Der Octagon MCP-Server bietet spezialisierte KI-gestützte Finanzforschung und -analyse durch die Integration mit der Octagon Market Intelligence API. So können Benutzer öffentliche Unterlagen, Transkripte von Telefonkonferenzen, Finanzkennzahlen, Börsendaten und umfangreiche private Markttransaktionen innerhalb von Claude Desktop und anderen beliebten MCP-Clients einfach analysieren und detaillierte Erkenntnisse daraus extrahieren.
✅ Spezialisierte KI-Agenten für öffentliche Marktdaten
Analyse von SEC-Anmeldungen und Datenextraktion (über 8.000 Aktiengesellschaften 10-K, 10-Q, 8-K, 20-F, S-1)
Analyse der Transkription der Telefonkonferenz zu den Quartalsergebnissen (10 Jahre historisch und aktuell)
Analyse der Finanzkennzahlen und -verhältnisse (10 Jahre historisch und aktuell)
Zugriff auf Börsendaten (über 10.000 aktive Ticker, täglich historisch und aktuell)
✅ Spezialisierte KI-Agenten für private Marktdaten
Forschung zu privaten Unternehmen (über 3 Millionen Unternehmen)
Finanzierungsrunden und Risikokapitalrecherche (über 500.000 Deals)
M&A- und IPO-Transaktionsrecherche (über 2 Mio. Deals)
Recherche zu Schuldentransaktionen (über 1 Mio. Transaktionen)
✅ Spezialisierte KI-Agenten für tiefgehende Forschung
Web-Scraping-Funktionen (JSON, CSV, Python-Skripte)
Umfassende Deep-Research-Tools
Related MCP server: FundzWatch MCP Server
Um Octagon MCP zu verwenden, müssen Sie:
Registrieren Sie sich für ein kostenloses Konto bei Octagon
Navigieren Sie nach der Anmeldung im linken Menü zu API-Schlüsseln
Generieren Sie einen neuen API-Schlüssel
Verwenden Sie diesen API-Schlüssel in Ihrer Konfiguration als OCTAGON_API_KEY -Wert
Bevor Sie Octagon MCP installieren oder ausführen, muss npx (das mit Node.js und npm geliefert wird) auf Ihrem System installiert sein.
Installieren Sie Homebrew (falls Sie es nicht haben):
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Installieren Sie Node.js (beinhaltet npm und npx):
brew install nodeDadurch wird die neueste Version von Node.js, npm und npx installiert.
Überprüfen Sie die Installation:
node -v
npm -v
npx -vLaden Sie das Node.js-Installationsprogramm herunter:
Gehen Sie zu https://nodejs.org/ und laden Sie die LTS-Version für Windows herunter.
Führen Sie das Installationsprogramm aus und folgen Sie den Anweisungen. Dadurch werden Node.js, npm und npx installiert.
Überprüfen Sie die Installation: Öffnen Sie die Eingabeaufforderung und führen Sie Folgendes aus:
node -v
npm -v
npx -vWenn Sie für alle drei Versionsnummern sehen, können Sie mit den folgenden Installationsschritten fortfahren.
So konfigurieren Sie Octagon MCP für Claude Desktop:
Öffnen Sie Claude Desktop
Gehen Sie zu Einstellungen > Entwickler > Konfiguration bearbeiten
Fügen Sie Folgendes zu Ihrer claude_desktop_config.json hinzu (Ersetzen Sie your-octagon-api-key durch Ihren Octagon-API-Schlüssel):
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Starten Sie Claude neu, damit die Änderungen wirksam werden
Cursor-Desktop konfigurieren 🖥️ Hinweis: Erfordert Cursor-Version 0.45.6+
So konfigurieren Sie Octagon MCP im Cursor:
Cursoreinstellungen öffnen
Gehen Sie zu Funktionen > MCP-Server
Klicken Sie auf „+ Neuen MCP-Server hinzufügen“.
Geben Sie Folgendes ein:
Name: „octagon-mcp“ (oder Ihr bevorzugter Name)
Typ: „Befehl“
Befehl: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp
Wenn Sie Windows verwenden und auf Probleme stoßen, versuchen Sie
cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"
Ersetzen Sie your-octagon-api-key durch Ihren Octagon-API-Schlüssel.
