text2animate
Click on "Install 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., "@text2animateanimate a goose waddling through grass, playful style"
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.
text2animate
Turn plain-language descriptions into animated SVGs — and refine them in a live chat, right from your editor.
An MCP server that lets Claude (or any MCP client) compose animated SVGs from text, preview them in a clean floating UI, and iterate on them conversationally.

Why
LLMs are good at writing SVG, but "make me an animation" usually produces something stiff and templated. text2animate fixes that two ways:
It bakes in motion-design best practices — layered scenes, natural easing, anticipation/overshoot, seamless loops, restrained palettes — and hands them to the model before it draws anything.
It closes the feedback loop. The result renders instantly in a local preview, and a chat box under the animation lets you say "make the goose have webbed feet and add parallax mountains" — the change applies live, through your own Claude Code session.
No API key, no separate billing — edits run on the model you're already using.
Related MCP server: PinePaper MCP Server
How it works
create_animation
┌──────────┐ ──────────────────► ┌──────────────────┐ ──► Preview UI
│ Claude │ │ text2animate │ (floating,
│ (MCP host)│ ◄────────────────── │ MCP server │ ◄── live over SSE)
└──────────┘ await_edit_request └──────────────────┘
▲ (long-poll for your │ embeds best practices
│ typed changes) │ validates + stores SVG
└─ apply_patch / apply_edit ───────────┘ serves the local UIThe model is the renderer's brain. Guided by the embedded best practices, it writes
a self-contained <svg> with @keyframes/SMIL animation. The server validates it,
stores it, and serves a preview. When you type a change, the server queues it and the
in-session agent picks it up, edits the SVG, and the preview refreshes.
Install
Option A — Claude Code plugin (recommended)
No clone, no build. In Claude Code:
/plugin marketplace add tom-pettit/text2animate
/plugin install text2animate@tom-pettitThat registers the MCP server (it runs a prebuilt, dependency-free bundle), so just ask:
"Use text2animate to animate a goose waddling through the grass, playful style."
The preview opens automatically at http://127.0.0.1:4321. The CLI equivalent:
claude plugin marketplace add tom-pettit/text2animate
claude plugin install text2animate@tom-pettitOption B — from source (for development)
git clone https://github.com/tom-pettit/text2animate.git
cd text2animate
npm installnpm install builds the server and auto-registers it in the surrounding
workspace's .mcp.json (merging — it never clobbers other servers). Restart Claude
Code to pick it up.
Registering somewhere specific, or not at all:
npm run register -- ~/.mcp.json # a specific config file
npm run unregister # remove the entry
T2A_NO_AUTO_REGISTER=1 npm install # skip auto-registrationResolution order: explicit path → T2A_MCP_TARGET env → <repo-parent>/.mcp.json.
Styling the look
Tell it the aesthetic and the server feeds concrete art direction (palette, shape language, motion feel, typography) to the model. Use a preset or any freeform phrase:
"Animate a loading spinner, minimalistic but modern."
Built-in presets: minimal-modern, playful, neon-cyber, flat-corporate,
hand-drawn, retro-80s. The chosen style is recorded and shown in the preview's
properties panel.
Live editing (the chat box)
Under the animation is a ChatGPT-style box. Type a change and it round-trips through your Claude Code session:
The box queues your request on the server.
The agent's watch loop (
await_edit_request) is long-polling — it unblocks with your text and the current SVG.The agent applies the change; the preview updates live.
Fast iteration. Small tweaks (color, size, timing, text, easing) go through
apply_patch — the agent sends a tiny find/replace instead of re-emitting the whole
document, so a recolor is ~10 tokens rather than a full regeneration. Structural
changes fall back to apply_edit. A bad patch is rejected wholesale (nothing
changes) so the request stays open to retry.
To turn it on, tell Claude once: "watch the preview for edits" — it keeps calling
await_edit_request. Pairs naturally with /loop, and /fast makes each turn
snappier. The box shows a green dot when a watcher is connected, amber when it
isn't.
Preview UI
A light, floating interface (no chrome docked to the edges):
Artboard — the animation in a 16:9 frame, centered on a warm canvas.
Animations panel (left) — everything created this session.
Properties panel (right) — style, duration, loop, and Export SVG.
Playback (bottom-right) — play/pause (
Space) and restart (R); pausing freezes both CSS@keyframesand SMIL timelines.Chat dock (bottom) — the live-edit box plus the watcher status.
