Capability Discovery for AI Agents: Developer GuideAI Agent 能力发现开发指南

What Is Capability Discovery for AI Agents?

Capability discovery is the ability of an AI application to find, understand, evaluate, and select external tools according to the current task. A capability can be an API endpoint, data query, document parser, compliance check, market-data feed, or controlled action. Discovery should expose enough metadata for the application to decide whether the capability is relevant and safe before execution.

Traditional integration starts with developers selecting providers and hard-coding a finite tool list. The model can choose among those tools, but it cannot access anything outside that list until an engineer deploys new code. Dynamic AI agent tool discovery reverses the sequence: the application starts with an intent, searches a catalog at runtime, inspects matching tools, and exposes only the best candidates to the model or routing policy.

This distinction becomes important as agents handle less predictable tasks. A fixed investment assistant might contain quote, filing, and news functions. A general finance agent may receive a sanctions-screening request, an options-volatility question, a macroeconomic comparison, or a cryptocurrency liquidity task. Loading every possible schema into the model wastes context and increases tool-selection errors. Runtime discovery keeps the active tool set small.

MCP tool discovery is related but not identical. MCP lets a client connect to servers and list the tools those servers expose. That standardization is valuable, but an application connected to many servers still needs search, ranking, deduplication, cost policy, permissions, and routing across the combined catalog. Capability discovery operates above individual connections.

The Problem Without AI Agent Capability Discovery

Without a discovery layer, developers become the search engine, integration team, and runtime router. They read provider documentation, compare endpoints, build adapters, normalize errors, and decide which tools every agent may see.

• Manual research: engineers must inspect API documentation, coverage, pricing, and licensing before each integration.
• Hard-coded catalogs: tool definitions live in prompts or source code and become stale when providers change.
• Context overload: exposing hundreds of tool schemas consumes tokens and makes similar functions difficult for the model to distinguish.
• Limited autonomy: an agent cannot extend to a new task even when a suitable capability already exists elsewhere.
• Repeated maintenance: authentication, rate limits, retries, pagination, and response normalization are rebuilt across products.
• Weak governance: static lists rarely encode dynamic user permissions, cost ceilings, data regions, or commercial rights.

Finance magnifies every issue. Market data, fundamentals, filings, analyst estimates, compliance, macroeconomics, and crypto often come from different providers. The same ticker can use different exchange suffixes, fiscal periods, adjustment rules, and units. Data coverage and entitlement can change by country or account.

A static integration can still be the correct choice for a small, high-volume workload that needs one contract and maximum control. Discovery matters when the task set and provider universe are diverse enough that choosing and maintaining tools becomes a bottleneck.

How QVeris Capability Discovery Works

QVeris organizes runtime access into Discover, Inspect, and Call. The current asynchronous Python SDK exposes these operations through QverisClient, along with usage and ledger methods for auditing execution and credits.

import asyncio

from qveris import QverisClient



async def find_quote_tools(client):

    discovered = await client.discover(

        "real-time stock price for US equities "

        "with exchange and timestamp",

        limit=8,

    )

    for tool in discovered.results:

        print(tool.tool_id, tool.name, tool.description)

    return discovered

async def inspect_candidates(client, discovered):

    ids = [tool.tool_id for tool in discovered.results[:3]]

    inspected = await client.inspect(

        ids,

        search_id=discovered.search_id,

    )

    for tool in inspected.results:

        print("ID:", tool.tool_id)

        print("PARAMS:", [p.model_dump() for p in tool.params])

        print("STATS:", tool.stats)

        print("BILLING:", tool.billing_rule)

    return inspected.results

async def call_quote(client, tool, search_id, ticker):

    params = {}

    if tool.examples and tool.examples.sample_parameters:

        params.update(tool.examples.sample_parameters)



    # Confirm the exact field name in tool.params.

    params["ticker"] = ticker



    response = await client.call(

        tool.tool_id,

        params,

        search_id=search_id,

        max_response_size=20_480,

    )

    if not response.success:

        raise RuntimeError(response.error_message)

    return response.result

Discover and Inspect are permanently free under the current QVeris model. Only Call consumes credits. The platform currently advertises 1,000 signup credits and 100 daily login credits; confirm current execution prices on the pricing page.

