QVeris Agent Toolkit

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• A broad catalog of capabilities across multiple categories
• 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 — a command-line interface for agents to discover and call tools. Learn more
• Works with CLI / MCP / Python SDK / REST API

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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

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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 the right capability with natural language (free)
• Inspect — optionally check missing or stale parameters, quality signals, and billing details
• Call — invoke any capability, get structured results and pre-settlement billing back
• Usage audit / Credits ledger — check whether calls were charged and how balance changed without dumping full history into Agent context
• QVeris CLI — call tools via qveris discover/inspect/call subprocess, no upfront catalog schemas
• Capabilities across finance, search, weather, maps, docs, social, blockchain, healthcare, and more
• Per-tool execution history, success-rate, and latency signals when available

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:

After connecting to QVeris, I can discover the best capability for free, then call it on demand — weather, stock prices, news, docs — one API Key for everything. Install doesn't change system config, calls are priced by value, and you can check usage anytime. Just give me an API Key.

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

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30-Second Setup

1. 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:

1. Open the plugin configuration and enter your QVERIS_API_KEY.
2. Start a new agent session so Cursor can connect to https://mcp.qveris.ai/mcp.
3. 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)
• All capability calls run in sandbox
• You can review call logs and credit usage anytime

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QVeris CLI

Discover and call API tools from your agent's shell.

CLI executes as a subprocess and discovers capabilities on demand, without preloading the full catalog. Instructions, commands, and results still consume context tokens. QVeris MCP also uses a small set of routing tools instead of exposing every catalog entry.

# 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

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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.

Core protocol

Agents interact with QVeris through three actions:

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.

Capability ecosystem

• A broad capability catalog across multiple categories
• Each capability includes parameter schema, examples, success rate, avg latency
• Supports private / org / public visibility levels
• Browse all: qveris.ai/providers

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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

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Security & Trust

• All capability calls execute in sandbox
• Session-scoped config, no system file modifications by default
• Full audit trail with execution IDs
• RBAC and per-capability access control
• Rate limiting and quota enforcement
• Enterprise options (VPC / private cloud) planned

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What's New

Latest capabilities and updates: qveris.ai

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Open Ecosystem

QVeris's core routing engine runs as a managed service. We actively support the open-source ecosystem by open-sourcing all client-side tooling — MCP server, SDKs, Agent skills, and plugins — and contributing code to upstream projects.

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

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Community

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

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⭐ Star & Share

If you believe agents need a better way to discover and use capabilities, star this repo and share it with other builders.