Готовые к работе инструменты финансовой аналитики — структурированные, а не «сырые».
Toolstem — это MCP-сервер (Model Context Protocol), который превращает «сырые» данные финансового рынка в структурированную, синтезированную аналитику для ИИ-агентов. В отличие от простых оберток, которые лишь предоставляют доступ к REST API поставщика, каждый инструмент Toolstem объединяет несколько источников данных, выводит сигналы и выполняет математические вычисления, которые агенту пришлось бы делать самостоятельно.
Один вызов. Один удобный для агента JSON-ответ. Никаких вложенных массивов для парсинга, никакой склейки данных из разных эндпоинтов, никакого шаблонного кода для проверки на null.
Большинство MCP-серверов для финансовых данных предоставляют по одному инструменту на каждый API-эндпоинт, заставляя вашего агента выполнять 4–5 последовательных вызовов, писать связующий код и самостоятельно анализировать структуру «сырых» данных. Toolstem устроен иначе:
Параллельная загрузка данных — каждый инструмент одновременно обращается к нескольким источникам.
Производные сигналы — понятные человеку рекомендации, такие как UNDERVALUED, STRONG, ACCELERATING, вычисленные на основе «сырых» чисел.
Предварительные вычисления — CAGR, рост год к году (YoY), динамика маржи, отклонение от 52-недельного максимума/минимума, доходность свободного денежного потока (FCF yield) и многое другое уже включено в ответ.
Плоская, предсказуемая схема — никаких глубоко вложенных особенностей поставщиков, просачивающихся в промпты агента.
Отказоустойчивость — если один из вышестоящих эндпоинтов не отвечает, остальная часть ответа все равно будет получена с подставленными значениями null.
Related MCP server: TickerAPI
get_stock_snapshotКомплексный обзор акций, объединяющий котировки, профиль, оценку DCF и рейтинг в единый ответ.
Входные данные:
{
"symbol": "AAPL"
}Пример ответа (сокращенный):
{
"symbol": "AAPL",
"company_name": "Apple Inc.",
"sector": "Technology",
"industry": "Consumer Electronics",
"exchange": "NASDAQ",
"price": {
"current": 178.52,
"change": 2.34,
"change_percent": 1.33,
"day_high": 179.80,
"day_low": 175.10,
"year_high": 199.62,
"year_low": 130.20,
"distance_from_52w_high_percent": -10.57,
"distance_from_52w_low_percent": 37.11
},
"valuation": {
"market_cap": 2780000000000,
"market_cap_readable": "$2.78T",
"pe_ratio": 29.5,
"dcf_value": 195.20,
"dcf_upside_percent": 9.35,
"dcf_signal": "FAIRLY VALUED"
},
"rating": {
"score": 4,
"recommendation": "Buy",
"dcf_score": 5,
"roe_score": 4,
"roa_score": 4,
"de_score": 5,
"pe_score": 3
},
"fundamentals_summary": {
"beta": 1.28,
"avg_volume": 55000000,
"employees": 164000,
"ipo_date": "1980-12-12",
"description": "Apple Inc. designs, manufactures..."
},
"meta": {
"source": "Toolstem via Financial Modeling Prep",
"timestamp": "2026-04-17T18:30:00Z",
"data_delay": "End of day"
}
}Производные поля (отсутствуют в «сырых» API):
dcf_signal — UNDERVALUED, если потенциал роста по DCF > 10%, OVERVALUED, если < -10%, в противном случае FAIRLY VALUED.
market_cap_readable — удобный для чтения формат: $2.78T, $450.2B, $12.5M.
distance_from_52w_high_percent / distance_from_52w_low_percent — предварительно вычисленное положение в диапазоне.
get_company_metricsГлубокий фундаментальный анализ — прибыльность, финансовое состояние, денежный поток, рост и показатели на акцию — синтезированный из 5 эндпоинтов финансовой отчетности.
Входные данные:
{
"symbol": "AAPL",
"period": "annual"
}Параметр period принимает значения annual (по умолчанию) или quarter.
Пример ответа (сокращенный):
{
"symbol": "AAPL",
"period": "annual",
"latest_period_date": "2025-09-30",
"profitability": {
"revenue": 394328000000,
"revenue_readable": "$394.3B",
"revenue_growth_yoy": 7.8,
"net_income": 96995000000,
"net_income_readable": "$97.0B",
"gross_margin": 46.2,
"operating_margin": 31.5,
"net_margin": 24.6,
"roe": 160.5,
"roa": 28.3,
"roic": 56.2,
"margin_trend": "EXPANDING"
},
"financial_health": {
"total_debt": 111000000000,
"total_cash": 65000000000,
"net_debt": 46000000000,
"debt_to_equity": 1.87,
"current_ratio": 1.07,
"interest_coverage": 41.2,
"health_signal": "STRONG"
},
"cash_flow": {
"operating_cash_flow": 118000000000,
"free_cash_flow": 104000000000,
"free_cash_flow_readable": "$104.0B",
"fcf_margin": 26.4,
"capex": 14000000000,
"dividends_paid": 15000000000,
"buybacks": 89000000000,
"fcf_yield": 3.7
},
"growth_3yr": {
"revenue_cagr": 8.2,
"net_income_cagr": 10.1,
"fcf_cagr": 9.5,
"growth_signal": "ACCELERATING"
},
"per_share": {
"eps": 6.42,
"book_value_per_share": 3.99,
"fcf_per_share": 6.89,
"dividend_per_share": 0.96,
"payout_ratio": 14.9
},
"meta": {
"source": "Toolstem via Financial Modeling Prep",
"timestamp": "2026-04-17T18:30:00Z",
"periods_analyzed": 3,
"data_delay": "End of day"
}
}Производные поля:
margin_trend — EXPANDING, STABLE или CONTRACTING в зависимости от направления динамики чистой маржи.
