Сервер Octagon MCP предоставляет специализированные финансовые исследования и анализ на базе искусственного интеллекта путем интеграции с API Octagon Market Intelligence, что позволяет пользователям легко анализировать и извлекать подробную информацию из публичных отчетов, стенограмм конференций по прибылям, финансовых показателей, данных фондового рынка и обширных частных рыночных транзакций в Claude Desktop и других популярных клиентах MCP.
✅ Специализированные агенты ИИ для публичных рыночных данных
Анализ отчетов SEC и извлечение данных (более 8000 публичных компаний 10-K, 10-Q, 8-K, 20-F, S-1)
Анализ стенограммы телефонного разговора о доходах (исторические и текущие данные за 10 лет)
Анализ финансовых показателей и коэффициентов (исторические и текущие данные за 10 лет)
Доступ к данным фондового рынка (более 10 000 активных тикеров, ежедневные исторические и текущие данные)
✅ Специализированные агенты ИИ для данных частного рынка
Исследование частных компаний (более 3 млн компаний)
Раунды финансирования и исследования венчурного капитала (более 500 тыс. сделок)
Исследование сделок M&A и IPO (более 2 млн сделок)
Исследование долговых сделок (более 1 млн сделок)
✅ Специализированные ИИ-агенты для глубоких исследований
Возможности веб-скрапинга (скрипты json, csv, python)
Комплексные инструменты глубокого исследования
Related MCP server: FundzWatch MCP Server
Чтобы использовать Octagon MCP, вам необходимо:
Зарегистрируйте бесплатную учетную запись на Octagon
После входа в систему в левом меню перейдите в раздел API Keys.
Сгенерировать новый ключ API
Используйте этот ключ API в своей конфигурации как значение OCTAGON_API_KEY
Перед установкой или запуском Octagon MCP вам необходимо установить в вашей системе npx (который поставляется с Node.js и npm).
Установите Homebrew (если у вас его нет):
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Установите Node.js (включая npm и npx):
brew install nodeЭто установит последнюю версию Node.js, npm и npx.
Проверьте установку:
node -v
npm -v
npx -vЗагрузите установщик Node.js:
Перейдите по ссылке https://nodejs.org/ и загрузите версию LTS для Windows.
Запустите установщик и следуйте инструкциям. Это установит Node.js, npm и npx.
Проверьте установку: откройте командную строку и выполните:
node -v
npm -v
npx -vЕсли вы видите номера версий для всех трех версий, вы готовы приступить к выполнению следующих шагов установки.
Чтобы настроить Octagon MCP для Claude Desktop:
Открыть рабочий стол Клода
Перейдите в Настройки > Разработчик > Изменить конфигурацию.
Добавьте следующее в ваш claude_desktop_config.json (замените your-octagon-api-key на ваш ключ API Octagon):
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Перезапустите Клода, чтобы изменения вступили в силу.
Настройка Cursor Desktop 🖥️ Примечание: требуется версия Cursor 0.45.6+
Чтобы настроить Octagon MCP в Cursor:
Открыть настройки курсора
Перейти к разделу «Функции» > «Серверы MCP»
Нажмите «+ Добавить новый сервер MCP»
Введите следующее:
Имя: «octagon-mcp» (или другое предпочитаемое вами имя)
Тип: "команда"
Команда: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp
Если вы используете Windows и столкнулись с проблемами, попробуйте
cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"
Замените your-octagon-api-key на ваш ключ API Octagon.
После добавления обновите список серверов MCP, чтобы увидеть новые инструменты. Composer Agent автоматически использует Octagon MCP, когда это уместно, но вы можете явно запросить его, описав свои потребности в инвестиционных исследованиях. Откройте Composer с помощью Command+L (Mac), выберите «Agent» рядом с кнопкой «Отправить» и введите свой запрос.
Добавьте это в ваш ./codeium/windsurf/model_config.json :
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpПодробную документацию по использованию агентов Octagon можно найти на нашем официальном сайте: https://docs.octagonagents.com
Документация включает в себя:
Подробные ссылки API
Инструкции по запросам, специфичные для агента
Примеры и варианты использования
Лучшие практики для инвестиционных исследований
Каждый инструмент использует один параметр prompt , который принимает запрос на естественном языке. Включите все соответствующие детали в подсказку.
Извлечение информации из документов SEC.
Пример:
What was Apple's gross margin percentage from their latest 10-Q filing?Анализируйте стенограммы конференций по вопросам финансовой отчетности.
Пример:
What did NVIDIA's CEO say about AI chip demand in their latest earnings call?Получите финансовые показатели и коэффициенты.
Пример:
Calculate the price-to-earnings ratio for Tesla over the last 4 quartersДоступ к данным фондового рынка.
Пример:
How has Apple's stock performed compared to the S&P 500 over the last 6 months?Изучите информацию о частных компаниях.
Пример:
What is the employee count and funding history for Anthropic?Исследуйте раунды финансирования стартапов и венчурный капитал.
Пример:
What was OpenAI's latest funding round size, valuation, and key investors?Исследуйте сделки слияний и поглощений и IPO.
