El servidor Octagon MCP proporciona investigación y análisis financieros especializados impulsados por IA al integrarse con la API de inteligencia de mercado de Octagon, lo que permite a los usuarios analizar y extraer fácilmente información detallada de presentaciones públicas, transcripciones de llamadas de ganancias, métricas financieras, datos del mercado de valores y extensas transacciones del mercado privado dentro de Claude Desktop y otros clientes populares de MCP.
✅ Agentes de IA especializados para datos del mercado público
Análisis y extracción de datos de presentaciones ante la SEC (más de 8000 empresas públicas 10-K, 10-Q, 8-K, 20-F, S-1)
Análisis de la transcripción de la llamada de ganancias (10 años de historial y actual)
Análisis de métricas y ratios financieros (10 años históricos y actuales)
Acceso a datos del mercado de valores (más de 10.000 tickers activos, históricos diarios y actuales)
✅ Agentes de IA especializados para datos del mercado privado
Investigación de empresas privadas (más de 3 millones de empresas)
Rondas de financiación e investigación de capital riesgo (más de 500.000 operaciones)
Investigación de transacciones de fusiones y adquisiciones y IPO (más de 2 millones de acuerdos)
Investigación de transacciones de deuda (más de 1 millón de transacciones)
✅ Agentes de IA especializados para investigación profunda
Capacidades de raspado web (json, csv, scripts de Python)
Herramientas integrales de investigación profunda
Related MCP server: FundzWatch MCP Server
Para utilizar Octagon MCP, necesitas:
Regístrese para obtener una cuenta gratuita en Octagon
Después de iniciar sesión, desde el menú de la izquierda, navegue hasta Claves API
Generar una nueva clave API
Utilice esta clave API en su configuración como el valor OCTAGON_API_KEY
Antes de instalar o ejecutar Octagon MCP, debe tener npx (que viene con Node.js y npm) instalado en su sistema.
Instalar Homebrew (si no lo tienes):
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Instalar Node.js (incluye npm y npx):
brew install nodeEsto instalará la última versión de Node.js, npm y npx.
Verificar la instalación:
node -v
npm -v
npx -vDescargue el instalador de Node.js:
Vaya a https://nodejs.org/ y descargue la versión LTS para Windows.
Ejecute el instalador y siga las instrucciones. Esto instalará Node.js, npm y npx.
Verificar la instalación: Abra el símbolo del sistema y ejecute:
node -v
npm -v
npx -vSi ve los números de versión de los tres, está listo para continuar con los pasos de instalación a continuación.
Para configurar Octagon MCP para Claude Desktop:
Abra Claude Desktop
Vaya a Configuración > Desarrollador > Editar configuración
Agregue lo siguiente a su claude_desktop_config.json (reemplace your-octagon-api-key con su clave API de Octagon):
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Reinicie Claude para que los cambios surtan efecto.
Configuración de Cursor Desktop 🖥️ Nota: Requiere la versión de Cursor 0.45.6+
Para configurar Octagon MCP en Cursor:
Abrir configuración del cursor
Vaya a Características > Servidores MCP
Haga clic en "+ Agregar nuevo servidor MCP"
Introduzca lo siguiente:
Nombre: "octagon-mcp" (o el nombre que prefiera)
Tipo: "comando"
Comando: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp
Si está usando Windows y tiene problemas, pruebe
cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"
Reemplace your-octagon-api-key con su clave API de Octagon.
Tras añadir, actualice la lista de servidores MCP para ver las nuevas herramientas. El Agente de Composer usará automáticamente Octagon MCP cuando corresponda, pero puede solicitarlo explícitamente describiendo sus necesidades de investigación de inversiones. Acceda a Composer con Comando+L (Mac), seleccione "Agente" junto al botón de envío e introduzca su consulta.
Agregue esto a su ./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-mcpPara obtener documentación completa sobre el uso de los agentes de Octagon, visite nuestra documentación oficial en: https://docs.octagonagents.com
La documentación incluye:
Referencias API detalladas
Pautas de consulta específicas del agente
Ejemplos y casos de uso
Mejores prácticas para la investigación de inversiones
Cada herramienta utiliza un único parámetro prompt que acepta una consulta en lenguaje natural. Incluya todos los detalles relevantes en su solicitud.
Extraer información de los archivos de la SEC.
Ejemplo:
What was Apple's gross margin percentage from their latest 10-Q filing?Analizar las transcripciones de las llamadas de ganancias.
Ejemplo:
What did NVIDIA's CEO say about AI chip demand in their latest earnings call?Recupere métricas y ratios financieros.
Ejemplo:
Calculate the price-to-earnings ratio for Tesla over the last 4 quartersAcceda a datos del mercado de valores.
Ejemplo:
How has Apple's stock performed compared to the S&P 500 over the last 6 months?Investigar información de empresas privadas.
Ejemplo:
What is the employee count and funding history for Anthropic?Investiga rondas de financiación de empresas emergentes y capital riesgo.
Ejemplo:
What was OpenAI's latest funding round size, valuation, and key investors?Investigar transacciones de fusiones y adquisiciones (M&A) y IPO.
Ejemplo:
What was the acquisition price when Microsoft acquired GitHub?Un agente de base de datos especializado para buscar información sobre inversores.
Ejemplo:
What is the latest investment criteria of Insight Partners?Un agente de base de datos especializado para analizar deudas privadas, prestatarios y prestamistas.
Ejemplo:
List all the debt activities from borrower American TowerExtraer datos de cualquier sitio web público.
Ejemplo:
Extract property prices and square footage data from zillow.com/san-francisco-ca/Realizar investigaciones exhaustivas sobre cualquier tema.
Ejemplo:
Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins"¿Cuáles fueron las cifras de ingresos y beneficios netos de Amazon en el cuarto trimestre de 2023?"
"Analice las tendencias de gasto en I+D de Tesla durante los últimos 3 años".
"¿Qué orientación proporcionó el CEO de NVIDIA con respecto a la demanda de chips de IA en su última presentación de resultados?"
"Compare las relaciones precio-beneficio, precio-ventas y EV/EBITDA de las cinco principales empresas de semiconductores".
"¿Cuál fue el tamaño, la valoración y los inversores clave de la última ronda de financiación de Anthropic?"
Extraer todos los campos de datos de zillow.com/san-francisco-ca/
Investigar el impacto financiero de los cambios de privacidad de Apple en los ingresos y márgenes de las empresas de publicidad digital.
Recopilar toda la actividad de deuda del prestamista ING Group en el cuarto trimestre de 2024.
"¿Cuántas inversiones realizó Andreessen Horowitz en startups de IA en los últimos 12 meses?"
Problemas de clave API : asegúrese de que su clave API de Octagon esté configurada correctamente en el entorno o en el archivo de configuración.
Problemas de conexión : asegúrese de que la conectividad a la API de Octagon funcione correctamente.
Limitación de velocidad : si encuentra errores de limitación de velocidad, reduzca la frecuencia de sus solicitudes.
env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpInstituto Tecnológico de Massachusetts (MIT)
⭐ ¡Estrella este repositorio si lo encuentras útil!
[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.
