Financial Modeling Prep API for AI Agents面向 AI Agent 的 Financial Modeling Prep API
Financial Modeling Prep API gives developers access to stock market data, financial statements, earnings, news, calendars, and company fundamentals. QVeris helps AI agents discover and call those financial capabilities through a unified routing layer.
Financial Modeling Prep API 为开发者提供股票市场数据、财务报表、财报、新闻、日历和公司基本面数据。QVeris 帮助 AI Agent 通过统一路由层发现并调用这些金融能力。
What Is the Financial Modeling Prep API?
什么是 Financial Modeling Prep API？
Financial Modeling Prep API, often called FMP API, is a financial data API for developers building dashboards, stock research tools, trading apps, screeners, and analytics systems. Its official documentation covers financial statements, historical prices, market calendars, company profiles, stock news, press releases, earnings reports, key metrics, and more.
Financial Modeling Prep API 通常也叫 FMP API，是面向开发者的金融数据 API，适合构建仪表盘、股票研究工具、交易应用、筛选器和分析系统。FMP 官方文档覆盖财务报表、历史价格、市场日历、公司资料、股票新闻、新闻稿、财报、关键指标等数据。
Financial Modeling Prep API Data Developers Can Use
开发者可使用的 Financial Modeling Prep API 数据
FMP provides income statements, balance sheets, cash flow statements, ratios, and standardized financial data for research and analytics.
FMP 提供利润表、资产负债表、现金流量表、财务比率和标准化财务数据，适合研究和分析。
Best for: fundamental analysis适合：基本面分析The FMP earnings calendar helps agents track upcoming and historical earnings dates, estimated EPS, actual EPS, and market events.
FMP 财报日历帮助 Agent 跟踪未来和历史财报日期、预估 EPS、实际 EPS 和市场事件。
Best for: earnings agents适合：财报 AgentCompany profile endpoints provide market capitalization, sector, industry, executives, descriptions, peers, and other operational context.
公司资料端点提供市值、板块、行业、高管、公司描述、同行和其他运营背景。
Best for: stock research适合：股票研究FMP stock news can support monitoring agents that need timely company news, market trend context, and structured headlines.
FMP 股票新闻可支持需要及时公司新闻、市场趋势背景和结构化标题的监控 Agent。
Best for: market monitoring适合：市场监控How QVeris Connects Financial Modeling Prep API to AI Agents
QVeris 如何把 Financial Modeling Prep API 连接到 AI Agent
FMP provides the financial data. QVeris provides the capability routing layer that helps AI agents discover, inspect, and call the right financial data capability when needed. This matters when an agent needs to combine FMP-style data with other tools, MCP servers, filings, news, prices, or internal workflows.
FMP 提供金融数据，QVeris 提供能力路由层，帮助 AI Agent 在需要时发现、检查并调用合适的金融数据能力。当 Agent 需要把 FMP 类型的数据与其他工具、MCP Server、文件披露、新闻、价格或内部工作流组合时，这一点尤其重要。
An agent can search for earnings calendar, stock news, financial statements, key metrics, or company profile capabilities.
Agent 可以搜索财报日历、股票新闻、财务报表、关键指标或公司资料能力。
Before calling, the agent can inspect inputs, output fields, examples, latency, provider notes, and estimated cost.
调用前，Agent 可以检查输入、输出字段、示例、延迟、供应商说明和预估成本。
QVeris helps agents call the selected capability in a consistent format and combine results with other financial signals.
QVeris 帮助 Agent 用一致格式调用被选中的能力，并与其他金融信号组合。
Direct FMP API Integration vs QVeris Routing
直接接入 FMP API 与 QVeris 路由对比
| Approach方式 | Best fit最适合 | Developer work开发工作量 | Agent valueAgent 价值 |
|---|---|---|---|
| Direct Financial Modeling Prep API直接接入 Financial Modeling Prep API | One app with known endpoints已知端点的单一应用 | Write API calls, auth, parsing, retries, and docs mapping编写 API 调用、认证、解析、重试和文档映射 | Strong if the workflow only needs FMP data如果只需要 FMP 数据，非常适合 |
| FMP MCP ServerFMP MCP Server | Claude, Cursor, or MCP-native assistantsClaude、Cursor 或 MCP 原生助手 | Configure MCP and expose FMP tools to the assistant配置 MCP，并把 FMP 工具暴露给助手 | Good when the agent already works inside an MCP client适合已经在 MCP 客户端里的 Agent |
| QVeris capability routingQVeris 能力路由 | Multi-source AI agents and production workflows多数据源 AI Agent 和生产工作流 | Discover, inspect, and call financial capabilities through one layer通过一层完成金融能力发现、检查和调用 | Best when the agent must choose among data sources dynamically适合 Agent 需要动态选择数据源的场景 |
Financial Modeling Prep API Use Cases for AI Agents
Financial Modeling Prep API 的 AI Agent 使用场景
Combine company profiles, key metrics, historical prices, statements, and news to generate structured research briefs.
