This server provides tools for React component scaffolding, review, fixing, and improvement using shadcn/ui templates.
generate_component — Scaffold a single React component (PascalCase) to a specified path, optionally including TypeScript types, Vitest tests, Storybook stories, and documentation.
list_templates — View all 41 available shadcn/ui component templates.
generate_component_library — Batch-generate multiple components at once to a base output directory, with optional tests and Storybook stories.
check_component_exists — Verify if a component already exists at a given path to avoid accidental overwrites.
review_component — Audit a component for TypeScript errors and accessibility issues, returning a quality grade (A+ to F) and actionable suggestions.
fix_component — Auto-fix common issues such as broken import paths, missing displayName, and incorrect path references.
improve_component — Enhance an existing component by expanding test coverage with edge cases, adding Storybook story variants, and enriching documentation.
Planned integration to perform Web Vitals / Lighthouse audits via CLI.
Allows scaffolding React components from 41 shadcn/ui templates.
Planned integration to auto-generate Storybook stories for existing components.
MCP servers for React + TypeScript development automation. Works with Claude Desktop, Cline, Cursor — and as plain CLI scripts — one protocol, zero duplication.
59 tools across 9 categories — component scaffolding, code quality, a full CRUD-feature factory, CRA→Vite migration, and more. Every tool ships as its own MCP server, built and tested independently.
Here's the thing nobody tells you when you start building agentic workflows: the loop itself is what's expensive, not the model. An agent working without any composed tools does everything the slow way — read a file, think, write a file, read it back to check its own work, repeat — and every single one of those turns re-sends the whole conversation so far as input tokens. By the time you're 20 steps into a real multi-file task, that resent context alone can be running 50K+ tokens per call. It adds up fast, and it's not really about which model you're using.
I didn't just take that on faith — a few sources back it up with real numbers. LeanOps measured agent loops running about 3.2× the tokens of a single direct call at 5 steps, ~30× at 50 steps, and past 100× once you're deep into a typical build-and-debug session — because re-sent context is roughly 62% of the bill. Vantage found similar: real agentic sessions run an input-to-output ratio around 25:1 (a direct call is closer to 1:1), with a 50-turn session routinely hitting a million input tokens, and non-agentic usage on comparable work costing something like 200× less per interaction on the same team. And a recent arXiv paper on agentic tokenomics puts agentic tasks at roughly 1000× the tokens of single-turn work, with up to 30× variance run to run on the exact same task — so it's not just expensive, it's unpredictable.
That's the problem this toolkit's composed tools are built to get rid of. workflow-runner's schema_to_feature and cra-to-vite don't add one more tool call into an agent's existing loop — they replace what would otherwise be 7 or 8 separate read/write/verify turns with a single in-process call that runs the whole generator or migration pipeline and hands back the finished result. That's the "50-turn loop collapses into 1 call" shape the research above says saves 10–100×, which is a very different thing from just bolting one extra tool onto an unchanged loop (that only gets you the 20–40% range).
To keep myself honest, I also ran a real, measured benchmark rather than just trusting the theory — ax-benchmark, 6 tasks, claude -p running headless, three arms (agent alone, agent with one MCP tool call added into its loop, and the tool called directly with no agent at all). This is a conservative baseline on purpose, since it only tests adding a single tool call into an otherwise unchanged loop, not the deeper pipeline collapse described above:
Agent alone | Agent + one MCP tool | Tool called directly | |
Analysis tasks (review, a11y, legacy-code) | baseline | ~41% lower cost | ~100% free, ~15× faster (when in scope) |
All 6 tasks, blended | baseline | ~19% lower cost | — |
New code (component, tests) | baseline | roughly cost-neutral | not applicable to novel work |
Two things worth being upfront about: cost is the fair metric here, not wall-time — the agent-alone arm ran headless with no shell access and over-explored on open-ended tasks, which inflated its time without touching its actual cost. And on small, novel, single-file work, the overhead of the tool's structured output can offset what it saves — the real win shows up on repetitive, mechanical, multi-file work, which also happens to be exactly where the multi-turn-loop tax above hits hardest.