Tools, resources & prompts
Tool | Purpose |
| The animation guidelines. Optional |
| List the built-in style presets. |
| Render a self-contained animated |
| List animations created this session. |
| Long-poll for a change typed into the preview's chat box. |
| Fast edit: apply small find/replace tweaks in place. |
| Apply a fully regenerated SVG in place. |
| Ensure the preview server is running and open it. |
Also exposed: an animation-best-practices resource and an animate prompt.
Configuration
Variable | Default | Meaning |
|
| Preferred preview port (walks upward if taken). |
|
| Where |
| – | Set to skip auto-registration on |
Development
npm run build # compile TypeScript → dist/
npm run dev # tsc --watch
npm run typecheck # tsc --noEmit
npm test # node:test suite (via tsx)
npm run serve # run just the preview UI (no MCP stdio channel)Stack: TypeScript + @modelcontextprotocol/sdk (stdio) for the MCP server, Express +
Server-Sent Events for the preview. Animations are self-contained SVGs — no runtime
dependencies in the browser.
src/
├─ index.ts # entry: stdio transport + --web-only mode
├─ config.ts # ports, timeouts, paths
├─ core/ # framework-free domain logic (unit-tested)
│ ├─ svg.ts # validate + patch helpers (pure)
│ ├─ store.ts # in-memory animation store
│ └─ edits.ts # edit-request queue + long-poll
├─ content/ # styles.ts, best-practices.ts (the guidance)
├─ web/server.ts # Express preview server + SSE
└─ mcp/server.ts # MCP tools, resources & prompts
public/ # the preview UI (vanilla HTML/CSS/JS)
test/ # node:test specs for core/
bundle/index.mjs # prebuilt single-file server for the plugin (generated)
.claude-plugin/ # plugin.json + marketplace.jsonThe plugin ships a prebuilt bundle (npm run bundle, via esbuild) so it runs on
node alone with no install step. CI fails if the committed bundle is stale, so run
npm run bundle and commit it whenever src/ changes.
License
MIT
Available Tools
8 toolsapply_editApply an edit to an animationB
Apply a regenerated SVG to the animation referenced by an edit request (from await_edit_request). Updates the animation in place — the preview UI refreshes live. Pass the complete new .
| Name | Required | Description | Default |
|---|---|---|---|
| svg | Yes | The complete, self-contained updated <svg>...</svg> with the change applied. | |
| requestId | Yes | The id from the await_edit_request result. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool updates the animation in place with live preview, indicating a mutation. However, no annotations are provided, so the description carries the full burden. It does not detail side effects, permissions, or error conditions.
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 sentences efficiently convey the tool's purpose and a key parameter hint. No extraneous information, though the first sentence could be slightly more structured.
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 the tool's function and parameter meaning given the absence of an output schema and annotations. Missing details about return values or failure modes, but the workflow context is clear via sibling tool references.
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 100%, so the baseline is 3. The description reinforces that 'svg' must be the complete updated SVG, but does not add substantial meaning 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 applies a regenerated SVG to an animation edit request. The verb 'apply' and resource 'edit request' are specific. However, it does not explicitly distinguish from sibling tool apply_patch, which might handle partial updates.
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?
Implies usage after await_edit_request by mentioning 'from await_edit_request'. Provides context for when to use but no explicit when-not or alternative guidance. Lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apply_patchPatch an animation (fast edit)A
Apply small find/replace edits to an animation's SVG in place — the fast path for tweaks like color, size, position, timing, text, or easing. Prefer this over apply_edit: it avoids resending the whole document. Each find must match the current SVG exactly (verbatim substring). If a find isn't found, the patch is rejected wholesale (nothing changes) — fix the find or fall back to apply_edit. The preview refreshes live.
| Name | Required | Description | Default |
|---|---|---|---|
| edits | Yes | Ordered find/replace operations applied to the current SVG. | |
| requestId | Yes | The id from the await_edit_request result. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral traits: it states edits are applied in place, the patch is rejected wholesale if any find is missing, and the preview refreshes live. It discloses atomicity and failure mode clearly.
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 concise and front-loaded: purpose first, then usage guidance, then constraints. Every sentence adds value with no redundancy. It fits in a short paragraph.
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 no output schema, the description could be more explicit about return values or response format. However, it covers the essential behavior (live preview, rejection) and constraints well. Slightly incomplete on result details.
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%, so baseline is 3. The description adds critical context beyond schema: 'Each find must match the current SVG exactly (verbatim substring)' and explains rejection behavior. This adds meaning to parameter usage.
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 the tool applies small find/replace edits to an animation's SVG in place, lists examples (color, size, etc.), and explicitly distinguishes itself from the sibling 'apply_edit' as a faster alternative.
