@qverisai/mcp

Official QVeris MCP Server — Dynamically search and execute tools via natural language.

Overview

This SDK provides a Model Context Protocol (MCP) server that enables LLMs to discover and execute third-party tools through the QVeris API. With a small set of tools, your AI assistant can:

• Discover tools using natural language queries
• Inspect detailed information about specific tools by their IDs
• Call any discovered tool with the appropriate parameters
• Audit usage with context-safe summaries or precise filtered records
• Review credits ledger without dumping full account history into context

Quick Start

1. Get Your API Key

Visit QVeris (Global) or QVeris (China) to get your API key.

2. Configure Your MCP Client

Use the QVeris CLI to generate config without hand-editing JSON. Placeholder output intentionally fails API key validation until you replace it or use --include-key:

# Print safe config with YOUR_QVERIS_API_KEY placeholder
qveris mcp configure --target cursor

# Write a working config using your resolved API key
qveris mcp configure --target cursor --write --include-key
qveris mcp configure --target claude-desktop --write --include-key
qveris mcp configure --target opencode --write --include-key
qveris mcp configure --target openclaw --write --include-key

# Validate config, or live-probe visible tools for stdio clients
qveris mcp validate --target cursor
qveris mcp validate --target cursor --probe

Add the QVeris server to your MCP client configuration:

Claude Desktop (claude_desktop_config.json):

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

Cursor (Settings → MCP Servers):

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

3. Start Using

Once configured, You could add this to system prompt:

"You can use qveris MCP Server to dynamically discover and call tools to help the user. First think about what kind of tools might be useful to accomplish the user's task. Then use the discover tool with a query describing the capability of the tool, not what params you want to pass to the tool later. Then call a suitable tool using the call tool, passing parameters through params_to_tool. You could reference the examples given if any for each tool. You may make multiple tool calls in a single response."

Then your AI assistant can discover and call tools:

"Find me a weather tool and get the current weather in Tokyo"

The assistant will:

1. Call discover with query "weather"
2. Optionally call inspect to review tool details
3. Call call with the tool_id and parameters
4. Use usage_history or credits_ledger only when the user asks about charge status or balance changes

Available Tools

discover

Discover available tools based on natural language queries.

Example:

{
  "query": "send email notification",
  "limit": 10
}

inspect

Inspect tools by their IDs to get detailed information (parameters, success rate, latency, examples, and billing_rule when available).

Example:

{
  "tool_ids": ["openweathermap.weather.execute.v1", "worldbank_refined.search_indicators.v1"],
  "search_id": "abcd1234-ab12-ab12-ab12-abcdef123456"
}

call

Call a discovered tool with specific parameters.

Example:

{
  "tool_id": "openweathermap.weather.execute.v1",
  "search_id": "abcd1234-ab12-ab12-ab12-abcdef123456",
  "params_to_tool": {"city": "London", "units": "metric"}
}

The call response may include compact pre-settlement billing. Final charge status should be checked with usage_history or credits_ledger.

usage_history

Context-safe request-level usage audit. Defaults to aggregated summary mode. Summary mode requests service-side summary=true aggregates when available and falls back to bounded client-side aggregation for older deployments.

Examples:

{ "mode": "summary", "bucket": "hour" }

{ "mode": "search", "execution_id": "exec-123" }

{ "mode": "search", "min_credits": 30, "max_credits": 100 }

credits_ledger

Context-safe final credit ledger query. Defaults to aggregated summary mode. Summary mode requests service-side summary=true aggregates when available and falls back to bounded client-side aggregation for older deployments.

Examples:

{ "mode": "summary", "bucket": "day" }

{ "mode": "search", "direction": "consume", "min_credits": 50 }

Large result sets should use mode: "export_file". The server writes JSONL under .qveris/exports/ and returns the file path instead of emitting every row into MCP context.

Deprecated tool names

For backward compatibility, the old tool names are still supported but emit a deprecation warning:

Session Management

Providing a consistent session_id in a same user session in any tool call enables:

• Consistent user tracking across multiple tool calls
• Better analytics and usage patterns
• Improved tool recommendations over time

If not provided, the SDK automatically generates and maintains a session ID for the lifetime of the server process. However, this result in a much larger granularity of user sessions.

Response Handling

Successful Execution

{
  "execution_id": "abcd1234-ab12-ab12-ab12-abcdef123456",
  "tool_id": "openweathermap.weather.execute.v1",
  "success": true,
  "result": {
    "data": {
      "temperature": 15.5,
      "humidity": 72,
      "description": "partly cloudy"
    }
  },
  "execution_time": 0.847
}

Large Responses

When tool output exceeds max_response_size, you'll receive:

{
  "result": {
    "message": "Result content is too long...",
    "truncated_content": "[[1678233600000, \"22198.56...",
    "full_content_file_url": "https://..."
  }
}

The full_content_file_url is valid for 120 minutes.

Environment Variables

Region

Region is auto-detected from your API key prefix — no extra configuration needed.

To override manually, set environment variables in your MCP client config:

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

Priority: QVERIS_BASE_URL > QVERIS_REGION > API key prefix auto-detection > default (global)

Requirements

• Node.js 18.0.0 or higher
• A valid QVeris API key (qveris.ai or qveris.cn)

Development

# Clone the repository
git clone https://github.com/QVerisAI/qveris-agent-toolkit.git
cd qveris-agent-toolkit/packages/mcp

# Install dependencies
npm install

# Build
npm run build

# Run locally
QVERIS_API_KEY=your-key node dist/index.js

License

MIT © QVerisAI

Support

• 🐛 Issue Tracker
• 💬 Contact: contact@qveris.ai