The workflow reduces context usage as well as integration work. The model or application sees a handful of relevant, inspected tools instead of thousands of raw definitions. Search IDs and execution IDs also make tool selection and billing easier to audit.

Why Capability Discovery Matters for Finance AI Agents

A financial request often spans several data domains. “Explain today’s price move” may require a real-time quote, intraday volume, a new filing, earnings context, company news, sector performance, and a macroeconomic event. No single API is always the best source for every component.

A traditional team may separately integrate Polygon.io for market data, Finnhub for news, Alpha Vantage for indicators, the SEC EDGAR APIs for filings, and additional providers for consensus or compliance. Each integration creates credentials, schemas, rate limits, error handling, and licensing work.

QVeris exposes more than 10,000 financial and real-world capabilities through a common discovery layer. The application can search for the required outcome, compare inspected metadata, and dynamically choose a suitable source. That does not mean routing should be unconstrained. Teams should prefer approved providers, enforce freshness and cost limits, and require explicit permission for regulated or account-specific actions.

AI agent capability routing is the policy step after discovery. Discovery produces candidates; routing ranks or filters them according to schema compatibility, user entitlements, jurisdiction, latency, success rate, data quality, and price. Separating these stages makes the decision explainable and testable.

Capability Discovery vs Traditional Tool Integration

Discovery does not eliminate direct integrations. A latency-sensitive trading path or proprietary licensed feed may remain a dedicated service. A hybrid architecture can use fixed tools for critical deterministic flows and dynamic discovery for long-tail research, enrichment, and new user requests.

Build an Agent with Dynamic Capability Discovery

The following example accepts a user request, discovers candidates, inspects the best few, applies a simple policy, executes the selected tool, and returns an auditable envelope. In production, replace keyword scoring with schema validation and a tested router.

import asyncio

from dataclasses import dataclass

from typing import Any



from qveris import QverisClient



@dataclass

class AgentResult:

    query: str

    search_id: str

    tool_id: str

    execution_id: str

    data: Any

    cost: float | None



def choose_tool(inspected):

    """Prefer tools with parameters and usable sample input."""

    eligible = [

        tool for tool in inspected

        if tool.params and tool.examples

    ]

    if not eligible:

        raise RuntimeError("No inspected tool passed policy")



    # Add allowlists, freshness, success rate, and cost here.

    return eligible[0]



def build_parameters(tool, user_parameters):

    """Start from a verified example, then apply user values."""

    params = {}

    if tool.examples and tool.examples.sample_parameters:

        params.update(tool.examples.sample_parameters)

    params.update(user_parameters)

    return params



async def dynamic_finance_agent(query, user_parameters):

    client = QverisClient()

    try:

        # 1. Find capabilities by user intent.

        discovered = await client.discover(query, limit=10)

        if not discovered.results:

            raise RuntimeError("No matching capabilities")



        # 2. Load complete schemas for only the top candidates.

        candidate_ids = [

            item.tool_id for item in discovered.results[:4]

        ]

        inspected = await client.inspect(

            candidate_ids,

            search_id=discovered.search_id,

        )



        # 3. Apply application policy before execution.

        selected = choose_tool(inspected.results)

        params = build_parameters(selected, user_parameters)



        # 4. Execute with a bounded response size.

        called = await client.call(

            selected.tool_id,

            params,

            search_id=discovered.search_id,

            max_response_size=30_000,

        )

        if not called.success:

            raise RuntimeError(called.error_message)



        # 5. Return correlation IDs for logs and billing audits.

        return AgentResult(

            query=query,

            search_id=discovered.search_id,

            tool_id=selected.tool_id,

            execution_id=called.execution_id,

            data=called.result,

            cost=called.cost,

        )

    finally:

        await client.close()



async def main():

    result = await dynamic_finance_agent(

        query=(

            "real-time stock price with timestamp "

            "and exchange information"

        ),

        user_parameters={"ticker": "AAPL"},

    )

    print(result)



if __name__ == "__main__":

    asyncio.run(main())

The example keeps the decision deterministic, but an LLM can participate after candidate inspection. Give it concise inspected metadata, not the full catalog, and ask it to choose within an allowlist. Validate the selected tool and arguments in code before Call. Treat descriptions and provider output as untrusted external content.

