Before an Agent Calls a Tool, It Should Know Three Things: Can It Be Used, How Should It Be Called, and What Will It Cost?
As AI agents move from answering questions to completing tasks, the hard part is often no longer whether a model can generate a paragraph of text. The real challenge is whether it can choose the right capability from thousands of external tools, construct the right parameters, and make an explainable decision before any cost is incurred.
If tool search and tool execution are collapsed into a single step, agents can easily run into three classes of problems: finding an interface with a similar name but the wrong capability, reusing stale parameters or another provider's schema, or launching a real paid call before the billing rules have been checked.
QVeris separates this process into three explicit stages: Discover, Inspect, and Call. The point is not to make the agent take extra steps. It is to turn an invisible act of guessing and executing into a decision chain that can be reviewed, confirmed, and traced.
From Finding a Tool to Selecting a Usable Capability
Traditional tool calling usually starts with a function name. The model reads a tool description, guesses which function is most appropriate, and then generates parameters directly. That pattern works for a small, stable set of internal tools maintained by the developer. But when an agent faces a large number of third-party data sources and real-time capabilities, the tool name alone is nowhere near enough.
The agent also needs to know who provides the capability, what data it covers, whether the parameters are complete, how success rate and latency look, how fresh the data is, and how much a successful call may cost. Only when these signals are part of the routing decision is the agent selecting a capability rather than guessing an interface.
| Stage | Question the agent must answer | Typical output | Cost |
|---|---|---|---|
| Discover | What candidate capabilities are available? | Candidate tools, providers, quality signals, and cost signals | Free |
| Inspect | How exactly should this tool be called? | Full parameter schema, required fields, enums, examples, and billing rules | Free |
| Call | Has execution of the real operation been confirmed? | Call result, execution ID, status, latency, and pre-settlement cost information | Based on the selected capability's billing rule |
Field Example: Asking a Coding Agent to Research NVIDIA Earnings and Market Performance
The following workflow can be run in Codex, Claude Code, Cursor, or any other agent client that supports remote MCP. Suppose we want to analyze NVIDIA's latest quarterly earnings, current stock price, and market reaction, but we do not want the agent to make a paid call immediately after finding the first relevant tool.
Step One: Discover Candidate Capabilities Only, Without Executing
First, give the agent a clear decision boundary:
我要分析 NVIDIA 最新一季财报、当前股价和市场反应。
请先只使用 QVeris Discover 搜索候选能力，不要执行任何 Call。
按数据覆盖、可靠性、延迟和计费规则比较候选工具，并说明你的选择依据。
At this point, the agent should return candidate capabilities rather than a final investment conclusion. A good intermediate result should explain which tools cover earnings or company fundamentals, which tools cover real-time or delayed market data, which providers the candidates come from, and whether success rate, latency, and estimated cost are visible.
Step Two: Inspect the Best Candidates
After narrowing the candidates, do not construct parameters from memory. Ask the agent to inspect the actual tool IDs returned by the current discovery step:
Inspect 你推荐的财报工具和行情工具。
列出每个工具的必填参数、枚举限制、时间范围、示例参数和调用前成本。
如果 Discover 与 Inspect 的信息有冲突，以 Inspect 的当前 schema 为准。
仍然不要执行 Call。
This step can surface many real issues before execution. For example, the tool may require a ticker rather than a company name; the exchange may need to be expressed as NASDAQ, US, or another enum; the reporting period may need to be quarterly, annual, or date-based; and real-time quotes and historical quotes may be provided by separate capabilities.
Step Three: Ask the Agent to Produce an Execution Plan and Cost Explanation
Before any real call, ask the agent to translate its intended execution into a plan that a person can review:
根据 Inspect 结果生成最终执行计划。
逐项列出：工具、Provider、用途、实际参数、预计费用和失败后的处理方式。
在我明确确认前不要调用；不要自动切换到未展示的付费工具。
This is especially useful for tasks that may call multiple data sources. The user can remove unnecessary calls, adjust the time range, or approve only part of the plan. What the agent receives is not open-ended authorization, but explicit confirmation for the current task.
Step Four: After Confirmation, Call the Tool and Preserve Execution Evidence
Once execution is confirmed, the agent should use the tool ID and schema it just inspected to construct the parameters. The response should preserve the execution ID, success status, latency, and billing fields. If the provider rejects the request, the parameters are invalid, or the upstream service is unavailable, the agent should report the failure clearly instead of hiding it behind plausible prose.
我确认执行上面的两个调用。
完成后请分别报告 execution ID、成功状态、实际耗时、计费摘要和数据时间。
任一调用失败时停止后续综合分析，先解释失败原因和是否产生费用。
Step Five: Reconcile the Result with Usage History and Credits Ledger
The cost fields in a call response are useful for immediate display, but final settlement should be checked against Usage History and the Credits Ledger. For team agents, this step answers three practical questions: which task produced the call, how successful, failed, or skipped calls were settled, and whether the balance movement matches the call record.
A genuinely closed-loop agent task is therefore not just one that "got an answer." It is one that also preserves the selection rationale, parameter source, user confirmation, execution status, and final settlement record.
Where This Workflow Fits
Financial and business research. The same question often requires quotes, earnings, news, and macro data. Comparing coverage and freshness first reduces the chance of calling the wrong source and having to redo the work.
Compliance and business verification. KYC, sanctions screening, and company registry tools often have strict parameter and regional coverage constraints. Inspect helps the agent find those boundaries before execution.
Development and operations agents. When working with GitHub, monitoring systems, cloud services, or external state data, the agent can first discover and inspect capabilities in a read-only way, then seek approval for operations that incur cost or change state.
Long-running automation. Separating Discover, Inspect, and Call allows teams to apply different policies to different stages. Discovery and inspection can run automatically, while real calls can enter human review based on amount, tool type, or risk.
Four Suggestions for Agent Developers
- Do not guess parameters from tool names. Always use the current tool ID and schema returned by Discover or Inspect.
- Treat the billing rule as a routing condition, not as an afterthought once the call has finished.
- Preserve search IDs, execution IDs, and task IDs so selection, execution, and settlement can be connected later.
- Set explicit human-in-the-loop thresholds for high-cost calls, write operations, and sensitive data.
Conclusion
Agent autonomy does not mean "call everything without confirmation." A more reliable autonomous system should first discover options, inspect constraints, explain its choices, and then execute within the right permissions and budget.
QVeris organizes 10,000+ real-world capabilities into a unified Discover, Inspect, and Call flow, so coding agents can do more than call tools. They can make checkable decisions before they call them.
You can try capability discovery on QVeris's tool search page, or connect the same workflow to Codex, Claude Code, Cursor, and other agent clients through Hosted MCP.