{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-octagon","slug":"octagon","name":"Octagon","type":"mcp","url":"https://github.com/OctagonAI/octagon-mcp-server","page_url":"https://unfragile.ai/octagon","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-octagon__cap_0","uri":"capability://tool.use.integration.real.time.investment.data.streaming.via.mcp.protocol","name":"real-time investment data streaming via mcp protocol","description":"Streams live market data, company fundamentals, and investment metrics through the Model Context Protocol (MCP) interface, enabling LLM agents and applications to access current financial information without polling. Implements MCP resource handlers that expose financial datasets as queryable endpoints, allowing Claude and other MCP-compatible clients to request specific securities, sectors, or market conditions with structured JSON responses.","intents":["I need my AI agent to access real-time stock prices and market data without building custom API integrations","I want to build an investment research chatbot that can pull live company fundamentals on demand","I need to stream market data into my LLM application without managing separate data pipelines"],"best_for":["AI engineers building investment research agents","FinTech teams integrating LLMs with market data","Developers prototyping financial advisory chatbots"],"limitations":["MCP protocol adds ~100-200ms latency per request due to serialization overhead","Real-time data freshness depends on upstream data provider update frequency","No built-in caching layer — repeated queries to same security hit the data provider each time","Limited to data sources integrated into Octagon's backend; custom data sources require code modification"],"requires":["MCP-compatible client (Claude Desktop, custom MCP host)","Network connectivity to Octagon MCP server","API credentials for underlying financial data providers (if required by Octagon's architecture)"],"input_types":["text queries (ticker symbols, company names, sector filters)","structured parameters (date ranges, metric types, market segments)"],"output_types":["JSON-structured market data","time-series price data","company fundamentals (earnings, revenue, ratios)","sector and index information"],"categories":["tool-use-integration","search-retrieval","real-time-data"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_1","uri":"capability://data.processing.analysis.private.market.data.aggregation.and.normalization","name":"private market data aggregation and normalization","description":"Aggregates and normalizes private market data (venture capital, private equity, M&A) from multiple sources into a unified schema, exposing it through MCP endpoints. Implements data transformation pipelines that reconcile different data formats, handle missing fields, and standardize company identifiers across private market databases, enabling consistent querying across fragmented data sources.","intents":["I need to research private companies and their funding history in a structured way","I want my investment research agent to access both public and private market data seamlessly","I need to normalize company data from multiple private market sources for comparison"],"best_for":["Venture capital and private equity professionals using AI research tools","M&A advisory teams building AI-assisted deal sourcing","Startup founders researching competitors and market landscape"],"limitations":["Private market data is inherently incomplete and delayed — funding announcements lag actual events by weeks or months","Data quality varies significantly across sources; some private companies have sparse or outdated information","Normalization heuristics may incorrectly merge or split company records when identifiers conflict","Access to premium private market datasets may require separate licensing agreements"],"requires":["MCP-compatible client","Potential API keys or credentials for private market data providers integrated into Octagon","Network access to Octagon MCP server"],"input_types":["company names or identifiers","funding round filters","investor or sector queries"],"output_types":["normalized company profiles","funding round history with standardized fields","investor and cap table information","acquisition and exit data"],"categories":["data-processing-analysis","search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_2","uri":"capability://search.retrieval.public.market.securities.and.fundamentals.lookup","name":"public market securities and fundamentals lookup","description":"Provides structured access to public market data including stock prices, financial statements, earnings reports, and valuation metrics through MCP tool and resource endpoints. Queries underlying financial data APIs (likely SEC EDGAR, Bloomberg, or similar) and returns normalized JSON responses with standardized field names, enabling LLM agents to retrieve company fundamentals without parsing HTML or handling API authentication.","intents":["I need my chatbot to look up a company's latest earnings and revenue figures","I want to retrieve historical stock prices and calculate returns for a given period","I need to access SEC filings and financial ratios for fundamental analysis"],"best_for":["Financial analysts building AI-assisted research workflows","Robo-advisor