Production Routing and Evaluation

Record every candidate, rejection reason, selected tool, parameters, latency, result validity, and cost. Build evaluation cases for common and adversarial requests. Measure retrieval recall, top-k relevance, schema compatibility, successful execution, and unsupported-tool selection. A discovery system can return a semantically similar capability that still lacks the required exchange, region, or update frequency.

Use tenant-aware policies. Enterprise users may have different providers, regions, budgets, or compliance permissions. Cache discovery results for stable intents, but re-inspect when schemas or operational metadata change. Never let cached routing bypass current authorization.

Production Design for AI Agent Tool Discovery

Build a Searchable Capability Index

High-quality discovery depends on metadata, not only embeddings. Index capability names, descriptions, parameter names, output fields, provider coverage, regions, freshness, authentication requirements, and examples. Add controlled taxonomy for asset class, document type, action risk, and data latency. Hybrid retrieval can combine lexical matching for exact concepts such as “10-K” with semantic matching for broader requests such as “latest annual filing.”

Version the index and retain the metadata used for every decision. When a description or schema changes, the team should be able to reproduce why an older request selected a particular tool. Relevance tuning should use real user tasks and labeled acceptable tools rather than generic similarity benchmarks.

Apply Security Before Tools Reach the Model

Filter candidates by tenant permissions, data contracts, geography, environment, and action risk before placing them in model context. Read-only research tools may be automatically available, while trading, account modification, or personally identifiable information should require stronger authorization and confirmation. Keep API credentials outside prompts and inject them only inside the trusted execution layer.

Treat tool descriptions and returned content as untrusted input. A provider response can contain text that looks like instructions to the model. Separate data from system instructions, validate structured output, enforce response-size limits, and remove unsupported fields before the result enters the reasoning loop.

Design Fallbacks Without Hiding Data Differences

A fallback should not silently replace one dataset with another. Providers may differ in adjustment policies, timestamps, currency, venue, or licensing. Define equivalence groups only for capabilities that satisfy the same research contract. When routing switches provider, include the source change in logs and downstream output.

Use circuit breakers for failing tools and health-aware ranking for temporary outages. However, operational success rate should not overpower schema relevance or user entitlement. The fastest tool is not the right tool when it lacks the required field or market.

Capability Discovery Use Cases for AI Agents

In each case, discovery expands what the agent can consider while routing policy defines what it may actually execute. This separation prevents “autonomy” from becoming uncontrolled access.

Getting Started with AI Agent Tool Discovery

Create a free QVeris account and generate an API key. Current onboarding includes 1,000 signup credits and 100 daily login credits. Discover and Inspect are always free; only Call consumes credits.

Use the Python SDK for application control, or connect Claude Code, Cursor, OpenCode, and other compatible clients through the QVeris MCP server. Start with a narrow intent and an allowlist, inspect candidate schemas, test calls with a small budget, then add routing rules and evaluation cases. Review the current documentation before deployment.

Why Capability Discovery for AI Agents Matters

Agents cannot become broadly useful by loading an ever-growing static list of functions. They need a controlled way to find relevant capabilities, understand contracts, select according to policy, and execute with traceable outcomes. QVeris provides that path through Discover, Inspect, and Call across a finance-oriented catalog.

The central value of capability discovery for AI agents is not unlimited tool access. It is making the path from intent to the right approved tool searchable, explainable, auditable, and economical.

Official Sources for MCP Tool Discovery

• Model Context Protocol documentation
• Anthropic tool-use documentation
• QVeris Python SDK
• QVeris MCP server