health_signal — STRONG, ADEQUATE или WEAK на основе соотношения долга к собственному капиталу, коэффициента текущей ликвидности и покрытия процентов.
growth_signal — ACCELERATING, STEADY или DECELERATING на основе траектории роста год к году.
revenue_cagr, net_income_cagr, fcf_cagr — совокупные среднегодовые темпы роста за анализируемый период.
fcf_margin, fcf_yield — предварительно вычислены на основе денежного потока, выручки и рыночной капитализации.
npm install -g toolstem-mcp-serverЗапуск в качестве stdio-сервера:
FMP_API_KEY=your_key_here toolstem-mcp-serverЗапуск в качестве HTTP-сервера (потоковый HTTP-транспорт):
FMP_API_KEY=your_key_here PORT=3000 toolstem-mcp-server --httpДобавьте в ваш claude_desktop_config.json:
{
"mcpServers": {
"toolstem": {
"command": "npx",
"args": ["-y", "toolstem-mcp-server"],
"env": {
"FMP_API_KEY": "your_fmp_api_key"
}
}
}
}Toolstem доступен в Smithery для установки в один клик в поддерживаемые MCP-клиенты.
Доступен в магазине Apify как Actor toolstem-financial-data. Вызывайте его из вашего рабочего процесса Apify с входными данными:
{
"tool": "get_stock_snapshot",
"symbol": "AAPL"
}или
{
"tool": "get_company_metrics",
"symbol": "AAPL",
"period": "annual"
}Результаты отправляются в набор данных по умолчанию. Актор монетизируется за каждый вызов инструмента через модель Apify Pay-Per-Event.
Соберите и запустите HTTP-транспорт:
npm install
npm run build
FMP_API_KEY=your_key npm run start:httpВаш MCP-клиент сможет подключиться к POST http://your-host:3000/mcp.
Переменная | Обязательно | Описание |
| Да | API-ключ Financial Modeling Prep. Получите его на financialmodelingprep.com. |
| Нет | Порт для HTTP-транспорта. По умолчанию |
npm install
npm run dev # stdio, hot reload via tsx
npm run build # TypeScript -> dist/
npm start # run built stdio server
npm run start:http # run built HTTP serversrc/
├── index.ts # MCP server entry (stdio + Streamable HTTP)
├── actor.ts # Apify Actor entry
├── services/
│ └── fmp.ts # Financial Modeling Prep API client
├── tools/
│ ├── get-stock-snapshot.ts
│ └── get-company-metrics.ts
└── utils/
└── formatting.ts # Market cap formatting, CAGR, trend signalsВсе эндпоинты FMP обернуты в единый класс FmpClient. Реализации инструментов параллельно обращаются к нескольким методам клиента через Promise.all, а затем синтезируют объединенный результат.
MIT — см. LICENSE.
Toolstem — структурированная финансовая аналитика для экономики, ориентированной на агентов.
Side-by-side comparison of 2-5 companies across price, valuation (P/E, P/B, P/S, EV/EBITDA, DCF), profitability (margins, ROE, ROA, ROIC), financial health (D/E, current ratio, interest coverage), growth (revenue and earnings YoY), dividends, and analyst ratings. Returns derived rankings showing which company leads each dimension — lowest_pe, highest_margin, strongest_balance_sheet, best_growth, most_undervalued, highest_rated. Use this for investment comparisons, competitive analysis, or evaluating alternatives in the same sector.