Aktualisieren Sie nach dem Hinzufügen die MCP-Serverliste, um die neuen Tools anzuzeigen. Der Composer Agent verwendet automatisch Octagon MCP, wenn dies erforderlich ist. Sie können es jedoch explizit anfordern, indem Sie Ihren Bedarf an Investmentrecherche beschreiben. Rufen Sie den Composer über Befehl+L (Mac) auf, wählen Sie „Agent“ neben der Schaltfläche „Senden“ und geben Sie Ihre Anfrage ein.
Fügen Sie dies zu Ihrer ./codeium/windsurf/model_config.json hinzu:
{
"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-mcpEine umfassende Dokumentation zur Verwendung von Octagon-Agenten finden Sie in unserer offiziellen Dokumentation unter: https://docs.octagonagents.com
Die Dokumentation umfasst:
Detaillierte API-Referenzen
Agentenspezifische Abfragerichtlinien
Beispiele und Anwendungsfälle
Best Practices für die Anlageforschung
Jedes Tool verwendet einen einzelnen prompt , der eine Abfrage in natürlicher Sprache akzeptiert. Fügen Sie alle relevanten Details in Ihre Eingabeaufforderung ein.
Extrahieren Sie Informationen aus SEC-Einreichungen.
Beispiel:
What was Apple's gross margin percentage from their latest 10-Q filing?Analysieren Sie die Transkripte von Telefonkonferenzen zu den Quartalsergebnissen.
Beispiel:
What did NVIDIA's CEO say about AI chip demand in their latest earnings call?Rufen Sie Finanzkennzahlen und -verhältnisse ab.
Beispiel:
Calculate the price-to-earnings ratio for Tesla over the last 4 quartersGreifen Sie auf Börsendaten zu.
Beispiel:
How has Apple's stock performed compared to the S&P 500 over the last 6 months?Recherchieren Sie Informationen zu privaten Unternehmen.
Beispiel:
What is the employee count and funding history for Anthropic?Recherchieren Sie Finanzierungsrunden für Startups und Risikokapital.
Beispiel:
What was OpenAI's latest funding round size, valuation, and key investors?Recherchieren Sie M&A- und IPO-Transaktionen.
Beispiel:
What was the acquisition price when Microsoft acquired GitHub?Ein spezialisierter Datenbankagent zum Nachschlagen von Informationen zu Investoren.
Beispiel:
What is the latest investment criteria of Insight Partners?Ein spezialisierter Datenbankagent zur Analyse privater Schulden, Kreditnehmer und Kreditgeber.
Beispiel:
List all the debt activities from borrower American TowerExtrahieren Sie Daten von jeder öffentlichen Website.
Beispiel:
Extract property prices and square footage data from zillow.com/san-francisco-ca/Führen Sie umfassende Recherchen zu jedem Thema durch.
Beispiel:
Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins„Wie hoch waren die Umsatz- und Nettogewinnzahlen von Amazon im vierten Quartal 2023?“
„Analysieren Sie die Trends der F&E-Ausgaben von Tesla in den letzten drei Jahren.“
„Welche Prognosen gab der CEO von NVIDIA in der letzten Telefonkonferenz zu den Quartalsergebnissen hinsichtlich der Nachfrage nach KI-Chips ab?“
„Vergleichen Sie die Kurs-Gewinn-, Kurs-Umsatz- und EV/EBITDA-Verhältnisse der fünf größten Halbleiterunternehmen.“
„Wie groß war die letzte Finanzierungsrunde von Anthropic, wie hoch war ihre Bewertung und wer waren die Hauptinvestoren?“
„Extrahieren Sie alle Datenfelder von zillow.com/san-francisco-ca/“
„Untersuchen Sie die finanziellen Auswirkungen der Datenschutzänderungen von Apple auf die Einnahmen und Margen digitaler Werbeunternehmen.“
„Stellen Sie alle Schuldenaktivitäten des Kreditgebers ING Group im vierten Quartal 2024 zusammen.“
„Wie viele Investitionen hat Andreessen Horowitz in den letzten 12 Monaten in KI-Startups getätigt?“
Probleme mit dem API-Schlüssel : Stellen Sie sicher, dass Ihr Octagon-API-Schlüssel in der Umgebung oder Konfigurationsdatei richtig eingestellt ist.
Verbindungsprobleme : Stellen Sie sicher, dass die Verbindung zur Octagon-API ordnungsgemäß funktioniert.
Ratenbegrenzung : Wenn bei der Ratenbegrenzung Fehler auftreten, reduzieren Sie die Häufigkeit Ihrer Anfragen.
env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpMIT
⭐ Markieren Sie dieses Repo mit einem Stern, wenn Sie es hilfreich finden!
[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.
