trajectories can show movement, but they often hide the semantic decisions that produced it.
Posecode keeps those decisions in readable source. A human can write the document, an animation tool can emit it, or an LLM can draft it. Parsing, validation, editing, and rendering do not require an AI model.
For example:
Bend your knees, move your hips backward, and keep your chest upright.
A human may understand that instruction, but a renderer cannot reliably determine:
which joints should move,
by how many degrees,
in which coordinate frame,
over what duration,
in what sequence,
or within which physical limits.
Humans, tools, and language models all need a shared syntax for expressing movement in a renderable and testable form.
Posecode provides that missing representation.
See Posecode in 28 seconds
From readable movement source to validated 3D rendering, MCP tools, and a one-script web embed.
Movement examples
Related MCP server: pyBittle MCP Server
Why Not Diffusion Text-to-Motion?
Neural text-to-motion systems can generate impressive movement, but they introduce problems for lightweight, programmable applications.
Resource intensive
Many systems require large models and GPU-backed inference, making real-time consumer deployment expensive.
Difficult to control
They usually produce coordinate trajectories rather than editable semantic instructions.
It is difficult to request a precise change such as:
Reduce knee flexion by 10 degrees during the second phase.
Hard to validate
Black-box trajectories do not naturally expose readable joint rules, phase definitions, or range-of-motion limits.
Hard to debug
When a movement looks wrong, developers may not know which semantic instruction caused the problem.
The Posecode Approach
Posecode uses a lightweight, text-driven pipeline.
Readable: movements are stored as small
.posecodedocuments.Structured: joints, actions, angles, timings, and constraints are explicit.
Editable: developers and models can modify individual movement properties.
Fast: parsing and rendering happen client-side.
Deterministic: the same Posecode input produces the same validated representation.
Inspectable: parser warnings and fidelity checks explain problems.
Agent-friendly: the MCP server exposes generation, validation, critique, and sharing tools.
Safety-aware: authored and IK-generated angles are constrained by configured range-of-motion limits.
The Idea in 30 Seconds
A .posecode file describes movement as timed phases with targeted joint actions.
1. Write | 2. Render the movement |
|
OpenAI Build Week 2026: Posecode existed before the hackathon. During Build Week, the project was extended using Codex — running on GPT-5.6 — as the primary engineering tool for a real batch of shipped work: motion/grounding quality, language contract diagnostics, licensing restructuring, release automation, and product-facing pages. The sections below distinguish previous work from Build Week work using actual commit history, not a roadmap.
OpenAI Build Week Extension
What existed before Build Week
Before Build Week, Posecode already included:
the core
.posecodedomain-specific language,a parser and intermediate motion representation,
basic range-of-motion validation,
a Three.js/WebGL renderer,
forward kinematics,
basic inverse-kinematics and ground-lock behavior,
a browser playground,
example movement files,
shareable Posecode links,
and an MCP server foundation.
This original version was developed primarily with Claude as an AI-assisted engineering tool.
That prior work provides the foundation for the project, but it is not presented as the new hackathon contribution.
What was added during Build Week
Every item below is a merged, dated pull request built with Codex (GPT-5.6) — see Build Week Evidence for direct links.
Motion and grounding overhaul — ROM-constrained reach IK, semantic palm/fist/sole/knee/pelvis contact surfaces, multi-contact refinement, stable support handoffs, and XBot-aware grounding (#76).
Language contract and diagnostics — Posecode language/IR v0.3 custom start-pose blocks with ROM-checked overrides, live and clip-wide grounding/self-collision diagnostics, and an accessible metric floor guide (#92).
Licensing restructure — split the monorepo into an Apache-2.0 standard layer (spec, parser, share, language, LSP, VS Code) and an AGPL-3.0 product layer (render, embed, MCP, eval, playground), with a commercial-license path (#84).
Release automation — Changesets-driven npm publishing via GitHub OIDC, MCP Registry publishing, and CI validation of package versions, entry points, and tarball contents (#66).