Related MCP server: MCP Quality Hub
Published on npm as mcp-react-toolkit. No clone or build required:
npx mcp-react-toolkit --list # list all 59 tools
npx mcp-react-toolkit legacy-analyzer # run one as an MCP server (stdio)Add it to Claude Desktop / Cursor / Cline:
// claude_desktop_config.json
{
"mcpServers": {
"legacy-analyzer": { "command": "npx", "args": ["-y", "mcp-react-toolkit", "legacy-analyzer"] }
}
}Swap in any tool name from npx mcp-react-toolkit --list. Restart your client and the tool appears.
Most MCP tools return raw JSON. These return that JSON plus an interactive HTML dashboard — health score, sortable issue triage, light/dark toggle, and one-click fix actions that call other tools in the toolkit. One self-contained artifact, no server, no external requests:
Where you run it | What you get |
Claude Desktop (MCP Apps) | Renders inline in the conversation (sandboxed iframe) |
Claude Code · Cursor · CLI | JSON plus a clickable |
Any browser | The same HTML, standalone |
Analysis tools (legacy-analyzer, component-reviewer, dep-auditor, etc.) get an audit view — grade, category cards, filterable issue table. Generators (component-factory, code-modernizer, etc.) get a result view — files created/changed, diffs, follow-ups. Powered by the internal @mcp-showcase/ui-kit package: dependency-free, ~30 KB per report.
Tool | What it does |
| Scaffold React components from 41 shadcn/ui templates — with tests + Storybook |
| Audit TypeScript errors, a11y issues, test coverage — graded A+ to F |
| Auto-fix broken imports, missing deps, inline style refactors |
| Extend a component with variants, comprehensive stories, and edge-case tests |
| Auto-generate Storybook stories — Default, variants, sizes, callbacks, play functions |
Tool | What it does |
| Scan for |
| WCAG 2.1 audit — alt text, label associations, ARIA roles, keyboard navigation |
| Apply safe a11y fixes (img alt, blank rel, htmlFor, tabIndex) |
| 5-stage audit (tests · types · perf · a11y · design tokens), graded A–F |
| Static A–F quality gate for generated/changed code |
| Flag hardcoded colors/spacing/radii/shadows, suggest tokens, grade A–F |
| Detect unnecessary re-renders, missing memo, inline objects/functions |
| Memory leaks, heavy imports, unoptimized images, deep nesting |
| Gate gzipped asset sizes against per-pattern budgets — fail CI on regressions |
| AST-based JS/JSX → TypeScript conversion, PropTypes → interfaces |
| Flag redundant useMemo/useCallback/memo for the React 19 Compiler |
| Generic regex codemod engine + named built-ins; dry-run by default |
| Execute refactor plans safely — move/rename/split, update imports, rollback |
| Audit Redux for anti-patterns (selectors, mutations, RTK Query migration hints) |
| Diff two API snapshots → breaking vs additive changes — CI gate against breaks |
| Scan JSX for hardcoded strings → i18n keys + message catalog |
| Analyze a TS/React source file and generate a Vitest test suite |
| Find unimplemented functions, uncovered branches, missing edge cases |
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| Run the suite, classify failures by root cause, generate targeted fixes |
| 22-tool health audit for any React/Next.js/Remix app — scores 0–100, migration hints |
Tool | What it does |
| Unused deps, duplicate versions, circular imports, bundle impact analysis |
| Workspace listing, dependency graph, health check, shared dep finder |
| Static HTML audit — meta tags, a11y, OG/Twitter cards, canonical, JSON-LD |
| Interactive HTML JSON viewer — collapsible, searchable, dark/light |
One JSON API sample (or OpenAPI schema) fans out into a full, typed CRUD feature. Every generator keys off the shared FieldSchema contract, so the pieces compose.
