QVeris 文档

npm install -g @qverisai/cli@0.13.2
qveris discover "weather" --public-read-only --limit 1 --json --base-url https://qveris.ai/api/v1

npm install -g @qverisai/cli@0.13.2
qveris auth login
qveris discover "天气预报 API" --json

qveris auth login
qveris discover "天气预报 API" --json

qveris call --context @context.json --params @params.json

{
  "mcpServers": {
    "qveris": {
      "type": "http",
      "url": "https://mcp.qveris.ai/mcp"
    }
  }
}

{
  "mcpServers": {
    "qveris": {
      "command": "npx",
      "args": ["-y", "@qverisai/mcp"],
      "env": {
        "QVERIS_API_KEY": "your-api-key-here"
      }
    }
  }
}

pip install qveris

import asyncio
from qveris import PublicDiscoveryClient, QverisConfig
async def main():
    async with PublicDiscoveryClient(QverisConfig(base_url="https://qveris.ai/api/v1")) as client:
        print(await client.discover("weather", limit=1))
asyncio.run(main())

npm install @qverisai/sdk

import { Qveris } from "@qverisai/sdk";

const qveris = Qveris.fromEnv({ baseUrl: "https://qveris.ai/api/v1" });
const discovered = await qveris.discover("天气预报 API", { limit: 5 });
console.log(discovered.search_id, discovered.results.map(tool => tool.tool_id));

Authorization: Bearer YOUR_API_KEY

curl -sS -X POST "https://qveris.ai/api/v1/search" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"query\":\"天气预报 API\",\"limit\":10}"

import os
import requests

API_KEY = os.environ["QVERIS_API_KEY"]

resp = requests.post(
    "https://qveris.ai/api/v1/search",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    },
    json={"query": "天气预报 API", "limit": 10},
    timeout=30,
)
resp.raise_for_status()
data = resp.json()
print(data["search_id"])
print(data["results"][0]["tool_id"] if data.get("results") else None)

const apiKey = process.env.QVERIS_API_KEY!;

const resp = await fetch("https://qveris.ai/api/v1/search", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ query: "天气预报 API", limit: 10 }),
});

if (!resp.ok) throw new Error(`HTTP ${resp.status}: ${await resp.text()}`);
const data = await resp.json();
console.log(data.search_id);
console.log(data.results?.[0]?.tool_id);

curl -sS -X POST "https://qveris.ai/api/v1/tools/by-ids" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"tool_ids\":[\"openweathermap.weather.execute.v1\"],\"search_id\":\"YOUR_SEARCH_ID\"}"

resp = requests.post(
    "https://qveris.ai/api/v1/tools/by-ids",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    },
    json={
        "tool_ids": ["openweathermap.weather.execute.v1"],
        "search_id": "YOUR_SEARCH_ID",
    },
    timeout=30,
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://qveris.ai/api/v1/tools/by-ids", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    tool_ids: ["openweathermap.weather.execute.v1"],
    search_id: "YOUR_SEARCH_ID",
  }),
});

if (!resp.ok) throw new Error(`HTTP ${resp.status}: ${await resp.text()}`);
const data = await resp.json();
console.log(data.results);

curl -sS -X POST "https://qveris.ai/api/v1/tools/execute?tool_id=openweathermap.weather.execute.v1" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"search_id\":\"YOUR_SEARCH_ID\",\"parameters\":{\"city\":\"London\",\"units\":\"metric\"},\"max_response_size\":20480}"

import os
import requests

API_KEY = os.environ["QVERIS_API_KEY"]

tool_id = "openweathermap.weather.execute.v1"  # 来自发现结果
search_id = "YOUR_SEARCH_ID"  # 来自 /search 响应

resp = requests.post(
    f"https://qveris.ai/api/v1/tools/execute?tool_id={tool_id}",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    },
    json={
        "search_id": search_id,
        "parameters": {"city": "北京", "units": "metric"},
        "max_response_size": 20480,
    },
    timeout=60,
)
resp.raise_for_status()
print(resp.json())

const apiKey = process.env.QVERIS_API_KEY!;

const toolId = "openweathermap.weather.execute.v1"; // 来自发现结果
const searchId = "YOUR_SEARCH_ID"; // 来自 /search 响应

const resp = await fetch(
  `https://qveris.ai/api/v1/tools/execute?tool_id=${encodeURIComponent(toolId)}`,
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${apiKey}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      search_id: searchId,
      parameters: { city: "London", units: "metric" },
      max_response_size: 20480,
    }),
  }
);

if (!resp.ok) throw new Error(`HTTP ${resp.status}: ${await resp.text()}`);
const data = await resp.json();
console.log(data);

你是一个有用的助手，可以动态发现并调用各种能力来帮助用户。首先思考完成用户任务可能需要哪类能力。然后使用 discover 工具，以描述能力的查询词进行搜索，而非直接写出你稍后要传入的具体参数。再使用 call 工具调用合适的能力，通过 params_to_tool 传入参数。如果能力具有 success_rate 和 avg_execution_time，请在选择时加以参考。你可以参考每个能力提供的示例。你可以在一次响应中发起多个工具调用。