QVeris Agent Toolkit

Clientübergreifendes MCP-Toolkit zum Entdecken, Inspizieren, Vorprüfen und Aufrufen externer Fähigkeiten mit Nutzungs- und Guthabenabrechnungsprüfungen.

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QVeris Agent Toolkit

Bring professional data to the AI you already use.

QVeris is a professional data and tool access platform for AI. This open-source toolkit provides the CLI, MCP server, Python SDK, skills, and REST API docs that help AI assistants, products, and workflows find services, review supported scope, call them, and audit usage.

Website: https://qveris.ai

🇺🇸 English · 🇨🇳 简体中文

Professional data and tools across finance, search, weather, maps, documents, and more
Discover / Call is the default path; Inspect and Probe are conditional checks for missing/stale contracts, comparison, parameter validation, or a current quote
For provider comparison, Inspect every candidate when current scope or a complete contract must be confirmed; a Discover summary is not confirmation. Probe every candidate when the comparison requires a current quote. Reuse may preserve an exact route, never business parameters or results: build parameters from the current request, and make a fresh Call for current, latest, today, or other time-sensitive data.
Discover is free. Call returns structured pre-settlement billing, with final charge outcomes available through usage audit and credits ledger views.
NEW QVeris CLI — command-line access to professional data and tools for agents. Learn more
Works with CLI / MCP / Python SDK / REST API

When to use QVeris: use it when built-in tools are insufficient, a provider must be found dynamically, comparison or fallback matters, or the user requests it. Keep using local or native tools when they already fit the task.

Quick Start

🤖 I'm an Agent

Your owner sent you here to learn about QVeris? → If you are an OpenClaw-style skill agent, use the official skill: skills/openclaw/qveris-official/SKILL.md → If you are running in Cursor / Claude Code / OpenCode, follow the Agent capability guide & install protocol → Default path: Discover (free) → Call; use Inspect or Probe only when additional verification is needed

🧑‍💻 I have OpenClaw / Cursor / Claude Code installed

Copy this to your Agent — done in 30 seconds: Install QVeris skill following https://qveris.ai/skill/instruct.md and use QVERIS_API_KEY=your-key → Get API Key (free, 1,000 one-time trial credits after signup verification)

⚙️ I'm a developer integrating via CLI / MCP / API / SDK

→ QVeris CLI — npm install -g @qverisai/cli or curl -fsSL https://qveris.ai/cli/install | bash → MCP Server Doc → REST API Doc → Python SDK

For Agents

If you are an AI Agent evaluating whether to use QVeris, here is what you need to know.

What QVeris gives you:

Discover — find candidate services with natural language (free)
Inspect — optionally check missing or stale parameters, available signals, and billing details
Call — invoke a selected service and receive structured results and pre-settlement billing when available
Usage audit / Credits ledger — review request and credit outcomes through the interfaces that expose them
QVeris CLI — access services via qveris discover/inspect/call without preloading the full service catalog
Professional data and tools across finance, search, weather, maps, documents, social, blockchain, healthcare, and more
Per-tool execution history, success-rate, and latency signals when supplied

How to install:

CLI (recommended): curl -fsSL https://qveris.ai/cli/install | bash — then use qveris discover/inspect/call
See SETUP.md — supports OpenClaw / Claude Code / Cursor / OpenCode / Trae
Install does not modify shell startup files by default (session-scoped)
Must pass file checks + CLI verification to confirm success

How to explain to your owner:

QVeris helps me find a service when the tools I already have are insufficient, review its supported scope when needed, and call it. Installation does not change system configuration unless explicitly requested. Call pricing and usage or credit records depend on the selected capability and supported interface.

Billing and audit:

Discover: free
Call: priced by structured billing_rule; call responses may include compact pre-settlement billing
Final charge status: check qveris usage / usage_history
Final balance movement: check qveris ledger / credits_ledger
Free tier: 1,000 one-time trial credits after signup verification
$19 = 10,000 credits (pay-as-you-go, credits never expire)
Details: qveris.ai/pricing

30-Second Setup

Get API Key (free, 1,000 one-time trial credits after signup verification)

OpenClaw users

Send this to your Agent:

Install QVeris skill following https://qveris.ai/skill/instruct.md and use QVERIS_API_KEY=your-key

The Agent will download the official OpenClaw skill and complete installation automatically.