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 advises 'Prefer this over apply_edit' and provides clear fallback behavior: if a find isn't found the patch is rejected, with instructions to fix the find or fall back to apply_edit. This gives excellent when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
await_edit_requestWait for an edit request from the preview UIA
Long-poll for the next change the user typed into the preview UI's chat box. This is how live edits work: call it, and it blocks until a request arrives (or ~25s passes). When it returns an edit, apply the change FAST: for small tweaks (color, size, position, timing, text, easing) call apply_patch with minimal find/replace edits — do NOT regenerate the whole SVG. Use apply_edit (full SVG) only for structural changes. Then call await_edit_request again to keep watching. When it returns 'idle', simply call it again. Keep this loop running while the user iterates.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. Discloses blocking behavior, timeout, return types ('idle' or edit), and required follow-up actions. Explains the continuous polling loop.
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?
Description is relatively long but well-structured: starts with purpose, then loop guidance, then tool choice. Each sentence adds necessary information. Could be slightly tighter but appropriate for complexity.
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 no parameters, output schema, or annotations, the description fully covers behavior, timing, return types, and integration with sibling tools. No gaps for effective agent usage.
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 has 0 parameters with 100% coverage, so baseline is 4. No parameter details needed. Description adds context about the tool's function without parameters.
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 defines the tool as a long-poll for user changes from the preview UI chat box. Uses specific verb ('wait', 'long-poll') and resource ('edit request'). Distinguishes from siblings like apply_patch and apply_edit.
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?
Provides explicit when-to-use and when-not-to-use instructions: use apply_patch for small tweaks, apply_edit for structural changes. Describes the iterative loop pattern and handling of 'idle' and timeout (~25s).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_animationCreate & preview an animationA
Render a self-contained animated SVG in the local preview UI and open it in the browser. First compose the scene as a layered SVG with an explicit viewBox, then animate it with CSS @keyframes (or SMIL) embedded in the SVG, using natural easing. Call get_best_practices (optionally with a style) first if you need the full guidance. Pass the same style you designed for so it is recorded and shown. The svg argument must be a complete ... document that animates on its own with no external assets or scripts.
| Name | Required | Description | Default |
|---|---|---|---|
| svg | Yes | A complete, self-contained animated <svg>...</svg> document with an explicit viewBox and embedded CSS @keyframes/SMIL animations. No external fonts, images, or scripts. | |
| loop | No | Whether the animation loops. | |
| open | No | Open (or focus) the preview UI in the default browser. | |
| style | No | The visual style this animation was designed in (preset key or freeform phrase), e.g. "minimal-modern". | |
| title | Yes | Short human-readable title for the animation. | |
| prompt | Yes | The original human-readable request this animation depicts. | |
| durationSeconds | No | Approximate length of one cycle of the animation, in seconds. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes rendering and opening in browser, and emphasizes that the SVG must be self-contained. However, it lacks details on side effects like whether it replaces previous previews or opens a new tab, and does not mention error handling or state changes.
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 concise with 5 sentences, each adding value: purpose, composition guidance, preliminary step recommendation, style recording note, and SVG requirement. No wasted 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?
Given complexity (7 params, no output schema), description explains input requirements and workflow well. However, it does not mention what the tool returns (e.g., success status, animation ID), which is a gap since no output schema is provided.
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%, so baseline is 3. The description adds meaning beyond schema by specifying that 'svg' must be a complete document with viewBox and embedded animations, and that 'style' should match the designed style for recording. This adds value for key parameters.
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 the tool renders an animated SVG in a local preview UI and opens it in the browser. It distinguishes from siblings by mentioning get_best_practices as a preliminary step, implying this is for creation rather than editing.
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?
It recommends calling get_best_practices first for full guidance, which provides context for when to use this tool. It does not explicitly state when not to use it, but the context against siblings like apply_edit is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_best_practicesGet animation best practicesA
Return the guidelines for composing and animating SVGs. Read this before generating an animation. Pass a style (a preset key like "minimal-modern" or a freeform phrase like "minimalistic but modern") to also get concrete art direction for that look.
| Name | Required | Description | Default |
|---|---|---|---|
| style | No | Optional visual style: a preset key (see list_styles) or any freeform phrase. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It honestly describes a read operation (returning guidelines) with optional style input, but does not disclose return format or any potential side effects. Adequate but could add more detail.
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 sentences, front-loaded with purpose, no redundant information. Every word 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?