platforms integrating LLM-based portfolio analysis","Educational tools teaching investment analysis with AI tutors"],"limitations":["Financial data has regulatory reporting delays — quarterly earnings are typically 30-45 days after quarter end","Stock prices are delayed by 15-20 minutes in free tier; real-time quotes may require premium data subscriptions","Historical data availability varies by security; delisted companies or recent IPOs have limited history","Fundamentals are point-in-time snapshots; no built-in time-series aggregation for trend analysis"],"requires":["MCP-compatible client","Valid ticker symbols or CIK identifiers for SEC lookups","Potential API keys for premium financial data providers"],"input_types":["ticker symbols (e.g., AAPL, MSFT)","CIK numbers for SEC filings","date ranges for historical queries","metric types (P/E ratio, debt-to-equity, etc.)"],"output_types":["current and historical stock prices","quarterly and annual financial statements","valuation metrics and ratios","earnings per share and dividend data","SEC filing metadata and links"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_3","uri":"capability://search.retrieval.sector.and.market.index.aggregation","name":"sector and market index aggregation","description":"Aggregates sector-level and broad market index data (S&P 500, Nasdaq, industry indices) through MCP endpoints, enabling queries for sector performance, composition, and comparative analysis. Implements index calculation and weighting logic, returning normalized sector metrics and constituent information that allows LLM agents to understand market structure and relative performance without manual index construction.","intents":["I want my research agent to understand which sectors are outperforming the market","I need to retrieve the composition of an index and analyze its top holdings","I want to compare sector valuations and identify relative opportunities"],"best_for":["Quantitative research teams building AI-assisted portfolio analysis","Market commentary and research automation platforms","Educational platforms teaching market structure and sector analysis"],"limitations":["Index composition changes are delayed by 1-2 days after official announcements","Sector classification varies by provider (GICS vs ICB); Octagon may use a single standard","Intra-day index calculations may lag real-time market movements by 5-15 minutes","Historical sector data is limited to available index history; some newer sectors have sparse data"],"requires":["MCP-compatible client","Index identifiers or sector codes","Network access to Octagon MCP server"],"input_types":["index names or symbols (S&P 500, Nasdaq-100, etc.)","sector codes or names","date ranges for historical analysis"],"output_types":["index composition and weighting","sector performance metrics","constituent company lists with weights","historical index values and returns"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_4","uri":"capability://planning.reasoning.investment.thesis.and.research.document.generation","name":"investment thesis and research document generation","description":"Leverages LLM reasoning capabilities through MCP to synthesize investment theses by combining real-time market data, fundamentals, and private market information into structured research narratives. The MCP server provides data access primitives that Claude or other LLMs use to build multi-step reasoning chains, generating investment recommendations with cited data sources and risk assessments without requiring pre-built templates.","intents":["I want to generate an investment thesis for a company by combining public and private market data","I need to create a comparative analysis between competitors using real-time fundamentals","I want to build a research report that cites specific data points and sources"],"best_for":["Investment research teams automating preliminary analysis and thesis generation","Wealth management platforms generating personalized investment narratives","Venture capital firms synthesizing market and company research at scale"],"limitations":["Generated theses reflect LLM reasoning quality and training data biases; not suitable as sole basis for investment decisions","LLM hallucinations may cite non-existent metrics or misinterpret data relationships","Thesis generation requires multiple MCP calls, adding 500ms-2s latency per analysis","No built-in compliance or regulatory review; generated content requires human review before publication"],"requires":["MCP-compatible LLM client (Claude, etc.)","Octagon MCP server with data access capabilities","Sufficient LLM context window to hold thesis generation prompts and data"],"input_types":["company identifiers or ticker symbols","analysis scope (competitive landscape, market opportunity, etc.)","optional constraints or focus areas"],"output_types":["structured investment thesis with sections (opportunity, risks, valuation)","cited data sources with specific metrics","recommendation and conviction level","risk assessment and sensitivity analysis"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_5","uri":"capability://data.processing.analysis.portfolio.analysis.and.performance.attribution","name":"portfolio analysis and performance attribution","description":"Provides MCP tools for analyzing portfolio composition, calculating