Third-party integration readiness — Posecode 0.2 timing vocabulary (
drive/settle/flow/snap), a parser validation CLI, and embed compatibility metadata (#62).Ground-lock correctness — parser-owned validation for per-side foot/hand/elbow ground locks and back-contact support for supine movements, replacing silent acceptance of invalid contacts (#61, #64).
LLM-first landing page and product page — redesigned the landing page around a prompt → Posecode → live 3D story, and added a
/for-productspage documenting the web component, parser, renderer, and MCP server for integrators (#82, #74).Mobile and search fixes — mobile toolbar/viewer layout, natural hand orientation, and Google Search indexing corrections (#78, #65).
Build Week feature status
Motion/grounding quality overhaul shipped (#76)
Language contract + diagnostics shipped (#92)
Licensing restructure shipped (#84)
Release automation shipped (#66)
How GPT-5.6 Is Used
During Build Week, Codex sessions ran on GPT-5.6 (GPT-5.6 Terra), which is the model that powers Codex for this event. GPT-5.6 is the reasoning engine behind every Build Week change listed above: reading the existing monorepo, proposing the ROM-constrained IK and contact-surface design in #76, designing the language/IR v0.3 diagnostics in #92, and drafting the licensing boundary in #84.
A GPT-5.6-powered natural-language-to-Posecode generation feature (describe a movement in plain English, get a validated .posecode document back) is a natural next step given the existing posecode_authoring_guide MCP tool, but it is not yet built — it is not claimed as shipped functionality here.
How Codex Is Used
Codex is the primary engineering tool used for the Build Week extension.
During the hackathon period, Codex was used to:
inspect and understand the existing monorepo before each change,
design and implement the ROM-constrained reach IK and contact-surface system (#76),
design and implement the language/IR v0.3 diagnostics and floor guide (#92),
restructure package licensing across the monorepo (#84),
build the Changesets/OIDC npm and MCP Registry release pipeline (#66),
fix ground-lock validation and silent-acceptance bugs (#61, #64),
redesign the landing page and add the product integration page (#82, #74),
write unit, integration, and evaluation-harness tests for each change,
and fix mobile UI and search-indexing regressions.
Codex accelerates implementation, but the project remains human-directed. The following decisions were reviewed and selected manually: DSL semantics, system architecture, licensing boundaries, biomechanical constraints, validation policy, user experience, and acceptance or rejection of generated code.
Codex development workflow
The Build Week workflow follows this process:
Define a specific product or engineering problem.
Ask Codex to inspect the relevant implementation.
Request one or more possible approaches.
Review the trade-offs and choose the architecture.
Use Codex to implement the selected approach.
Run type checking, tests, and biomechanical evaluations (
npm run eval).Inspect failures manually.
Refine the implementation with additional Codex sessions.
Review the final changes before committing.
Build Week Evidence
All Build Week work is public, dated, and directly linked below — no placeholders.