Given no output schema and no annotations, the description adequately covers purpose and optional style usage, but does not specify the return format (e.g., text, JSON) or response size. Moderately complete.
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% for the single parameter. The description adds value by explaining that 'style' can be a preset key or freeform phrase and that it yields concrete art direction, going beyond the schema description.
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 guidelines for composing and animating SVGs, and specifies it should be read before generating an animation. It differentiates from sibling tools like create_animation and list_styles.
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 'Read this before generating an animation,' providing clear when-to-use guidance. It also explains how the optional style parameter adds art direction, though it lacks explicit when-not-to-use or sibling comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_animationsList animationsB
List animations created this session, newest first.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states the result ordering and session scope. With no annotations, it fails to disclose behavioral traits such as whether the list is read-only, if authentication is required, or if the output includes metadata. The agent lacks crucial safety and usage 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?
The description is a single sentence that is concise and front-loaded with the key action and result. 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?
While the tool is simple with no parameters, the description omits the return value structure. Since no output schema exists, the description should explain what fields each animation object contains. It also does not define 'session,' leaving ambiguity.
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 in the input schema, so the baseline is 4. The description adds no parameter information because none exists, which is appropriate.
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 the verb 'list', the resource 'animations', the scope 'created this session', and the ordering 'newest first'. It fully distinguishes from sibling tools such as create_animation or list_styles.
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 guidance is provided on when to use this tool versus alternatives like get_best_practices or apply_edit. The description does not mention excluded scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_stylesList style presetsA
List the built-in animation style presets. You can also pass any freeform style phrase to create_animation/get_best_practices.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. The verb 'list' implies a read-only operation with no side effects, but the description does not explicitly state that it is non-destructive or safe. It is adequate but not explicit.
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 sentences: the first states the main purpose, the second provides usage guidance with alternatives. No filler or redundant information. Exceptionally concise and well-structured.
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 no parameters and no output schema, the description covers the essential purpose and usage. It could be slightly more complete by hinting at the return format (e.g., list of preset names/IDs), but the current text is sufficient for a simple list tool.
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 tool has no parameters, so the description does not need to add parameter information. The mention of freeform phrases for other tools is helpful context but not relevant to this tool's parameters. Baseline for 0 params is 4.
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 lists built-in animation style presets. It also mentions alternatives for freeform style phrases, but does not explicitly differentiate from sibling tools that also deal with styles or animations. The purpose is specific and clear.
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 tells when to use this tool (to list built-in presets) and when to use alternatives (create_animation/get_best_practices for freeform phrases). This provides clear guidance on selecting the correct tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
open_previewOpen the preview UIA
Ensure the preview web server is running and open it in the browser.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool may start a web server and opens the browser, which is good, but does not mention potential blocking behavior, error handling, or whether it is safe to invoke multiple times.
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, front-loaded sentence with no wasted words. Every word adds value: the verb, the resource, and the prerequisite condition.
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 zero parameters, no output schema, and simple action, the description is complete. It tells the agent to ensure the server is running and then open it in the browser, covering the main behavior.
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 has zero parameters and schema coverage is 100%, so the description does not need to add parameter meaning. It appropriately describes the action without needing param details.
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 verb 'open' and the resource 'preview UI', and adds the prerequisite of ensuring the web server is running. This clearly distinguishes it from sibling tools that handle editing, patching, or listing styles.
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 usage for previewing UI output but does not explicitly state when to use it over alternatives like apply_edit or list_animations. It lacks guidance on exclusions or context.
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.
8 tool updates
v0.1.0- First observed
apply_edit - First observed
apply_patch - First observed
await_edit_request - First observed
create_animation - First observed
get_best_practices - First observed
list_animations - First observed
list_styles - First observed
open_preview
TDQS
Each tool targets a distinct action: applying full SVGs, patching, polling user edits, creating animations, fetching guidelines, listing animations/styles, and opening preview. No overlap in purpose.
All tool names follow a consistent verb_noun pattern with underscores (e.g., apply_edit, list_animations, open_preview), making them predictable and easy to understand.
8 tools cover the full workflow of creating, editing, listing, and previewing animations without excess. The scope is well-defined and each tool earns its place.
Core CRUD operations are covered except for explicit deletion or retrieval of a single animation. The interactive edit loop is robust, but missing a delete tool is a minor gap.
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Rayzia MCPofficial
AlicenseNot gradedqualityCmaintenanceEnables AI agents to drive a live SVG/vector editor, allowing a full observe-and-act loop on a canvas with real tools, state reading, and PNG rendering.MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/tom-pettit/text2animate'
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