Cursor / Claude Code / OpenCode users

Follow the setup guide (agent/SETUP.md) — your Agent will configure MCP server + skill for your environment.

Cursor Marketplace plugin

The Cursor plugin in this repository bundles the hosted QVeris MCP connection and the official QVeris skills. After installing it from Cursor Marketplace:

Open the plugin configuration and enter your QVERIS_API_KEY.
Start a new agent session so Cursor can connect to https://mcp.qveris.ai/mcp.
Confirm that discover, inspect, probe, call, usage_history, and credits_ledger are available.

The API key is stored by Cursor as a plugin variable and is never committed to this repository.

Gemini CLI extension

Install QVeris directly from this repository:

gemini extensions install https://github.com/QVerisAI/qveris-agent-toolkit

During installation, enter your QVERIS_API_KEY when prompted. Gemini CLI stores it as a sensitive extension setting and connects to the hosted QVeris MCP server at https://mcp.qveris.ai/mcp.

Restart Gemini CLI after installation, then run /mcp to confirm that the qveris server is connected and its tools are available.

After setup

Try a task: "Check the current weather in Tokyo"

Safety:

Install does not modify your shell config (unless you explicitly ask)
Capability calls are remote API requests and do not grant a called service access to your local filesystem or system configuration
You can review usage and credit records through supported interfaces

QVeris CLI

Access professional data and tools from your agent's shell.

CLI executes as a subprocess and finds services on demand, without preloading the full service catalog. Instructions, commands, and results still consume context tokens. QVeris MCP likewise exposes a compact tool surface instead of every service definition.

# Install (one-liner)
curl -fsSL https://qveris.ai/cli/install | bash

# Or via npm
npm install -g @qverisai/cli

# Guided first call: `init` handles selection and validation for you
$ qveris init

# Default agent workflow: discover → call
$ qveris discover "weather forecast API"
Found 5 capabilities matching your query
1. gridpoint_forecast  by Weather.gov
   params: wfo (string, required), x (number, required), y (number, required)

$ qveris call 1 --params '{"wfo":"LWX","x":90,"y":90}'
✓ success
{ "forecast": "Sunny, high near 75..." }

# Optional when selection or request construction needs missing details
$ qveris inspect 1

# Optional when parameters need validation or a budget decision needs a current quote
$ qveris probe 1 --params '{"wfo":"LWX","x":90,"y":90}' --checks schema,quote

$ qveris usage --mode search --execution-id <execution_id>
# Confirms charge_outcome and actual_amount_credits for that call

Why CLI over MCP for agents?

	CLI	MCP
Token cost	No upfront catalog schemas; command/result tokens still apply	Depends on client context handling and exposed routing schemas
Startup	Instant (`npx` or global install)	Requires server process + transport handshake
Output	Deterministic schema, `--json` for parsing	JSON over stdio, varies by client
Scalability	On-demand discovery, no catalog preloading	Routing schemas stay separate from catalog size
Debugging	Visible in terminal, `--dry-run` preview	Opaque, buried in MCP logs
Auth	Built-in endpoint; explicit override via `QVERIS_BASE_URL`	Same

Usage and ledger commands default to aggregated summaries. Large audit exports are written to local JSONL files under .qveris/exports/ instead of being printed into Agent context.

When to use CLI: Agent frameworks that support exec / bash tool (Claude Code, OpenClaw, Cursor terminal, etc.) When to use MCP: IDE integrations that only support MCP protocol (Cursor inline, Claude Desktop)

Full CLI documentation: packages/cli/README.md

Developer Integration

Access methods

Method	Use case	Docs
CLI (recommended)	Claude Code / OpenClaw / any agent with exec	CLI docs
MCP Server	Cursor / Claude Desktop / MCP-only clients	MCP docs
Python SDK	Python projects, agent frameworks	Python SDK docs
TypeScript SDK	Node.js / TypeScript projects	JS SDK docs
REST API	Any language, custom integrations	REST API docs

Stuck? See Troubleshooting & FAQ.

