OpenRouter
Model routing for compatibility and fallback checks.
CAPABILITY ROUTING
Capability Routing for AI Agents
Discover, inspect, and test external capabilities through QVeris, with explicit inputs, permissions, costs, and traceable outputs.
On this page
Platform
Built for production AI agent workflows
Infrastructure designed from day one to connect agents with verified real-world capabilities.
10,000+
Verified capabilities across 15+ categories
Unified
One protocol for every tool, API, and data source
Multi-Interface
REST API, Python SDK, MCP Server, and CLI
Production
Before production, check current service terms and test availability, latency, access controls, log redaction and fallback. This page does not promise a fixed SLA or latency target.
What is QVeris
A capability routing layer for AI agents
Modern AI agents need to call external tools to do real work — market data, KYC checks, document parsing, weather APIs, and thousands more. But each tool comes with its own API, auth method, rate limits, error handling, and billing. Integrating a handful is manageable. Integrating dozens is a maintenance tax that slows teams down.
QVeris solves this with a discover-first architecture. Your agent describes the capability it needs in natural language, and QVeris finds the best match from 10,000+ verified tools across multiple verified providers. One protocol. One API key. Reduce per-provider integration work.
It is not an LLM. It is not an agent framework. It is the routing layer that connects agents to the real world — discover, inspect, and call, all in one unified flow.
QVeris
Router
Router
Agent
APIs
Data
Tools
How It Works
Discover. Inspect. Call.
Three steps. Discovery and inspection are always free. You only spend credits when your agent makes a call.
1
Discover
Your agent submits a natural-language query — "today's S&P 500 movers," "KYC check on this entity," or "extract tables from this PDF." QVeris searches across 10,000+ capabilities and returns a ranked list of matches, each tagged with estimated cost, average execution time, historical success rate, and provider name. No hardcoded endpoints. No pre-registration.
2
Inspect
Before committing, your agent inspects any candidate capability to see its full parameter schema, usage examples, billing rules, and live quality metrics. Compare multiple capabilities side by side and choose the best one for the task. Zero-cost, zero-risk.
3
Call
Your agent calls the selected capability with structured parameters. QVeris executes in a sandboxed environment and returns structured JSON — result payload, unique execution ID, billing details, and remaining credit balance. Every call is traced from agent to execution.
Capabilities
15+ categories, 10,000+ verified capabilities
From finance-related capabilities to general developer tools, sourced from a growing provider network.
Finance Data
Real-time and historical market data across equities, FX, commodities, and indices. Built for quantitative workflows and trading agents.
Investment Research
Earnings data, analyst consensus, valuation models, and fundamentals. Give your research agent a complete data foundation.
Risk & Compliance
KYC verification, sanctions screening, and regulatory data. Built for fintech agents needing compliance-aware capabilities.
Crypto & On-Chain Data
Blockchain data, DeFi metrics, stablecoin flows, and wallet intelligence. Supports crypto and traditional market workflows.
Alternative Signals
News sentiment, social media analytics, event-driven indicators, and macroeconomic surprise data. Go beyond standard datasets.
Developer Tools
PDF parsing, OCR, image generation, weather, geolocation, and more. General-purpose capabilities for any agent workflow.
Developers
Integrate your way — no framework lock-in
QVeris meets your agent where it runs. REST, Python, MCP, or CLI — pick the interface that fits your stack.
REST API
Standard HTTP interface at api.qveris.ai/v1. Three
endpoints: discover, inspect, call. Works from any language, any
runtime.
Python SDK
Typed client library with automatic retry, session management, and async support. Install in one command and start discovering.
MCP Server
Native Model Context Protocol support. If your agent speaks MCP — Claude Code, Cursor, and others — QVeris exposes 10,000+ tools through an agent tool-discovery workflow.
CLI
Terminal-native workflow for scripting, CI/CD, and local development. Discover, inspect, and call without leaving the command line.
Production-Ready
Built for production agent workflows
QVeris is designed for teams running agents in production — not just prototyping. Every capability call is executed in a sandboxed environment with structured output, full parameter validation, and consistent JSON responses your agent can parse reliably.
Auditability: Every call produces a unique execution ID linked to a search session and an agent session. Full trace from intent to result. Built for debugging, compliance, and cost analysis.
Observability: Monitor success rates, execution latency, and credit consumption per capability, per session, and per agent. Make data-driven decisions about which capabilities your agents depend on.
Before production, check current service terms and test availability, latency, access controls, log redaction and fallback. This page does not promise a fixed SLA or latency target.
Use Cases
What teams are building with QVeris
From financial services to agentic SaaS, QVeris powers the tool-calling layer behind production AI agents.