Build Week pull requests
PR | Merged | What it did |
2026-07-15 | Posecode 0.2 timing vocabulary, validation CLI, embed compatibility | |
2026-07-15 | Per-side ground-lock validation | |
2026-07-16 | Google Search indexing fix | |
2026-07-16 | npm + MCP Registry release automation | |
2026-07-16 | Back ground-lock for supine movements | |
2026-07-16 |
| |
2026-07-17 | Motion/grounding overhaul: ROM-constrained reach IK, contact surfaces | |
2026-07-17 | Mobile viewer sizing and natural hand orientation | |
2026-07-17 | LLM-first landing page redesign | |
2026-07-17 | Apache-2.0 / AGPL-3.0 licensing restructure | |
2026-07-19 | Language/IR v0.3, grounding/self-collision diagnostics, floor guide |
Build Week comparison
Before Build Week | Added during Build Week |
Core Posecode DSL | Language/IR v0.3 custom start-pose blocks |
Basic ROM clamping | Grounding, self-collision, and floor-guide diagnostics |
Working IK/grounding | ROM-constrained reach IK with semantic contact surfaces |
Single license file | Apache-2.0 / AGPL-3.0 layered licensing with commercial path |
Manual publishing | Automated npm + MCP Registry release pipeline |
Editorial landing page | LLM-first landing page + |
Existing tests | New diagnostics, IK, and licensing regression tests |
Architecture
┌─────────────────────────┐
│ Natural-language prompt │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ GPT-5.6 authoring layer │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ .posecode source │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ Parser and ROM checking │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ Kinematics and IK layer │
└────────────┬────────────┘
│
├─────────────────────┐
▼ ▼
┌─────────────────────────┐ ┌──────────────────────┐
│ Three.js/WebGL renderer │ │ Fidelity measurements│
└─────────────────────────┘ └──────────┬───────────┘
│
▼
┌──────────────────────┐
│ GPT-5.6 Physics │
│ Critic and revision │
└──────────────────────┘Installation and Usage
Live Playground
Preview, edit, and share movements without installing anything:
Local Development
Requirements:
Node.js 20 or newer
npm
Clone the repository:
git clone https://github.com/posecode-dev/posecode.git
cd posecodeInstall dependencies:
npm installStart the playground:
npm run devRun tests:
npm testRun type checking:
npm run typecheckRun fidelity evaluations:
npm run evalBuild the playground:
npm run buildEditor support
A VS Code extension provides syntax highlighting, ROM diagnostics, and
completion for .posecode files — see
editors/vscode. Until it is published, you can
get basic highlighting immediately by associating .posecode files with
Markdown:
"files.associations": {
"*.posecode": "markdown"
}See the editor guide for VS Code, Cursor, Sublime Text, and Neovim instructions.
MCP Server
Posecode includes a Model Context Protocol server for AI agents.
Run it with:
npx -y posecode-mcp@latestExample MCP client configuration:
{
"mcpServers": {
"posecode": {
"command": "npx",
"args": ["-y", "posecode-mcp@latest"]
}
}
}The MCP server exposes:
validate_posecoderender_posecode
See packages/posecode-mcp for the complete configuration and tool documentation.
Web Component Embed
Embed a Posecode player on a page:
<script src="https://unpkg.com/posecode-embed/dist/posecode-embed.js"></script>
<posecode-player src="/movements/squat.posecode"></posecode-player>The player can be used in:
documentation,
educational content,
exercise guides,
blog posts,
and movement libraries.
Core Libraries
Install the parser:
npm install posecode-parserInstall the renderer:
npm install posecode-renderExample:
import { parse } from "posecode-parser";
import { createViewer } from "posecode-render";
const source = `
posecode exercise "Lateral raise"
rig humanoid
pose start = standing
step "Raise" 1.4s settle:
shoulders: abduct 90
`;
const { ir, errors, warnings } = parse(source);
if (!ir || errors.length > 0) {
console.error(errors);
} else {
console.warn(warnings);
const viewer = createViewer(document.querySelector("#viewer"));
viewer.load(ir);
viewer.play();
}The #viewer element is an HTML <canvas>.
Exporting motion (BVH)
posecode-render can bake a movement into a Biovision Hierarchy
(.bvh) file for import into Blender and other animation tools. In the
playground, use the Download BVH button; programmatically:
import { parse } from "posecode-parser";
import { exportBVH } from "posecode-render";
const { ir } = parse(source);
const bvh = exportBVH(ir!, { fps: 30 }); // string, ready to write to diskOptions: fps (default 30), scale (default 1 = metres; pass 100 for
centimetres), includeFingers (default false), and proportions for a
calibrated rig.
Coordinate system: right-handed, Y-up, figure faces +Z in the rest pose (identical to the renderer and Three.js). Enable Blender's "Y up" BVH import option.
Units: metres by default.
Rotation channels:
Zrotation Xrotation Yrotation(Euler orderZXY).Scope: this exports the authored joint motion plus root travel/turn. It does not yet re-run the renderer's contact/IK solve, so IK-dependent movements (e.g.
reach: hand_left floor) export the authored pose rather than the solved one. See issue #63.