Пример:
What was the acquisition price when Microsoft acquired GitHub?Специализированный агент базы данных для поиска информации об инвесторах.
Пример:
What is the latest investment criteria of Insight Partners?Специализированный агент базы данных для анализа частных долгов, заемщиков и кредиторов.
Пример:
List all the debt activities from borrower American TowerИзвлечение данных с любого общедоступного веб-сайта.
Пример:
Extract property prices and square footage data from zillow.com/san-francisco-ca/Проведение комплексного исследования по любой теме.
Пример:
Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins«Каковы были показатели выручки и чистой прибыли Amazon в четвертом квартале 2023 года?»
«Проанализируйте тенденции расходов Tesla на НИОКР за последние 3 года».
«Какие рекомендации дал генеральный директор NVIDIA относительно спроса на чипы ИИ в своем последнем отчете о финансовых результатах?»
«Сравните коэффициенты цена/прибыль, цена/продажи и EV/EBITDA для пяти крупнейших компаний по производству полупроводников».
«Каков был размер последнего раунда финансирования Anthropic, оценка и ключевые инвесторы?»
«Извлечь все поля данных из zillow.com/san-francisco-ca/»
«Исследовать финансовое влияние изменений политики конфиденциальности Apple на доходы и маржу компаний, занимающихся цифровой рекламой»
«Составьте список всех операций с задолженностью кредитора ING Group за четвертый квартал 2024 года»
«Сколько инвестиций Andreessen Horowitz сделал в стартапы в сфере ИИ за последние 12 месяцев?»
Проблемы с ключом API : убедитесь, что ваш ключ API Octagon правильно установлен в файле среды или конфигурации.
Проблемы с подключением : убедитесь, что подключение к API Octagon работает правильно.
Ограничение скорости : если вы столкнулись с ошибками ограничения скорости, уменьшите частоту запросов.
env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpМассачусетский технологический институт
⭐ Поставьте этому репо звездочку, если он оказался вам полезен!
[COMPREHENSIVE MARKET INTELLIGENCE] Orchestrates all agents for comprehensive market intelligence analysis. Capabilities: Combines insights from SEC filings, earnings calls, financial metrics, stock data, institutional holdings, private company research, funding analysis, M&A transactions, investor intelligence, and debt analysis to provide holistic market intelligence. Best for: Complex research requiring multiple data sources and comprehensive analysis across public and private markets. Example queries: 'Retrieve year-over-year growth in key income-statement items for AAPL, limited to 5 records and filtered by period FY', 'Analyze the latest 10-K filing for AAPL and extract key financial metrics and risk factors', 'Retrieve the daily closing prices for AAPL over the last 30 days', 'Analyze AAPL's latest earnings call transcript and extract key insights about future guidance', 'Provide a comprehensive overview of Stripe, including its business model and key metrics', 'Retrieve the funding history for Stripe, including all rounds and investors'.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Your natural language query or request for the agent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While it mentions the tool 'orchestrates all agents' and lists data sources, it doesn't disclose critical behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, response format, or error handling. The description focuses on capabilities rather than operational behavior.
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 front-loaded with purpose and capabilities, but becomes verbose with the lengthy list of data sources and multiple example queries. While all content is relevant, the example section could be more concise. The structure is logical but could be more efficiently organized.
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 this is a complex orchestration tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are structured, error conditions, or operational constraints. The example queries help but don't compensate for the lack of behavioral and output documentation needed for effective agent use.
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% with the single 'prompt' parameter well-documented as 'Your natural language query or request for the agent.' The description adds value through example queries that illustrate what constitutes a good prompt, but doesn't provide additional parameter-specific guidance beyond what the schema already states.
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 'orchestrates all agents for comprehensive market intelligence analysis' and lists specific capabilities like SEC filings, earnings calls, financial metrics, etc. It distinguishes from siblings by emphasizing comprehensive multi-source analysis, though it doesn't explicitly name the sibling tools for comparison.
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 clear context with 'Best for: Complex research requiring multiple data sources and comprehensive analysis across public and private markets.' It includes example queries that illustrate appropriate use cases. However, it doesn't explicitly state when NOT to use this tool or directly compare it to the sibling tools by name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
[PUBLIC & PRIVATE MARKET INTELLIGENCE] A comprehensive agent that can utilize multiple sources for deep research analysis. Capabilities: Aggregate research across multiple data sources, synthesize information, and provide comprehensive investment research. Best for: Investment research questions requiring up-to-date aggregated information from the web. Example queries: 'Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins', 'Analyze the competitive landscape in the cloud computing sector, focusing on AWS, Azure, and Google Cloud margin and growth trends', 'Investigate the factors driving electric vehicle adoption and their impact on battery supplier financials'.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Your natural language query or request for the agent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions capabilities like aggregation and synthesis, but lacks critical behavioral details such as rate limits, authentication requirements, data freshness guarantees, or potential costs. The description doesn't contradict annotations (since none exist), but provides insufficient operational context for a tool performing complex research.