Tool | What it does |
| JSON sample / OpenAPI → typed |
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| Wire the pieces into routes — React Router 7 or Next App Router |
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| Runs the whole chain end-to-end, gated by |
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| Scaffold the Playwright harness — config, fixtures, base POM, auth setup |
| Playwright |
Migrate a Create React App project to Vite: analyze → plan → scaffold → migrate → verify.
Tool | What it does |
| Orchestrator — runs the six tools below in sequence, one call, graded report |
| Deep CRA config inspection (react-scripts, env, proxy, jest, browserslist, SVG) |
| CRA deps → Vite plan (remove/add with versions + unmapped) |
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| Best-effort webpack/CRACO → Vite translation + manual-review list |
Tool | What it does |
| Generate an |
| JSON sample → plain TS interfaces (nested objects become their own interfaces) |
| State shape → typed Zustand store (setters, reset, persist/devtools) |
| Raw SVG → typed React component (SVGProps, currentColor) — SVGR-grade |
| Zod-validated typed env module (Vite/Next) — fail fast on missing/bad vars |
| Loading/empty/error state components + a switch wrapper for a data view |
Tool | What it does |
| Scaffold + wire + verify new MCP tools in this package |
| Analyze + improve MCP tools across 7 dimensions — proposed diffs, apply, rollback |
| Generate a README (from an MCP tool) or an API reference (from a TS module + JSDoc) |
Chain tools together for common tasks:
Workflow | Chain |
Code Modernization |
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Component Quality Pipeline |
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Render Performance Audit |
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App Health Check |
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Dependency Health |
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Full CRUD feature |
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CRA → Vite migration |
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Claude Desktop / Cline / Cursor
│ JSON-RPC over stdio
▼
MCP Server (e.g. typescript-enforcer)
│
▼
Tool handlers (your code)Each server extends McpServerBase from tools/shared/ — handles transport, routing, and error formatting. Adding a new tool is ~50 lines:
import { McpServerBase } from '@mcp-showcase/shared';
class MyTool extends McpServerBase {
constructor() {
super({ name: 'my-tool', version: '1.0.0' });
}
protected registerTools(): void {
this.addTool('do_thing', 'Does a thing', {
type: 'object',
properties: { path: { type: 'string', description: 'Target path' } },
required: ['path'],
}, async (args) => {
const { path } = args as { path: string };
return this.success({ result: `Processed ${path}` });
});
}
}
new MyTool().run();Prefer npm for everyday use (see Install). Clone only to hack on the tools or run the showcase UI:
git clone https://github.com/Nishant-Chaudhary5338/mcp-toolkit.git
cd mcp-toolkit
npm install
npm run build
npm test # run the full suite across all tools
npm run dev # server on :3002, client on :5173Point Claude Desktop at a local build the same way as Install, swapping npx -y mcp-react-toolkit <name> for node /path/to/mcp-toolkit/tools/<name>/build/index.js.
Every tool's build/index.js has a #!/usr/bin/env node shebang — pipe a JSON-RPC message to it on stdin:
# Health score + migration hints for a full app
echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"analyze-legacy-app","arguments":{"path":"/path/to/app"}}}' \
| node tools/legacy-analyzer/build/index.js
# Review a component — grade A+ to F
echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"review","arguments":{"path":"src/components/Button.tsx"}}}' \
| node tools/component-reviewer/build/index.js
# List a tool's available commands
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node tools/legacy-analyzer/build/index.jsEvery tool has a co-located Vitest suite covering its core logic directly — no MCP transport required.
npm test # all tools
npm run test -w tools/legacy-analyzer # single toolCI runs on every push and PR against Node 20 and 22.
code-graph-indexer — indexes any TS/React/Next.js repo into a queryable code graph (files, components, functions, and the edges between them) and answers structural questions: who calls this, blast radius, dead code, semantic search. Same family, separate package.
npx code-graph-indexer ui --root .See CONTRIBUTING.md — how to scaffold a new tool, write tests, and open a PR.