Exporting motion (glTF / GLB)
For web animation pipelines, posecode-render can export the rig and a
baked animation clip as a glTF/GLB asset. In the playground, use Download
glTF; programmatically:
import { parse } from "posecode-parser";
import { exportGLTF } from "posecode-render";
const { ir } = parse(source);
const glb = await exportGLTF(ir!); // GLB ArrayBuffer (default)
const gltf = await exportGLTF(ir!, { binary: false }); // glTF JSON objectThe result loads with Three.js GLTFLoader,
and the clip plays on the included rig:
const gltf = await new GLTFLoader().loadAsync(url);
const mixer = new THREE.AnimationMixer(gltf.scene);
mixer.clipAction(gltf.animations[0]).play();Joint nodes are named by Posecode bone id; the animated root is
posecode_root.Limitations: exports the procedural mannequin rig, not a humanoid/Mixamo skeleton, so there is no retargeting onto external rigs yet, and (as with BVH) it bakes the authored motion, not the contact/IK-solved motion. See issue #90.
How Posecode Stays Honest
Posecode uses multiple layers of checking.
1. Range-of-motion clamping
Joint angles are constrained before rendering.
For example:
knees: flex 200is clamped to the configured knee-flexion limit and produces a warning instead of rendering an impossible angle.
2. Kinematic evaluation
The engine measures the actual resulting skeleton after:
parsing,
forward kinematics,
inverse kinematics,
and ground-lock corrections.
3. Geometric fidelity invariants
Movement examples can define expected properties.
For example, a deadlift may require:
sufficient torso pitch,
limited forward knee travel,
stable foot contact,
and symmetrical hip movement.
4. GPT-5.6 Physics Critic
The Build Week critic interprets the movement and deterministic measurements together.
It explains biomechanical problems in natural language and proposes specific revisions.
Packages
Package | Purpose |
Language definitions and editor support | |
Converts | |
Renders animated figures with Three.js, forward kinematics, and IK | |
Encodes Posecode documents into URL-safe share tokens | |
Exposes Posecode capabilities to AI agents through MCP | |
Runs headless biomechanical and geometric fidelity evaluations | |
Interactive editor, 3D viewport, warnings, generation, critique, and sharing |
Technology
Posecode is built with:
TypeScript
JavaScript
Node.js
Three.js
WebGL
Vite
CodeMirror
Model Context Protocol
Zod
Vitest
Playwright
esbuild
GPT-5.6
Codex
Scope
Version 0.1
Posecode currently focuses on:
single-person human movement,
fitness,
physiotherapy demonstrations,
posture,
dance,
education,
rehabilitation visualization,
forward kinematics,
ground locking,
ROM-constrained inverse kinematics,
hip hinging,
standing, seated, and lying poses,
basic scene props,
and browser-based rendering.
Deferred
The following are outside the current scope:
two-person or partner motion,
comprehensive collision detection and rigid-body dynamics,
detailed object physics,
advanced equipment simulation,
multi-joint finger animation,
FBX or GLB animation export,
and medical diagnosis.
Limitations and Safety
Posecode is an engineering and visualization project.
Its range-of-motion values and biomechanical checks are based on general reference data and simplified models.
They are not:
medical advice,
diagnosis,
injury-prevention guarantees,
physiotherapy prescriptions,
or a substitute for a qualified professional.
Generated movements should be reviewed by a qualified expert before being used for healthcare, rehabilitation, or safety-critical applications.
Potential Applications
Posecode could support:
game and character animation,
fitness instruction,
exercise visualization,
anatomy education,
physiotherapy demonstrations,
posture training,
dance and choreography prototyping,
sports technique analysis,
robotics research,
synthetic motion-data generation,
and embodied AI systems.
Repository Structure
posecode/
├── packages/
│ ├── posecode-language/
│ ├── posecode-parser/
│ ├── posecode-render/
│ ├── posecode-share/
│ ├── posecode-mcp/
│ └── posecode-eval/
├── playground/
├── editors/
├── spec/
├── docs/
├── scripts/
└── README.mdTesting
Run all unit tests:
npm testRun coverage:
npm run coverageRun type checking:
npm run typecheckRun biomechanical evaluations:
npm run evalThe CI workflow verifies that the project:
builds successfully,
passes type checking,
passes unit tests,
and satisfies configured movement invariants.