performance metrics, and attributing returns to specific holdings or factors. Implements portfolio weighting calculations, return aggregation, and risk metrics (volatility, Sharpe ratio, drawdown) by querying underlying security data and combining it with portfolio position data, enabling LLM agents to perform portfolio analysis without requiring external portfolio management systems.","intents":["I want to analyze a portfolio's performance and understand which holdings drove returns","I need to calculate risk metrics for a portfolio and identify concentration risks","I want to compare my portfolio's performance against relevant benchmarks"],"best_for":["Robo-advisor platforms analyzing client portfolios with AI","Wealth managers automating portfolio review and rebalancing analysis","Individual investors using AI to understand their portfolio performance"],"limitations":["Portfolio analysis requires position-level data; Octagon cannot access user portfolios directly — must be provided as input","Performance attribution is simplified; does not account for complex derivatives or options strategies","Risk metrics are backward-looking; volatility and Sharpe ratios based on historical data may not predict future risk","Benchmark selection is manual; no automatic benchmark matching based on portfolio characteristics"],"requires":["MCP-compatible client","Portfolio position data (holdings, quantities, purchase prices)","Octagon MCP server with security pricing and metrics data"],"input_types":["portfolio holdings (ticker, quantity, cost basis)","date range for performance analysis","optional benchmark identifiers for comparison"],"output_types":["portfolio-level returns and performance metrics","holding-level contribution to portfolio return","risk metrics (volatility, Sharpe ratio, max drawdown)","benchmark comparison and alpha/beta analysis","concentration and diversification metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_6","uri":"capability://search.retrieval.earnings.call.transcript.search.and.analysis","name":"earnings call transcript search and analysis","description":"Indexes and enables semantic search over earnings call transcripts through MCP, allowing LLM agents to retrieve relevant excerpts and perform textual analysis without downloading or parsing raw transcript files. Implements transcript storage with embeddings-based search, returning matched segments with speaker attribution and timestamp context, enabling agents to extract management guidance, Q&A insights, and sentiment signals from earnings calls.","intents":["I want to search earnings transcripts for mentions of specific topics or competitors","I need to extract management guidance and forward-looking statements from earnings calls","I want to analyze sentiment and tone in earnings calls to gauge management confidence"],"best_for":["Equity research teams automating earnings call analysis","Hedge funds extracting alpha signals from earnings transcripts","Investor relations platforms analyzing competitor earnings calls"],"limitations":["Transcript availability is limited to companies that hold earnings calls; private companies typically not covered","Semantic search may return false positives for ambiguous queries; requires LLM interpretation","Transcript indexing lags earnings call dates by 1-3 days","Speaker attribution may be incomplete or incorrect for some transcripts","Sentiment analysis is LLM-based and subject to interpretation bias"],"requires":["MCP-compatible client","Octagon MCP server with transcript index and embeddings","Query terms or semantic descriptions for search"],"input_types":["company identifiers or ticker symbols","date ranges for transcript filtering","semantic search queries (e.g., 'management concerns about competition')","optional speaker filters (CEO, CFO, etc.)"],"output_types":["transcript excerpts with speaker and timestamp","full transcript text","sentiment and tone analysis","extracted management guidance and forward-looking statements","Q&A segments with questioner attribution"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_7","uri":"capability://search.retrieval.news.and.sentiment.aggregation.for.securities","name":"news and sentiment aggregation for securities","description":"Aggregates financial news, social media sentiment, and analyst commentary for securities through MCP endpoints, providing LLM agents with access to recent news, sentiment scores, and market commentary without requiring separate news API integrations. Implements news source aggregation and sentiment scoring (likely using pre-trained models), returning normalized news items with sentiment labels and source credibility indicators.","intents":["I want my research agent to understand recent news and sentiment around a company","I need to identify sentiment shifts that might signal trading opportunities","I want to track analyst upgrades/downgrades and major news events for a security"],"best_for":["Trading platforms integrating sentiment signals into decision-making","News aggregation and monitoring platforms for investors","Sentiment-driven quantitative strategies and backtesting"],"limitations":["Sentiment scoring is model-based and may misclassify sarcasm, context-dependent statements, or domain-specific language","News aggregation has inherent bias toward major news