TypeScript strict · Node.js · MCP SDK 1.12 · Vitest · React 19 · Vite · Tailwind CSS · Express
Nishant Chaudhary — Senior Frontend Engineer · nishantchaudhary.dev@gmail.com
Also see: dashcraft · react-present · ai-builder
MIT License
Check if a component already exists at the specified path
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Component name | |
| outputPath | Yes | Output directory path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states read operation but does not disclose return format or edge cases (e.g., case sensitivity, partial matches). Basic transparency but incomplete.
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?
Single sentence, no filler, front-loaded. Every word adds value.
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?
Simple tool with 2 parameters and no output schema. Description hints at boolean response but does not specify return type or status codes. Adequate but leaves ambiguity for an agent.
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 description adds no additional meaning beyond the schema. Baseline of 3 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?
Description uses specific verb 'Check' and resource 'component existence at specified path', clearly distinguishing from sibling tools like generate, fix, improve, review, list.
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 on when to use this tool versus alternatives. Sibling tools are listed but no explicit when-to-use or when-not-to-use criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Auto-fix common issues in a component: broken import paths, missing displayName, @/lib/utils → relative path
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the component directory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It lists the auto-fixes (import paths, displayName, path conversion), but does not disclose what happens on success/failure, whether changes are reversible, or if the file is modified in-place. The transparency is moderate.
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 lists the specific issues it fixes. No redundant words, and the purpose is front-loaded. 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 one parameter, no annotations, and no output schema, the description covers the what but not the outcome or error handling. For a tool that modifies files, details on success/failure signals or confirmation are missing, making it somewhat incomplete for an AI agent.
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 for the single 'path' parameter is 100% with a schema description 'Path to the component directory'. The tool description adds no additional context about the parameter (e.g., format, relative vs absolute path, or constraints). Baseline 3 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 'Auto-fix common issues in a component' and enumerates specific issues (broken import paths, missing displayName, path conversion). This distinguishes the tool from siblings like review_component (review) or generate_component (creation).
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?
While the description implies usage when a component has these specific issues, it lacks explicit guidance on when to use versus alternatives (e.g., improve_component) or when not to use it. No exclusion criteria or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a React component using actual shadcn/ui source code with TypeScript types, tests, and Storybook stories
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Component name in PascalCase (e.g., Button, Card, Input) | |
| outputPath | Yes | Output directory path | |
| includeDocs | No | Generate documentation file | |
| includeTests | No | Generate Vitest test file | |
| includeTypes | No | Generate separate types file | |
| includeStories | No | Generate Storybook stories |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'using actual shadcn/ui source code' but lacks details on side effects (e.g., overwriting existing files), permissions required, or error conditions. Without annotations, it carries a moderate burden but only partially meets it.
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, direct sentence with no extraneous words. It efficiently conveys the core functionality.
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 generation tool with 6 parameters and no output schema, the description omits critical context: expected output structure, overwrite behavior, prerequisites (e.g., project setup), and how 'outputPath' is used. This could lead to incorrect invocations.
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 schema already documents all parameters. The description adds value by referencing 'shadcn/ui source code' but does not elaborate on parameter meanings beyond what the schema provides.
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 generates a React component with specific deliverables (TypeScript types, tests, Storybook stories) using shadcn/ui source code, effectively distinguishing it from siblings like fix_component or check_component_exists.
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 on when to use this tool versus alternatives (e.g., generate_component_library, improve_component). The description implies use for new component generation but does not exclude cases where other tools might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate multiple components at once using shadcn/ui templates
| Name | Required | Description | Default |
|---|---|---|---|
| components | Yes | List of component names in PascalCase | |
| outputPath | Yes | Base output directory path | |
| includeTests | No | ||
| includeStories | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits (e.g., overwrite behavior, auth requirements, side effects). It only mentions the template source, leaving agents uninformed about important operational details.
