# Qveris Coding

> When coding a project that needs external APIs or tools, use QVeris to discover, verify, and generate production code that calls those APIs. Covers weather, stocks, search, currency, data retrieval, and thousands more.

- Skill: `qverisai/qveris-coding` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qverisai/qveris-coding`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qverisai/qveris-coding/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qverisai (https://skillmd.com/u/qverisai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/qverisai/qveris-coding

---


# QVeris — External API Code Generation

When a coding project needs to call external APIs or third-party tools (weather, stocks, search, currency, data retrieval, etc.), follow this workflow to discover a working API and generate production code.

## Step 1 — Search & Verify via MCP

Use the QVeris MCP server to find and test tools.

1. **Search**: Call the `search_tools` MCP tool with a query that describes the **capability** you need (not specific parameters).
   - Good query: `"current weather data"`, `"stock price API"`, `"currency exchange rates"`
   - Bad query: `"get weather for London"` (too specific)
2. **Test**: Call `execute_tool` with the `tool_id` from search results and pass parameters via `params_to_tool` (JSON string). Refer to examples returned by search if available.
3. You may call multiple MCP tools in a single response. Iterate between `search_tools` and `execute_tool` until you find a tool that works.

## Step 2 — Generate Production Code

Once you have confirmed a working `tool_id` from Step 1, generate code that calls the **QVeris REST API** directly. Do **not** use the MCP tool results as the final output — generate real, standalone code.

Rules for generated code:
- Use the verified `tool_id` directly (no search call needed in production code).
- Set HTTP request timeout to **5 seconds**.
- Handle error responses correctly (non-200 status, `success: false`).
- Place the `QVERIS_API_KEY` in the code (read from environment variable or config).

### QVeris REST API Reference

**Base URL**: `https://qveris.ai/api/v1`

**Authentication**: Bearer token in the `Authorization` header.

```
Authorization: Bearer YOUR_QVERIS_API_KEY
```

**Execute Tool Endpoint**:

```
POST /tools/execute?tool_id={tool_id}
```

Request body:

```json
{
  "search_id": "string",
  "session_id": "string",
  "parameters": {
    "city": "London",
    "units": "metric"
  },
  "max_response_size": 20480
}
```

Response (200 OK):

```json
{
  "execution_id": "string",
  "result": {
    "data": { ... }
  },
  "success": true,
  "error_message": null,
  "elapsed_time_ms": 847
}
```

## Fallback — CLI Script (if MCP is unavailable)

If MCP tools are not available or not working, use the CLI script instead.

**Search**:
```bash
uv run {baseDir}/../scripts/qveris_tool.py search "weather forecast API" --limit 5
```

**Execute** (to verify a tool works):
```bash
uv run {baseDir}/../scripts/qveris_tool.py execute <tool_id> --search-id <id> --params '{"city": "London", "units": "metric"}'
```

After verifying a working tool via CLI, proceed to **Step 2** above to generate production code using the REST API.