Background
Posecode follows the design study:
Kinematic Motion Definition Protocols for Large Language Models
The project explores whether semantic, text-based movement programs can provide a controllable and inspectable alternative to black-box motion generation.
The specification covers:
DSL design,
biomechanical constraints,
client-side rendering,
agent integration,
and possible product applications.
See:
Character assets
The hosted playground currently uses an Adobe Mixamo character and one showcase animation under the applicable Adobe terms. These binary assets are not covered by Posecode's software licenses. See third-party notices.
The renderer also includes a zero-asset procedural figure and accepts compatible humanoid GLB characters through characterUrl.
Multiple character appearances (avatar avatar1 / avatar2 / avatar3)
All built-in characters use the same rig humanoid skeleton topology. An
optional avatar directive selects appearance without redefining that rig (see
spec/SPEC.md). Pass characterUrls (selector → GLB URL map)
to createViewer instead of a single characterUrl; ir.avatar is used when
present and ir.rig supplies the default selector otherwise. Switching
documents, or editing the avatar directive, swaps the visible character. A
selector with no entry in the map (or any load failure) falls back to the
procedural figure. See
packages/posecode-render/README.md
for the option, and packages/posecode-embed's character attribute docs for
the same behavior in the web component (absent by default; set an explicit URL
to pin one character regardless of avatar).
Bringing your own character rig
Pass a characterUrl (fixed) or characterUrls (per-selector, see above) pointing
to a skinned GLB to replace the bundled Mixamo character. Requirements:
Format: glTF binary (
.glb) containing aTHREE.SkinnedMesh.Rest pose: T-pose.
Bone naming: Mixamo convention. Names may carry the
mixamorig:/mixamorigN:namespace prefix — it's stripped automatically. These bones must all be present:Torso/head:
Hips,Spine,Spine2,Neck,HeadArms:
LeftArm,LeftForeArm,LeftHand,RightArm,RightForeArm,RightHandLegs:
LeftUpLeg,LeftLeg,LeftFoot,RightUpLeg,RightLeg,RightFootFingers (first phalanx only):
LeftHandThumb1,LeftHandIndex1,LeftHandMiddle1,LeftHandRing1,LeftHandPinky1, and theRightHand*1equivalents
If any required bone is missing, loading the character rejects and the viewer silently falls back to the zero-asset procedural figure — a bad rig never breaks the scene.
The simplest way to source a compatible rig is mixamo.com:
export a character in T-pose with "skin with skeleton," then convert
FBX → GLB (e.g. with Blender's glTF exporter or FBX2glTF). Bone names come
out Mixamo-compatible automatically.
The bone map and retarget/calibration logic live in
packages/posecode-render/src/character.ts.
Supporting a different naming convention (e.g. VRM humanoid bones) means
editing the BONE_MAP table and plainName() prefix-stripping there.
Licensing
Posecode is open source with a clear standard and product boundary:
Layer | Components | License |
Open standard | Specification, examples, parser, share codec, language service, LSP, VS Code extension | Apache-2.0 |
Product layer | Renderer, web embed, MCP server, eval harness, hosted playground | AGPL-3.0-only |
Organizations that need to use an AGPL component in a closed-source product may contact hello@posecode.org about a separate commercial agreement.
Earlier grants are unchanged. MIT revisions remain MIT, and the 0.2.2 npm packages remain Apache-2.0. See licensing, commercial licensing, and trademark policy.
Feedback and Support
Feedback and contributions are welcome.
Email: hello@posecode.org
Issues: GitHub Issues
Available Tools
3 toolsposecode_authoring_guideHow to write PosecodeA
Return the guide that teaches the Posecode (.posecode) language: grammar, joints, actions, and an example. Read this before writing a movement.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description covers basic behavior (returning a guide) but does not explicitly state that it's a read-only, non-destructive operation, though this is obvious from context.
