ts-agent-sdk▌
jezweb/claude-skills · updated Apr 8, 2026
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Generate typed TypeScript SDKs for AI agents to call MCP server tools with clean function signatures.
- ›Converts MCP tool definitions (Zod schemas, descriptions, endpoints) into typed TypeScript interfaces, client methods, and example scripts
- ›Scans project for MCP servers in src/server/modules/mcp*/server.ts , extracts tool metadata, and generates module-based client classes with one async method per tool
- ›Includes built-in error handling (AuthError, ValidationError, RateLimitError, MCP
ts-agent-sdk
Overview
This skill generates typed TypeScript SDKs that allow AI agents (primarily Claude Code) to interact with web applications via MCP servers. It replaces verbose JSON-RPC curl commands with clean function calls.
Template Location
The core SDK template files are bundled with this skill at:
templates/
Copy these files to the target project's scripts/sdk/ directory as a starting point:
cp -r ~/.claude/skills/ts-agent-sdk/templates/* ./scripts/sdk/
SDK Generation Workflow
Step 1: Detect MCP Servers
Scan the project for MCP server modules:
src/server/modules/mcp*/server.ts
Each server.ts file contains tool definitions using the pattern:
server.tool(
'tool_name',
'Tool description',
zodInputSchema,
async (params) => { ... }
)
Step 2: Extract Tool Definitions
For each tool, extract:
- name: The tool identifier (e.g., 'create_document')
- description: Tool description for JSDoc
- inputSchema: Zod schema defining input parameters
- endpoint: The MCP endpoint path (e.g., '/api/mcp-docs/message')
Step 3: Generate TypeScript Interfaces
Convert Zod schemas to TypeScript interfaces:
// From: z.object({ name: z.string(), email: z.string().email() })
// To:
export interface CreateEnquiryInput {
name: string;
email: string;
}
Step 4: Generate Module Client
Create a client class with methods for each tool:
// scripts/sdk/docs/client.ts
import { MCPClient, defaultClient } from '../client';
import type { CreateDocumentInput, CreateDocumentOutput } from './types';
const ENDPOINT = '/api/mcp-docs/message';
export class DocsClient {
private mcp: MCPClient;
constructor(client?: MCPClient) {
this.mcp = client || defaultClient;
}
async createDocument(input: CreateDocumentInput): Promise<CreateDocumentOutput> {
return this.mcp.callTool(ENDPOINT, 'create_document', input);
}
async listDocuments(input: ListDocumentsInput): Promise<ListDocumentsOutput> {
return this.mcp.callTool(ENDPOINT, 'list_documents', input);
}
// ... one method per tool
}
export const docs = new DocsClient();
Step 5: Generate Example Scripts
Create runnable examples in scripts/sdk/examples/:
#!/usr/bin/env npx tsx
// scripts/sdk/examples/create-doc.ts
import { docs } from '../';
async function main() {
const result = await docs.createDocument({
spaceId: 'wiki',
title: 'Getting Started',
content: '# Welcome\n\nThis is the intro.',
});
console.log(`Created document: ${result.document.id}`);
}
main().catch(console.error);
Step 6: Update Index Exports
Add module exports to scripts/sdk/index.ts:
export { docs } from './docs';
export { enquiries } from './enquiries';
Output Structure
project/
└── scripts/sdk/
├── index.ts # Main exports
├── config.ts # Environment config
├── errors.ts # Error classes
├── client.ts # MCP client
│
├── docs/ # Generated module
│ ├── types.ts # TypeScript interfaces
│ ├── client.ts # Typed methods
│ └── index.ts # Module exports
│
├── enquiries/ # Another module
│ ├── types.ts
│ ├── client.ts
│ └── index.ts
│
└── examples/ # Runnable scripts
├── create-doc.ts
├── list-spaces.ts
└── create-enquiry.ts
Environment Variables
The SDK uses these environment variables:
| Variable | Description | Default |
|---|---|---|
SDK_MODE |
Execution mode: 'local', 'remote', 'auto' | 'auto' |
SDK_BASE_URL |
Target Worker URL | http://localhost:8787 |
SDK_API_TOKEN |
Bearer token for auth | (none) |
Execution
Run generated scripts with:
SDK_API_TOKEN="your-token" SDK_BASE_URL="https://app.workers.dev" npx tsx scripts/sdk/examples/create-doc.ts
Naming Conventions
- Module names: Lowercase, from MCP server name (e.g., 'mcp-docs' → 'docs')
- Method names: camelCase from tool name (e.g., 'create_document' → 'createDocument')
- Type names: PascalCase (e.g., 'CreateDocumentInput', 'CreateDocumentOutput')
Error Handling
The SDK provides typed errors:
AuthError- 401, invalid tokenValidationError- Invalid inputNotFoundError- Resource not foundRateLimitError- 429, too many requestsMCPError- MCP protocol errorsNetworkError- Connection failures
Regeneration
When MCP tools change, regenerate the SDK:
- Re-scan
src/server/modules/mcp*/server.ts - Update types.ts with new/changed schemas
- Update client.ts with new/changed methods
- Preserve any custom code in examples/
How to use ts-agent-sdk on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ts-agent-sdk
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ts-agent-sdk from GitHub repository jezweb/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ts-agent-sdk. Access the skill through slash commands (e.g., /ts-agent-sdk) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★45 reviews- ★★★★★Mateo Menon· Dec 28, 2024
ts-agent-sdk reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Dec 24, 2024
ts-agent-sdk is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Amina Smith· Dec 16, 2024
Registry listing for ts-agent-sdk matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amina Tandon· Dec 8, 2024
Solid pick for teams standardizing on skills: ts-agent-sdk is focused, and the summary matches what you get after install.
- ★★★★★Xiao Zhang· Nov 19, 2024
ts-agent-sdk has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 15, 2024
Keeps context tight: ts-agent-sdk is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Xiao Tandon· Nov 15, 2024
We added ts-agent-sdk from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★James Sharma· Nov 7, 2024
ts-agent-sdk fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Nikhil Kim· Oct 26, 2024
ts-agent-sdk is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Daniel Perez· Oct 10, 2024
Useful defaults in ts-agent-sdk — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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