Octagon MCP サーバーは、Octagon Market Intelligence API と統合することで、AI を活用した専門的な金融リサーチと分析を提供します。これにより、ユーザーは、Claude Desktop やその他の一般的な MCP クライアント内で、公開書類、収益報告の記録、財務指標、株式市場データ、広範なプライベート市場取引から詳細な洞察を簡単に分析して抽出できます。
✅公開市場データに特化したAIエージェント
SEC提出書類の分析とデータ抽出（8,000社以上の上場企業の10-K、10-Q、8-K、20-F、S-1）
決算説明会の記録分析（過去10年間と現在）
財務指標と比率分析（過去10年間と現在）
株式市場データへのアクセス（10,000 以上のアクティブなティッカー、毎日の過去および現在のデータ）
✅プライベート市場データに特化したAIエージェント
民間企業調査（300万社以上）
資金調達ラウンドとベンチャーキャピタルの調査（50万件以上の取引）
M&AおよびIPO取引調査（200万件以上の取引）
債務取引調査（100万件以上の取引）
✅深い研究のための専門AIエージェント
Web スクレイピング機能 (json、csv、python スクリプト)
包括的なディープリサーチツール
Related MCP server: FundzWatch MCP Server
Octagon MCP を使用するには、次のことが必要です。
Octagonで無料アカウントを登録する
ログイン後、左側のメニューからAPIキーに移動します
新しいAPIキーを生成する
このAPIキーをOCTAGON_API_KEY値として設定で使用します。
Octagon MCP をインストールまたは実行する前に、システムにnpx (Node.js および npm に付属) がインストールされている必要があります。
Homebrew をインストールします(インストールされていない場合)。
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Node.js をインストールします (npm と npx を含む)。
brew install nodeこれにより、Node.js、npm、npx の最新バージョンがインストールされます。
インストールを確認します:
node -v
npm -v
npx -vNode.js インストーラーをダウンロードします。
https://nodejs.org/にアクセスし、Windows 用の LTS バージョンをダウンロードします。
インストーラーを実行し、プロンプトに従ってください。これにより、Node.js、npm、npxがインストールされます。
**インストールの確認:**コマンドプロンプトを開いて次のコマンドを実行します:
node -v
npm -v
npx -v3 つすべてのバージョン番号が表示されている場合は、以下のインストール手順に進む準備ができています。
Claude Desktop 用に Octagon MCP を構成するには:
クロードデスクトップを開く
設定 > 開発者 > 設定の編集に移動します
claude_desktop_config.jsonに以下を追加します ( your-octagon-api-key Octagon API キーに置き換えます)。
{
"mcpServers": {
"octagon-mcp-server": {
"command": "npx",
"args": ["-y", "octagon-mcp@latest"],
"env": {
"OCTAGON_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}変更を有効にするには、Claude を再起動してください。
Cursor Desktop の設定 🖥️ 注: Cursor バージョン 0.45.6 以降が必要です
カーソルで Octagon MCP を構成するには:
カーソル設定を開く
機能 > MCPサーバーへ移動
「+新しいMCPサーバーを追加」をクリックします
以下を入力してください:
名前: 「octagon-mcp」（またはお好みの名前）
タイプ:「コマンド」
コマンド: env OCTAGON_API_KEY=your-octagon-api-key npx -y octagon-mcp
Windows を使用していて問題が発生している場合は、
cmd /c "set OCTAGON_API_KEY=your-octagon-api-key && npx -y octagon-mcp"を試してください。
your-octagon-api-key Octagon API キーに置き換えます。
追加後、MCPサーバーリストを更新して新しいツールをご確認ください。Composer Agentは適切な場合にOctagon MCPを自動的に使用しますが、投資リサーチのニーズを明記することで、明示的にリクエストすることも可能です。Command+L（Mac）でComposerにアクセスし、送信ボタンの横にある「Agent」を選択して、クエリを入力してください。
これを./codeium/windsurf/model_config.jsonに追加します:
{
"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 エージェントの使用に関する包括的なドキュメントについては、次の公式ドキュメントをご覧ください: https://docs.octagonagents.com
ドキュメントには以下が含まれます。
詳細なAPIリファレンス
エージェント固有のクエリガイドライン
例と使用例
投資リサーチのベストプラクティス
各ツールは、自然言語クエリを受け入れる単一のpromptパラメータを使用します。プロンプトには関連する詳細情報をすべて含めてください。
SEC 提出書類から情報を抽出します。
例：
What was Apple's gross margin percentage from their latest 10-Q filing?収益報告の記録を分析します。
例：
What did NVIDIA's CEO say about AI chip demand in their latest earnings call?財務指標と比率を取得します。
例：
Calculate the price-to-earnings ratio for Tesla over the last 4 quarters株式市場データにアクセスします。
例：
How has Apple's stock performed compared to the S&P 500 over the last 6 months?民間企業の情報を調査します。
例：
What is the employee count and funding history for Anthropic?スタートアップの資金調達ラウンドとベンチャーキャピタルを調査します。
例：
What was OpenAI's latest funding round size, valuation, and key investors?M&AおよびIPO取引を調査します。
例：
What was the acquisition price when Microsoft acquired GitHub?投資家の情報を検索するための専門データベースエージェント。
例：
What is the latest investment criteria of Insight Partners?民間債務、借り手、貸し手を分析するための専門データベース エージェント。
例：
List all the debt activities from borrower American Tower任意の公開 Web サイトからデータを抽出します。
例：
Extract property prices and square footage data from zillow.com/san-francisco-ca/あらゆるトピックについて包括的な調査を実行します。
例：
Research the financial impact of Apple's privacy changes on digital advertising companies' revenue and margins「2023年第4四半期のAmazonの収益と純利益はいくらでしたか？」
「過去 3 年間のテスラの研究開発費の傾向を分析します。」
「NVIDIA の CEO は最新の決算説明会で AI チップの需要に関してどのような指針を示しましたか?」
「上位 5 社の半導体企業の株価収益率、株価売上高比率、EV/EBITDA 比率を比較します。」
「Anthropic の最新の資金調達ラウンドの規模、評価額、主要投資家はいくらでしたか？」
「zillow.com/san-francisco-ca/ からすべてのデータ フィールドを抽出します」
「Appleのプライバシー変更がデジタル広告会社の収益と利益に与える財務的影響を調査する」
「2024年第4四半期のINGグループのすべての債務活動をまとめる」
「アンドリーセン・ホロウィッツは過去12か月間にAIスタートアップにいくつの投資をしましたか？」
API キーの問題: Octagon API キーが環境または構成ファイルで正しく設定されていることを確認します。
接続の問題: Octagon API への接続が正しく機能していることを確認してください。
レート制限: レート制限エラーが発生した場合は、リクエストの頻度を減らしてください。
env OCTAGON_API_KEY=your_octagon_api_key npx -y octagon-mcpnpm install -g octagon-mcpマサチューセッツ工科大学
⭐ 役に立ったと思ったらこのリポジトリにスターを付けてください!
[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.