组合公司资料、关键指标、历史价格、财务报表和新闻，生成结构化研究简报。
Use earnings calendar and earnings report data to monitor reporting dates, surprises, and follow-up research tasks.
使用财报日历和财报数据监控披露日期、超预期情况和后续研究任务。
Use stock news, press releases, quotes, and market calendars to detect events that matter to a watchlist.
使用股票新闻、新闻稿、报价和市场日历检测自选股相关事件。
When to Use FMP with QVeris
什么时候把 FMP 与 QVeris 一起使用
Use Financial Modeling Prep API directly when your application needs a known set of endpoints and a fixed data workflow. Use QVeris when your AI agent needs to discover financial capabilities at runtime, inspect schemas before calling, compare providers, or route FMP-powered data alongside other market, filing, news, and internal tools.
当应用只需要一组已知端点和固定数据工作流时，可以直接使用 Financial Modeling Prep API。当 AI Agent 需要在运行时发现金融能力、调用前检查 Schema、比较供应商，或把 FMP 类型数据与其他市场、文件、新闻和内部工具一起路由时，适合使用 QVeris。
What to Check Before Using the Financial Modeling Prep API
使用
FMP is useful because it gives developers broad finance data access through a familiar API model. For AI agents, however, the question is not only whether the endpoint exists. The agent must know which fields are fresh, which identifiers are required, whether the response includes enough context, and when the output should be routed through another capability for validation.
FMP 的优势在于用熟悉的 API 方式提供广泛金融数据。对 AI Agent 来说，问题不只是端点是否存在。Agent 还需要知道哪些字段是最新的、需要哪些标识符、响应是否包含足够上下文，以及什么时候应该路由到另一个能力进行验证。
For this page, useful fields include market data, fundamentals, statements, calendars, analyst data, company profiles, and financial ratios. These fields should be documented before the agent relies on them.
本页相关的关键字段包括市场数据、基本面、财务报表、日历、分析师数据、公司资料和财务比率。Agent 依赖这些字段前，应先确认其定义和更新规则。
Common workflows include developer prototypes, AI finance agents, dashboards, valuation tools, and research automation. QVeris can help route the intent to the right capability instead of hard-coding one endpoint for every task.
常见工作流包括开发者原型、AI 金融 Agent、看板、估值工具和研究自动化。QVeris 可以帮助把意图路由到合适能力，而不是为每个任务硬编码一个端点。
FMP API Implementation Notes for AI Agents
AI Agent 使用 FMP API 的实现注意事项
| Implementation area实现环节 | Recommended check建议检查 |
|---|---|
| Identifier handling标识符处理 | Confirm ticker, exchange, CIK, and company identifiers before calling downstream tools.调用下游工具前确认 ticker、交易所、CIK 和公司标识。 |
| Freshness数据新鲜度 | Expose timestamps and reporting periods so the model does not mix old and current data.暴露时间戳和报告期，避免模型混用旧数据和当前数据。 |
| FallbackFallback | Define whether the agent should retry, use another source, or ask the user when data is missing.当数据缺失时，定义 Agent 是重试、换来源还是询问用户。 |
Related QVeris Workflows
相关 QVeris 工作流
Use FMP as a strong finance data source, then connect it to QVeris when the agent needs discovery, inspection, routing, or multi-provider fallback. This lets the page stay useful for FMP searchers while naturally explaining why QVeris belongs in the agent layer.
可以把 FMP 作为重要金融数据源使用；当 Agent 需要发现、检查、路由或多供应商 fallback 时，再接入 QVeris。这样页面既能服务 FMP 搜索用户，也能自然说明 QVeris 在 Agent 层的价值。