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 moderately structured with capability lists, usage guidance, and examples, but could be more front-loaded. Some sentences like 'Capabilities: Aggregate research across multiple data sources, synthesize information, and provide comprehensive investment research' could be more efficiently integrated. The bracketed '[PUBLIC & PRIVATE MARKET INTELLIGENCE]' adds little value and disrupts flow.
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?
For a complex research tool with no annotations and no output schema, the description is incomplete. It doesn't explain what format the research results will take, whether they include citations or sources, how comprehensive the aggregation is, or any limitations on research scope. The examples help but don't compensate for missing behavioral and output context that an agent would need to use this tool effectively.
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 its single parameter ('prompt'), which is well-documented as 'Your natural language query or request for the agent'. The description adds value by providing example queries that illustrate appropriate prompt content, but doesn't add significant semantic information beyond what the schema already provides. With high schema coverage, the 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's purpose as a comprehensive research agent that aggregates, synthesizes, and provides investment research using multiple data sources. It specifies the verb ('utilize multiple sources for deep research analysis') and resource ('investment research questions'), but doesn't explicitly differentiate from sibling tools like 'octagon-agent' or 'octagon-scraper-agent' beyond mentioning its comprehensive nature.
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 clear context for when to use this tool: 'Best for: Investment research questions requiring up-to-date aggregated information from the web' and includes example queries. However, it doesn't explicitly state when NOT to use it or mention alternatives like the sibling tools, leaving some ambiguity about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
[PUBLIC & PRIVATE MARKET INTELLIGENCE] Specialized agent for financial data extraction from investor websites. Capabilities: Extract structured financial data from investor relations websites, tables, and online financial sources. Best for: Gathering financial data from websites that don't have accessible APIs. Example queries: 'Extract all data fields from zillow.com/san-francisco-ca/', 'Extract all data fields from www.carvana.com/cars/'.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Your natural language query or request for the agent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but provides minimal behavioral disclosure. It mentions 'financial data extraction' but doesn't describe rate limits, authentication needs, error handling, or what happens when extraction fails. The description doesn't contradict annotations since none exist, but it's insufficient for a tool performing web scraping operations.
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 reasonably concise with three focused sentences: purpose statement, capabilities, usage guidance, and examples. The bracketed '[PUBLIC & PRIVATE MARKET INTELLIGENCE]' adds some noise, but overall the structure is clear and front-loaded with the core purpose.
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?
For a web scraping tool with no annotations and no output schema, the description is incomplete. It doesn't explain what structured data format to expect, error conditions, rate limits, or authentication requirements. The examples help but don't compensate for missing behavioral and output information critical for an AI agent.
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 description doesn't mention the 'prompt' parameter at all, though schema description coverage is 100% with the parameter well-documented as 'Your natural language query or request for the agent'. The description's example queries imply what the prompt should contain, but adds minimal value beyond what the schema already provides.
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's purpose as 'financial data extraction from investor websites' with specific capabilities like extracting structured data from tables and online financial sources. It distinguishes from siblings by specifying 'financial data' focus, though not explicitly contrasting with 'octagon-agent' or 'octagon-deep-research-agent'.
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 clear context for when to use this tool: 'Best for: Gathering financial data from websites that don't have accessible APIs.' It gives two example queries showing practical applications. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools.
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.
v1.0.0octagon-agentoctagon-companies-agentoctagon-deals-agentoctagon-debts-agentoctagon-financials-agentoctagon-funding-agentoctagon-investors-agentoctagon-sec-agentoctagon-stock-data-agentoctagon-transcripts-agentoctagon-companies-agentoctagon-deals-agentoctagon-debts-agentoctagon-deep-research-agentoctagon-financials-agentoctagon-funding-agentoctagon-investors-agentoctagon-scraper-agentoctagon-sec-agentoctagon-stock-data-agentoctagon-transcripts-agentScored across 3 tools
The tools have overlapping purposes, causing significant ambiguity. All three tools are described as providing comprehensive market intelligence, with octagon-agent and octagon-deep-research-agent both focusing on aggregated research across multiple sources, making it unclear when to choose one over the other. The descriptions do not clearly delineate distinct boundaries, leading to potential misselection.
The tool names follow a highly consistent pattern with the prefix 'octagon-' followed by a descriptive suffix ('agent', 'deep-research-agent', 'scraper-agent'). This uniform naming convention makes the tools easily identifiable and predictable, with no deviations in style or structure.
With only 3 tools, the count feels thin for the broad scope of 'comprehensive market intelligence' covering public and private markets. While the tools aim to cover multiple data sources and analyses, the limited number may not adequately support the complex workflows implied by the descriptions, bordering on under-scoped for the domain.
There are significant gaps in the tool surface for market intelligence. The tools focus on aggregation and scraping but lack dedicated operations for specific actions like updating data, deleting records, or managing user queries, which are essential for a complete CRUD lifecycle. This incompleteness could lead to agent failures in handling varied tasks.
Market data, financial statements, valuation, research, and news for investment workflows.
Company and market intelligence, news, enrichment, and agentic workflows for dealmakers.
Live market data, financial analysis, and portfolio research tools across 10,000+ tickers.
Private company data, entity-resolved news, targeted lists, and alternative signals for AI agents.