sources; smaller or regional news may be missed","Sentiment data is backward-looking; past sentiment may not predict future price movements","Real-time sentiment requires continuous updates; historical sentiment data may be sparse for less-covered securities","Social media sentiment is noisy and subject to manipulation; requires careful interpretation"],"requires":["MCP-compatible client","Octagon MCP server with news and sentiment data","Security identifiers (ticker, company name)"],"input_types":["ticker symbols or company names","date ranges for news filtering","optional sentiment thresholds or source filters","news categories or topics"],"output_types":["recent news items with headlines and summaries","sentiment scores (positive/negative/neutral)","source attribution and credibility indicators","analyst ratings and recommendation changes","social media sentiment aggregates","news impact or relevance scoring"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-octagon__cap_8","uri":"capability://data.processing.analysis.comparable.company.analysis.and.valuation.multiples","name":"comparable company analysis and valuation multiples","description":"Enables peer group analysis by retrieving comparable company metrics and valuation multiples through MCP, allowing LLM agents to construct peer groups, calculate median multiples, and perform relative valuation analysis without manual data compilation. Implements peer selection logic (by sector, size, growth profile) and multiple calculations (EV/EBITDA, P/E, Price/Sales), returning normalized multiples with outlier detection and context for interpretation.","intents":["I want to find comparable companies and calculate median valuation multiples for peer analysis","I need to understand how a company's valuation compares to its peers","I want to identify valuation outliers and understand the drivers of valuation differences"],"best_for":["Investment banking teams performing valuation analysis","Private equity firms evaluating acquisition targets","Equity research teams building valuation models"],"limitations":["Peer group selection is heuristic-based; may include non-comparable companies or miss true peers","Valuation multiples are point-in-time snapshots; historical multiple trends require separate queries","Outlier detection is statistical; extreme but justified valuations may be flagged as anomalies","Multiple calculations assume clean financial data; special items or one-time charges may distort multiples","Peer groups for niche industries or early-stage companies may be too small for reliable analysis"],"requires":["MCP-compatible client","Target company identifier (ticker or company name)","Octagon MCP server with comparable company data and fundamentals"],"input_types":["target company identifier","peer selection criteria (sector, size, growth rate, geography)","valuation multiple types (EV/EBITDA, P/E, etc.)","optional date for historical analysis"],"output_types":["peer group composition with company identifiers","valuation multiples for each peer","median, mean, and quartile multiples","outlier identification with context","implied valuation for target company based on multiples","multiple drivers and sensitivity analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"moderate","permissions":["MCP-compatible client (Claude Desktop, custom MCP host)","Network connectivity to Octagon MCP server","API credentials for underlying financial data providers (if required by Octagon's architecture)","MCP-compatible client","Potential API keys or credentials for private market data providers integrated into Octagon","Network access to Octagon MCP server","Valid ticker symbols or CIK identifiers for SEC lookups","Potential API keys for premium financial data providers","Index identifiers or sector codes","MCP-compatible LLM client (Claude, etc.)"],"failure_modes":["MCP protocol adds ~100-200ms latency per request due to serialization overhead","Real-time data freshness depends on upstream data provider update frequency","No built-in caching layer — repeated queries to same security hit the data provider each time","Limited to data sources integrated into Octagon's backend; custom data sources require code modification","Private market data is inherently incomplete and delayed — funding announcements lag actual events by weeks or months","Data quality varies significantly across sources; some private companies have sparse or outdated information","Normalization heuristics may incorrectly merge or split company records when identifiers conflict","Access to premium private market datasets may require separate licensing agreements","Financial data has regulatory reporting delays — quarterly earnings are typically 30-45 days after quarter end","Stock prices are delayed by 15-20 minutes in free tier; real-time quotes may require premium data subscriptions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=octagon","compare_url":"https://unfragile.ai/compare?artifact=octagon"}},"signature":"APp0jshzRC49INUZfQ/7H9hBuzEe4ELTD+QcmsGxJK+AApc39G51OgNhH6moDVP/iuI9+yiBH/PYkpIpWRHCBQ==","signedAt":"2026-06-20T17:46:12.833Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/octagon","artifact":"https://unfragile.ai/octagon","verify":"https://unfragile.ai/api/v1/verify?slug=octagon","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}