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?
Two concise and front-loaded sentences with no superfluous information. Each sentence adds value: purpose and usage guidance.
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 zero parameters and no output schema, the description fully covers what the tool does and when to use it. No missing information.
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?
No parameters in schema; description correctly avoids parameter details. Baseline score of 4 for zero-parameter tool.
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 'Return the guide that teaches the Posecode language' with specific content details (grammar, joints, actions, example), and distinguishes from sibling tools (render_posecode, validate_posecode) by being about authoring.
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?
Includes 'Read this before writing a movement' which provides clear context on when to use the tool. Does not explicitly exclude alternatives but implicitly prioritizes usage before sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
render_posecodeRender a Posecode movement to a linkA
Validate a .posecode document and return a permalink that animates it as a 3D figure in the Posecode playground. Give the link to the user to view the movement.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | The full .posecode document text |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description transparently states validation and permalink generation. Missing details on potential errors or permissions, but no indication of hidden behavior.
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?
Two short sentences: first describes function, second provides user instruction. No superfluous words.
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?
Adequately covers purpose and output for a simple tool with one parameter and no output schema. Could mention error conditions or output format (URL) but not necessary.
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 100%, and parameter description in schema matches description text. The tool description adds no additional semantics beyond the schema.
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?
Clearly states it validates a .posecode document and returns a permalink for 3D animation, effectively distinguishing from sibling tools like validate_posecode which only validates.
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?
Implicitly indicates usage for creating shareable animations by telling to give link to user, but does not explicitly compare with sibling tools like validate_posecode or posecode_authoring_guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_posecodeValidate a Posecode movementA
Parse a .posecode document and return any errors plus configured range-of-motion (ROM) clamps. Use this to check the source before showing it to a user; a clean result is not a clinical safety assessment.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | The full .posecode document text |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the tool returns errors and ROM clamps, and notes the limitation regarding clinical safety. However, it does not discuss side effects, idempotency, or error handling, leaving gaps in transparency.
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 consists of two concise sentences that clearly communicate the tool's function and usage. The information is front-loaded with the core purpose, followed by usage guidance. No unnecessary words are present.
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's simplicity (one parameter, no output schema), the description provides adequate context: it explains the input, output (errors and ROM clamps), and appropriate use case. The caution about clinical safety adds important context. No additional information is needed for agent understanding.
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 already fully describes the single parameter 'source' with 100% coverage. The description does not add any additional semantic information about the parameter beyond what the schema provides. Thus, the description adds no extra value for parameter understanding.
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 that the tool parses a .posecode document and returns errors and ROM clamps. It also distinguishes itself from sibling tools by specifying its use case: to check source before showing to a user, implying it is for validation rather than authoring or rendering.
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 instructs when to use the tool: 'Use this to check the source before showing it to a user'. It also provides a critical caution that 'a clean result is not a clinical safety assessment'. However, it does not explicitly state when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v0.1.3- Added
render_posecode - Changed
validate_posecode1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
1 tool update
v0.1.2- Removed
render_posecode
3 tool updates
v0.1.1- First observed
posecode_authoring_guide - First observed
render_posecode - First observed
validate_posecode
TDQS
The guide and validator serve completely distinct purposes: one provides language documentation, the other checks syntax and constraints. No functional overlap.
Both tools use snake_case, but one follows a noun_verb_noun pattern ('posecode_authoring_guide') while the other uses verb_noun ('validate_posecode'), showing minor inconsistency.
With only 2 tools, the server feels minimal. However, for a niche DSL server solely focused on authoring and validation, it is borderline acceptable.
The server lacks tools for creating, editing, or converting Posecode documents. An agent cannot progress beyond reading the guide and validating pre-existing files.
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- FlicenseNot gradedqualityDmaintenanceA Python server that enables remote control of Bittle robots via Bluetooth using the Model Context Protocol (MCP), allowing users to programmatically send movement and pose commands.-
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