Octagon MCP 서버는 Octagon Market Intelligence API와 통합하여 전문적인 AI 기반 재무 조사 및 분석을 제공합니다. 이를 통해 사용자는 Claude Desktop 및 기타 인기 있는 MCP 클라이언트에서 공개 제출, 수익 전화 회의록, 재무 지표, 주식 시장 데이터 및 광범위한 비공개 시장 거래에서 자세한 통찰력을 쉽게 분석하고 추출할 수 있습니다.
✅ 공공 시장 데이터를 위한 전문 AI 에이전트
SEC 제출 분석 및 데이터 추출(8,000개 이상의 상장 기업 10-K, 10-Q, 8-K, 20-F, S-1)
수익 전화 회의록 분석(10년간의 과거 및 현재)
재무 지표 및 비율 분석(10년간의 과거 및 현재)
주식 시장 데이터 접근(10,000개 이상의 활성 티커, 일일 과거 및 현재)
✅ 민간 시장 데이터를 위한 전문 AI 에이전트
민간 기업 연구(300만 개 이상의 기업)
펀딩 라운드 및 벤처 캐피털 리서치(50만 건 이상의 거래)
M&A 및 IPO 거래 조사(200만 건 이상)
부채 거래 조사(100만 건 이상 거래)
✅ 심층 연구 를 위한 전문 AI 에이전트
웹 스크래핑 기능(json, csv, python 스크립트)
포괄적인 심층 연구 도구
Related MCP server: FundzWatch MCP Server
Octagon MCP를 사용하려면 다음이 필요합니다.
Octagon 에서 무료 계정에 가입하세요
로그인 후 왼쪽 메뉴에서 API 키로 이동합니다.
새로운 API 키 생성
구성에서 이 API 키를 OCTAGON_API_KEY 값으로 사용하세요.
Octagon MCP를 설치하거나 실행하기 전에 시스템에 npx (Node.js 및 npm과 함께 제공)가 설치되어 있어야 합니다.
Homebrew를 설치하세요 (없으면):
지엑스피1
Node.js 설치(npm 및 npx 포함):
brew install node이렇게 하면 Node.js, npm, npx의 최신 버전이 설치됩니다.
설치 확인:
node -v
npm -v
npx -vNode.js 설치 프로그램을 다운로드하세요:
https://nodejs.org/ 로 가서 Windows용 LTS 버전을 다운로드하세요.
설치 프로그램을 실행 하고 안내를 따르세요. 그러면 Node.js, npm, npx가 설치됩니다.
설치 확인: 명령 프롬프트를 열고 다음을 실행합니다.
node -v
npm -v
npx -v세 가지 버전 번호가 모두 표시되면 아래 설치 단계를 진행할 준비가 된 것입니다.
Claude Desktop에 맞게 Octagon MCP를 구성하려면:
클로드 데스크톱 열기
설정 > 개발자 > 구성 편집으로 이동하세요.
claude_desktop_config.json 에 다음을 추가합니다( your-octagon-api-key Octagon API 키로 바꾸세요):
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}변경 사항을 적용하려면 Claude를 다시 시작하세요.
커서 데스크톱 구성 🖥️ 참고: 커서 버전 0.45.6+ 필요
Cursor에서 Octagon MCP를 구성하려면:
커서 설정 열기
기능 > MCP 서버로 이동하세요
"+ 새 MCP 서버 추가"를 클릭하세요
다음을 입력하세요.
이름: "octagon-mcp"(또는 원하는 이름)
유형: "명령"
명령어: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp
Windows를 사용 중이고 문제가 발생하는 경우
cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"시도해 보세요.
your-octagon-api-key Octagon API 키로 바꾸세요.
추가 후 MCP 서버 목록을 새로 고쳐 새로운 도구를 확인하세요. Composer Agent는 필요한 경우 Octagon MCP를 자동으로 사용하지만, 투자 리서치 요구 사항을 명시하여 명시적으로 요청할 수 있습니다. Command+L(Mac)을 눌러 Composer에 접속하고, 제출 버튼 옆의 "Agent"를 선택한 후 쿼리를 입력하세요.
./codeium/windsurf/model_config.json 에 다음을 추가하세요.
{
"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-mcpOctagon 에이전트 사용에 대한 포괄적인 설명서는 https://docs.octagonagents.com 에서 공식 설명서를 참조하세요.
문서에는 다음이 포함됩니다.
자세한 API 참조
에이전트별 쿼리 가이드라인
예시 및 사용 사례
투자 연구를 위한 모범 사례
각 도구는 자연어 쿼리를 허용하는 단일 prompt 매개변수를 사용합니다. 프롬프트에 모든 관련 세부 정보를 포함하세요.
SEC 제출 서류에서 정보를 추출합니다.
예:
What was Apple's gross margin percentage from their latest 10-Q filing?수익 전화 회의 내용을 분석합니다.
예:
What did NVIDIA's CEO say about AI chip demand in their latest earnings call?재무 지표와 비율을 검색합니다.
예:
Calculate the price-to-earnings ratio for Tesla over the last 4 quarters주식 시장 데이터에 접근하세요.
예:
How has Apple's stock performed compared to the S&P 500 over the last 6 months?비상장 기업 정보를 조사하세요.
예:
What is the employee count and funding history for Anthropic?스타트업 자금 조달 라운드와 벤처 캐피털을 조사하세요.
예:
What was OpenAI's latest funding round size, valuation, and key investors?M&A와 IPO 거래를 조사하세요.
예:
What was the acquisition price when Microsoft acquired GitHub?투자자에 대한 정보를 조회하는 전문 데이터베이스 에이전트입니다.
예:
What is the latest investment criteria of Insight Partners?개인 부채, 차용인, 대출인을 분석하는 전문 데이터베이스 에이전트입니다.
예:
List all the debt activities from borrower American Tower모든 공개 웹사이트에서 데이터를 추출합니다.
예:
Extract property prices and square footage data from zillow.com/san-francisco-ca/모든 주제에 대해 포괄적인 조사를 수행합니다.
예:
Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins"아마존의 2023년 4분기 매출과 순이익은 얼마였나요?"
"지난 3년간 테슬라의 R&D 지출 추세를 분석하세요."
"NVIDIA CEO는 최근 실적 발표에서 AI 칩 수요와 관련해 어떤 지침을 제공했습니까?"
"상위 5개 반도체 기업의 주가수익비율, 주가매출비율, EV/EBITDA 비율을 비교해보세요."
"Anthropic의 최근 자금 조달 라운드 규모, 가치 평가 및 주요 투자자는 무엇입니까?"
"zillow.com/san-francisco-ca/에서 모든 데이터 필드를 추출합니다."
"Apple의 개인정보 보호 정책 변경이 디지털 광고 회사의 수익과 마진에 미치는 재정적 영향을 조사하세요."
"2024년 4분기 ING 그룹의 모든 부채 활동을 정리하세요"
"Andreessen Horowitz는 지난 12개월 동안 AI 스타트업에 얼마나 많은 투자를 했나요?"
API 키 문제 : Octagon API 키가 환경이나 구성 파일에 올바르게 설정되어 있는지 확인하세요.
연결 문제 : Octagon API에 대한 연결이 제대로 작동하는지 확인하세요.
속도 제한 : 속도 제한 오류가 발생하면 요청 빈도를 줄이세요.
env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpMIT
⭐ 도움이 된다면 이 저장소에 별표를 표시해 주세요!
[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.