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 short sentence, which is concise but lacks structure. It could be slightly expanded to include key details without becoming verbose.
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?
No output schema is provided, and the description omits any mention of return values or side effects. For a tool with four parameters and required fields, this leaves gaps in 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?
Schema description coverage is 50%, yet the description adds no extra parameter meaning. It does not clarify 'includeTests' or 'includeStories', nor does it elaborate on the required parameters 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?
The description clearly states the action 'generate multiple components at once' using a specific template system, effectively distinguishing it from sibling tools like the singular 'generate_component'.
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 'generate_component' or when not to use it. The description simply states the action without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Improve a component by expanding tests with edge cases, adding more Storybook story variants, and enhancing docs
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the component directory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the intended actions (expand tests, add stories, enhance docs) but omits behavioral details such as whether files are modified, if the operation is destructive, or any side effects. The description is adequate but lacks depth.
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, well-structured sentence that efficiently communicates the tool's purpose without redundancy or extraneous 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?
The description adequately covers the tool's actions but lacks information about output, behavior on failure, or prerequisites (e.g., component must exist). Given the tool's simplicity (1 param, no output schema), the description is minimally complete but could be improved.
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 'path', with a clear description. The tool description adds no additional semantics beyond what the schema already provides, achieving the baseline expected when schema coverage is high.
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 'improve' and resource 'component', listing specific actions: expanding tests with edge cases, adding Storybook story variants, and enhancing docs. These concrete details differentiate it from siblings like 'fix_component' or 'review_component'.
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 when to use this tool (to improve a component with tests, stories, and docs) but provides no explicit guidance on when not to use it or how it differs from alternatives like 'fix_component'. Usage context is implied but not clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available shadcn/ui component templates
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of disclosing behavior. It accurately describes a read-only listing operation. No further behavioral details are needed due to the tool's simplicity.
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, clear, and straightforward sentence. No wasted words, perfectly sized for the tool's simplicity.
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, no output schema, and a simple listing task, the description is mostly complete. It could mention the return format (e.g., list of names), but the current text suffices.
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 no parameters, so the baseline score is 4. The description does not need to add parameter information, and the schema coverage is 100%.
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 action ('list') and the resource ('all available shadcn/ui component templates'). It distinguishes itself from siblings like 'check_component_exists' and 'generate_component' which have different purposes.
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 on when to use this tool versus siblings is provided. While the name and description imply it's for listing all templates, it does not 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.
Review a generated component for TypeScript errors and accessibility issues. Returns a quality score and suggestions.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Path to the component directory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the behavioral burden. It discloses what the tool checks (TypeScript errors, accessibility) and what it returns (score, suggestions), but lacks details on side effects, auth requirements, or performance implications. The description does not contradict annotations (none provided).
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 with zero fluff, efficiently conveying the primary action, scope, and output.
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 single-parameter tool with no output schema, the description covers the main purpose and return value. However, it could explicitly mention that the component must exist (implied by 'generated component') or prerequisites. Overall, it is sufficiently 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?
The schema has 100% coverage for the single parameter 'path' with a clear description. The tool description adds little extra meaning beyond the schema, so baseline 3 applies.
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 reviews a generated component for TypeScript errors and accessibility, returning a quality score and suggestions. It uses a specific verb ('review') and resource ('generated component'), and the mention of quality score and suggestions distinguishes it from sibling tools like check_component_exists or generate_component.
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 after generation but offers no explicit guidance on when to use or avoid this tool versus alternatives. No references to sibling tools are provided, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a distinct purpose: checking existence, fixing, generating single/library, improving, listing templates, and reviewing. No overlaps or ambiguity.
All tool names follow a consistent verb_noun pattern using snake_case, e.g., check_component_exists, fix_component, generate_component.
7 tools is well-scoped for a component generation toolkit, covering the essential operations without bloat.
The tool set covers the full lifecycle: listing templates, generating, checking existence, fixing, improving, and reviewing. Gaps like deletion are not needed for this domain.
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