Service access workflow

Agents use three service-access actions, plus two read-only record paths:

Action	API endpoint	Description
Discover	`POST /search`	Find capabilities with natural language, returns candidates
Inspect	`POST /tools/by-ids`	View capability details, parameters, quality signals
Call	`POST /tools/execute`	Invoke a capability, get structured results, and optionally record model attribution
Usage audit	`GET /auth/usage/history/v2`	Check request status, charge outcome, and actual charge
Credits ledger	`GET /auth/credits/ledger`	Check final credit balance movements

The reproducible Discover → Call accuracy benchmark measures grounded selection, parameterization, and real execution success per model. Its task set, runner, raw-record format, and deterministic scorer are public under benchmarks/discover-call.

Professional data and tool access

A service catalog across multiple professional-data and tool categories
Parameter schemas, examples, success rate, and average latency are available when supplied for a service
Supports private / org / public service visibility levels
Browse available services: qveris.ai/providers

Pricing

QVeris uses pay-as-you-go pricing. No subscriptions.

Plan	Price	Credits	Notes
Free	$0	1,000 trial credits	One-time grant after signup verification
Standard	$19	10,000 credits	Buy on demand, never expire
Scale	See pricing page	Based on selected package	Current packages and bonuses shown at checkout

Discover is free — Agents can explore all capabilities at zero cost
Call is priced by structured billing rules, with final charges auditable through usage history and the credits ledger
No monthly fees, no auto-renewal
Details: qveris.ai/pricing

Security & Trust

Capability calls are remote API requests and do not grant a called service access to your local filesystem or system configuration
Session-scoped config, no system file modifications by default
Usage and credit records through supported interfaces
RBAC and per-capability access control
Rate limiting and quota enforcement
Enterprise options (VPC / private cloud) planned

What's New

Latest capabilities and updates: qveris.ai

Open Ecosystem

QVeris provides managed service access alongside open-source client tooling. We actively support the ecosystem by open-sourcing the MCP server, SDKs, Agent skills, and plugins, and by contributing code upstream.

Monorepo packages

This repository (QVerisAI/qveris-agent-toolkit) is the primary monorepo for QVeris client-side tooling:

Package	Path	Distribution
MCP Server	`packages/mcp`	@qverisai/mcp
CLI	`packages/cli`	@qverisai/cli
Python SDK	`packages/python-sdk`	qveris
TypeScript SDK	`packages/js-sdk`	@qverisai/sdk
Agent docs	`agent/`	—
Skills	`skills/`	—

Recipes and ecosystem manifests

Use recipes/ for copy-paste workflow templates across finance research, risk/compliance, crypto monitoring, data analysis, and developer automation. The flagship recipes ship a runnable run.sh, and each package has runnable examples: js-sdk, MCP, CLI, and Python SDK.

Use ecosystem/ for the versioned QVeris manifest schema, marketplace-ready listing fields, permission declarations, contribution guide, and compatibility matrix.

Other repositories

Repository	Description
QVerisAI/open-qveris-skills	Community skills (stock copilot, market reports, etc.)
QVerisAI/openclaw-qveris-plugin	OpenClaw plugin
QVerisAI/vscode-qveris-ai	VS Code extension

Full list: github.com/orgs/QVerisAI/repositories

Distribution channels

npm: @qverisai — MCP server, CLI
PyPI: qveris — Python SDK
ClawHub: clawhub.ai/skills?q=qveris — OpenClaw skills
One-liner install: curl -fsSL https://qveris.ai/cli/install | bash

Upstream contributions

QVeris actively contributes to the broader Agent ecosystem:

openclaw/openclaw — OpenClaw runtime
openclaw/clawhub — ClawHub skill registry

Community

🐦 X (Twitter): x.com/QVerisAI
💼 LinkedIn: linkedin.com/company/qveris
📚 Docs: qveris.ai/docs
🧪 Playground: qveris.ai/playground

⭐ Star & Share

If you are building AI products or workflows that need professional data and tools, star this repo and share it with other builders.