Finance AI Agents
Give trading, research, and advisory agents on-demand access to market data, fundamentals, and alternative signals through one API. No per-vendor integration.
Investment Research Automation
Automate earnings analysis, peer comparison, and macro research. Agents discover and call the right data sources dynamically as research questions evolve.
Compliance Workflows
Embed KYC, sanctions screening, and regulatory checks into agent workflows. Full audit trail for every call. Built for regulated environments.
Market & Crypto Intelligence
Power dashboards and monitoring agents with real-time on-chain data, DeFi metrics, sentiment signals, and cross-asset market intelligence.
Agentic SaaS Products
Build AI-native SaaS products where agents dynamically discover and call capabilities at runtime. Ship faster by outsourcing the tool-integration layer to QVeris.
Research & Analysis Agents
General-purpose research agents that pull data from diverse sources — financial, legal, technical, and beyond — through one unified discover-inspect-call flow.
Compare
How QVeris compares
QVeris sits at a different layer of the stack. Here is how it relates to other approaches.
| Capability | QVeris | Manual API Integration | Generic API Marketplace | Simple Tool Directory |
|---|---|---|---|---|
| Tool discovery | Natural language, dynamic | Must know each API in advance | Keyword search, human-driven | Browse by category, manual |
| Cost & quality preview | Cost, latency, success rate before calling | No preview — call and see | Pricing page, not programmatic | Not available |
| Unified protocol | One API for all capabilities | N different APIs, auth, schemas | Aggregated, but limited depth | No protocol — links only |
| Provider transparency | Full provider visibility per capability | Full visibility | Often opaque | Listed but unverified |
| Agent-native design | Built for agents from day one | Built for human developers | Built for human developers | Built for human browsing |
| Audit trail | execution_id, search_id, session_id | Varies by provider | Limited to billing logs | None |
| MCP support | Native MCP server | None | None | None |
FAQ
Frequently asked questions
What is a capability routing network?
A capability routing network is an infrastructure layer that sits
between AI agents and external tools, APIs, and data sources.
Instead of hardcoding each integration, the agent describes what
it needs in natural language, and the network discovers, inspects,
and routes the call to the best-matching capability. QVeris is the
first capability routing network built specifically for AI agents,
with 10,000+ verified capabilities across 15+ categories.
How does QVeris connect my AI agent to external APIs?
QVeris provides a unified REST API, Python SDK, MCP server, and
CLI. Your agent sends a natural-language query to the
/discover endpoint. QVeris returns ranked capability
matches with cost, latency, and success rate. Your agent then
inspects and calls the chosen capability — all through one
protocol. No per-API integration code required.
How is QVeris different from LLM function calling?
LLM function calling lets a model invoke tools you have
pre-registered — but you still need to code and maintain every
integration yourself. QVeris maintains 10,000+ pre-verified
integrations across multiple verified providers, and lets your
agent discover them dynamically at runtime. Function calling
handles the LLM-to-tool handoff; QVeris handles the
tool-to-real-world routing.
How does QVeris work with MCP servers?
QVeris ships a native MCP server. If your agent or coding
environment supports the Model Context Protocol — including Claude
Code, Cursor, and other MCP-compatible platforms — QVeris appears
as an MCP server exposing 10,000+ discoverable tools. You
configure the endpoint once and start discovering capabilities
immediately.
How much does QVeris cost?
Discover and Inspect are free. Receive 1,000 one-time signup credits after verification; buy 10,000 credits for $19. Credits never expire, with no monthly fees or auto-renewal. Check the selected tool before a live call.
What kind of capabilities does QVeris provide?
QVeris covers 15+ categories: quantitative trading data, macro and
fixed income, risk and compliance (KYC, sanctions screening),
investment research, crypto and digital assets, alternative
signals (news sentiment, social data), PDF parsing, OCR, image
generation, weather, geolocation, and more — 10,000+ individual
capabilities sourced from a growing provider network.
Is QVeris suitable for production use?
Before production, check current service terms and test availability, latency, access controls, log redaction and fallback. This page does not promise a fixed SLA or latency target.
How do I start using QVeris with my AI agent?
Read the official integration docs and confirm the current SDK package and version; check the existing example command
pip install qveris before installation, or use the REST API. Discover and inspect first, then check permissions and cost; Playground execution requirements follow the current app.Give your agent the tools it needs
Discover and Inspect are free. Receive 1,000 one-time signup credits after verification; buy 10,000 credits for $19. Credits never expire, with no monthly fees or auto-renewal. Check the selected tool before a live call.