| Name | Required | Description | Default |
|---|---|---|---|
| symbols | Yes | 2-5 stock ticker symbols to compare (e.g., ["AAPL", "MSFT", "GOOGL"]) |
| Name | Required | Description |
|---|---|---|
| symbols_compared | Yes | |
| comparison_date | Yes | |
| companies | Yes | |
| rankings | Yes | |
| meta | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description aligns fully, detailing the read-only operation and output format (derived rankings). No contradictions, and the description adds significant behavioral context (categories of metrics, derived rankings) beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: first states core purpose, second lists all metric categories, third gives use cases. Front-loaded, no filler, every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (many metrics and derived rankings) and the presence of an output schema, the description is complete. It covers input constraints (2-5 symbols), output nature (derived rankings), and typical use cases. No gaps for an agent to misuse.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the symbols parameter has a detailed description including example). The tool description restates '2-5 companies' but adds no new semantics beyond the schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool performs side-by-side comparison of 2-5 companies across price, valuation, profitability, financial health, growth, dividends, and analyst ratings. It also lists derived rankings (lowest_pe, etc.). This clearly distinguishes from siblings get_company_metrics (likely single company) and get_stock_snapshot (likely a quick overview).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: 'Use this for investment comparisons, competitive analysis, or evaluating alternatives in the same sector.' It does not explicitly state when not to use or name alternatives, but the context is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Deep financial analysis including profitability, financial health, cash flow, growth (3-year CAGR), and per-share metrics. Synthesizes key metrics, financial ratios, income statement, balance sheet, and cash flow statement into one agent-ready response with derived signals: margin_trend (EXPANDING/STABLE/CONTRACTING), health_signal (STRONG/ADEQUATE/WEAK), and growth_signal (ACCELERATING/STEADY/DECELERATING). Use this for fundamental analysis, financial health checks, or when you need to understand a company's trajectory.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock ticker symbol (e.g., AAPL, MSFT, TSLA) | |
| period | No | Reporting period. Defaults to annual. | annual |
| Name | Required | Description |
|---|---|---|
| symbol | Yes | |
| period | Yes | |
| latest_period_date | Yes | |
| profitability | Yes | |
| financial_health | Yes | |
| cash_flow | Yes | |
| growth_3yr | Yes | |
| per_share | Yes | |
| meta | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, covering safety. The description adds value by explaining derived signals and output structure, but doesn't disclose additional behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with key content. Each sentence contributes: first lists included metrics, second explains output and use cases. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (handling return values), complete schema coverage, and annotations covering safety, the description provides sufficient context about purpose, usage, and derived signals. It is thorough for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters adequately. The description does not add extra parameter detail beyond what is in the schema, aligning with the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides deep financial analysis and synthesizes key metrics, ratios, and statements into an agent-ready response. It distinguishes from siblings (compare_companies and get_stock_snapshot) by emphasizing depth and derived signals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends use for fundamental analysis, financial health checks, or understanding a company's trajectory. While it doesn't directly mention alternatives, sibling tool names and the focus on depth imply when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a comprehensive stock snapshot including real-time price, valuation metrics, DCF analysis, and analyst ratings for any publicly traded company. Returns curated, agent-ready data synthesized from multiple sources in a single call — includes derived signals like dcf_signal (UNDERVALUED/FAIRLY VALUED/OVERVALUED), human-readable market cap, and 52-week range distance. Use this when you need a quick overview of a stock before digging into financials.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock ticker symbol (e.g., AAPL, MSFT, TSLA) |
| Name | Required | Description |
|---|---|---|
| symbol | Yes | |
| company_name | Yes | |
| sector | Yes | |
| industry | Yes | |
| exchange | Yes | |
| price | Yes | |
| valuation | Yes | |
| rating | Yes | |
| fundamentals_summary | Yes | |
| meta | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds behavioral context by explaining the tool synthesizes data from multiple sources, returns derived signals (dcf_signal), and provides curated agent-ready data. This adds value beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of three focused sentences. The first sentence states the main purpose, the second lists key output components, and the third provides usage guidance. No redundant or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter), presence of output schema, and rich annotations, the description sufficiently covers the tool's functionality, output highlights, and usage context. It explains derived signals and the nature of the data, making it complete for an agent to understand and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single required parameter 'symbol' (ticker). The description does not add additional semantic information about the parameter beyond what the schema already provides. With full schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a comprehensive stock snapshot including real-time price, valuation metrics, DCF analysis, and analyst ratings. It distinguishes from siblings by noting it is a quick overview before diving into financials, differentiating from get_company_metrics and compare_companies.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when you need a quick overview of a stock before digging into financials,' providing clear context for when to use the tool. It implies but does not explicitly state when not to use it or mention alternatives beyond the sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.2.9screen_stocksv1.1.0compare_companiesscreen_stocksv1.0.0get_company_metricsget_stock_snapshotThe two tools have clearly distinct purposes: get_company_metrics focuses on deep financial analysis and fundamental metrics, while get_stock_snapshot provides a comprehensive stock overview including real-time price and valuation. There is no overlap in functionality, making it easy for an agent to choose the right tool based on the task.
Both tools follow a consistent verb_noun naming pattern (get_company_metrics and get_stock_snapshot), using the same verb 'get' and descriptive nouns. This uniformity makes the tool set predictable and easy to understand.
With only two tools, the server feels under-scoped for financial analysis, as it lacks essential operations like searching for companies, comparing metrics, or updating data. While the tools are well-defined, the count is too low to cover a comprehensive financial domain effectively.
The tool set is severely incomplete for financial analysis, missing critical operations such as listing companies, retrieving historical data, or performing comparisons. Agents will face dead ends when trying to conduct thorough analysis beyond the two provided snapshots.
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SEC filings and financial data for AI agents: 55 tools for financials, valuation and supply chains.
The financial MCP for AI agents - 90+ financial tables, SEC filings, signals, alt-data.
Real SEC, 13F, insider, congress & macro data your AI agent can cite. Hosted MCP, 24 tools.
13-model stock valuation engine for AI agents - fair values for 5,900+ US stocks, updated daily.
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