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Funda AI

Free

Access comprehensive financial data and analysis.

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Free · Opens the source repo

What Funda AI does

Funda AI provides a dual interface for querying financial data, catering to both detailed research and raw data needs. The skill leverages the MCP (Multi-Channel Protocol) server for in-depth analysis and synthesis, while also offering a REST API for accessing structured financial data. Users can utilize the MCP for tasks such as discounted cash flow (DCF) analyses, earnings previews, and sector deep-dives, enabling them to generate comprehensive reports and insights. The REST API, on the other hand, is ideal for obtaining real-time stock quotes, historical price data, and specific financial statement line items in a machine-readable format.

This skill is particularly useful for financial analysts, researchers, and developers who require quick access to both qualitative and quantitative data. The MCP surface excels in providing synthesized information, making it suitable for users looking to understand complex financial narratives or trends. The REST API is designed for those who need raw data for further processing or integration into applications, allowing for flexibility in how financial data is consumed and utilized.

Both surfaces require an active Funda AI subscription, ensuring that users have access to the latest financial data and insights. The skill is structured to guide users in selecting the appropriate surface based on their needs, with clear instructions on how to frame questions for optimal results. Whether you need a high-level overview or detailed data points, Funda AI streamlines the process of financial analysis, making it an essential tool for professionals in finance and investment.

When to use it

Use Funda AI when you need comprehensive financial analysis or real-time data for stocks and market trends.

When not to use it

This skill is not suitable for personalized investment advice or trade execution, as it focuses on data retrieval and analysis.

What you can build with it

Earnings Recap Analysis

Use the MCP to generate a detailed recap of a company's earnings report, including beat/miss analysis and key insights.

Real-Time Market Data

Utilize the REST API to fetch real-time stock quotes and historical price data for analysis in your applications.

Sector Deep-Dive Research

Leverage the MCP for a comprehensive sector analysis, summarizing trends and key players within the market.

How to install Funda AI

View source

1. Install with the skills CLI

npx skills add himself65/finance-skills/funda-data --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by himself65

Funda AI Skill

Funda AI exposes two complementary surfaces backed by the same data:

SurfaceBest forAuthOutput
MCP agent_chat at https://funda.ai/api/mcpResearch, analysis, synthesisOAuth (auto via claude mcp add)Synthesized text with disclaimer
REST /v1/* at https://api.funda.aiRaw structured dataFUNDA_API_KEY BearerJSON

Both require an active Funda AI subscription.


Step 1: Decide Which Surface

User wantsSurface
DCF / comps walkthrough, sector view, transcript synthesis, company primerMCP
Earnings preview/recap with judgment, beat-miss decomposition, narrative framingMCP
Real-time or intraday quote, EOD price historyREST
Raw options chain snapshot, greeks, GEX time seriesREST
Specific line item from a financial statement (single number, JSON)REST
13F filings, insider trades, congressional trades as rowsREST
News with structured sentiment / event timeline (JSON)REST
Bulk dataset downloadsREST
AI-company hiring signals (OpenAI, Anthropic, Google, xAI)REST

Default to MCP for ambiguous research-style questions. Use REST when the user wants machine-readable structured data — or when the MCP refuses (real-time prices, raw quotes).

The MCP also refuses buy/sell calls, price targets, personalized portfolio advice, tax/legal advice, and trade execution. Those are out of scope for both surfaces — decline politely and don't fall through to REST hoping for a different answer.


Step 2: MCP Flow (Research)

2a. Verify the MCP is connected

!`claude mcp list 2>/dev/null | grep -iE "^funda:" || echo "FUNDA_MCP_NOT_CONNECTED"`
  • A line starting with funda: → registered. The tool is callable as mcp__funda__agent_chat. Continue.
  • FUNDA_MCP_NOT_CONNECTED → ask the user to install:
    claude mcp add --transport http funda https://funda.ai/api/mcp
    
    A browser tab opens for OAuth approval (1-hour token + 30-day refresh, auto-managed). The Claude Code session may need to be restarted before the tool registers.

2b. Frame the question

agent_chat is a fresh research turn with no cross-call memory — bake the ticker, time horizon, and assumptions into the question text itself.

User wantsQuestion shape
Earnings preview"Preview MSFT's Q3 print Thursday — segment trends, where consensus is aggressive/conservative, beat/miss pattern."
Earnings recap"Walk through NVDA Q2: beat/miss by segment, guide vs consensus, transcript Q&A on data-center demand."
Sector deep-dive"Summarize the 2026 hyperscaler capex cycle — spending tiers by name, supplier exposure, gross-margin implications."
Supply chain"Map TSMC's customer concentration and N2 ramp risks — top three exposures by revenue."
Filing summary"Diff the new risk factors in PLTR's latest 10-K versus the prior year."
DCF"Walk through a DCF for NVDA assuming 25% data-center growth, 10% terminal margin, 9% WACC — surface the sensitivity table."
Macro"Where in the Dalio long-term debt cycle is the US, and what does that imply for duration positioning?"
Ownership"Has institutional ownership of CRWD shifted in the latest 13F filings — net buyers vs sellers?"

If the user gave only a ticker, ask one clarifying question to scope the turn (preview? recap? primer? DCF?) before calling — vague questions burn a turn and return vague answers.

If the user is following up on a prior Funda response, quote the relevant paragraph back inside the new question; the agent has no memory of prior calls.

For more example questions per topic, see references/research-topics.md.

2c. Call the tool

mcp__funda__agent_chat(question: "<full research question>")

Typical run is 15–60 seconds; the server streams progress notifications throughout, so the client doesn't time out.

Response shape:

  • content[0].text — answer prefixed with [Funda research output — fundamental analysis, informational only…]. Keep the prefix.
  • _meta["funda.io/conversation_id"] — UUID. The in-app history page is https://funda.ai/agent-chat?c=<id> (the /agent-chat route redirects to /agent-chat-v2?c=<id>).
  • _meta["funda.io/timed_out"]true if the agent hit its run budget. Answer is partial; offer to retry with a tighter scope.

If the call returns 403 subscription_required, the MCP is registered but the account isn't subscribed — direct the user to https://funda.ai to activate.

Each call costs a research turn. Don't speculatively re-call with a rephrased question if the first answer was reasonable.


Step 3: REST Flow (Raw Data)

3a. Resolve FUNDA_API_KEY

The skill resolves FUNDA_API_KEY in this order:

  1. FUNDA_API_KEY environment variable
  2. FUNDA_API_KEY in .env in the current directory
  3. FUNDA_API_KEY in .env at the git repo root (so a worktree inherits the key from the main checkout)
!`if [ -n "$FUNDA_API_KEY" ]; then echo "KEY_FROM_ENV_VAR"; elif [ -f .env ] && grep -qE "^FUNDA_API_KEY=" .env; then echo "KEY_FROM_LOCAL_DOTENV:$(pwd)/.env"; else GIT_COMMON=$(git rev-parse --path-format=absolute --git-common-dir 2>/dev/null); if [ -n "$GIT_COMMON" ]; then ROOT=$(dirname "$GIT_COMMON"); if [ -f "$ROOT/.env" ] && grep -qE "^FUNDA_API_KEY=" "$ROOT/.env"; then echo "KEY_FROM_ROOT_DOTENV:$ROOT/.env"; else echo "KEY_NOT_SET"; fi; else echo "KEY_NOT_SET"; fi; fi`

Then act on the result:

  • KEY_FROM_ENV_VAR — use $FUNDA_API_KEY directly in curl calls.
  • KEY_FROM_LOCAL_DOTENV:<path> / KEY_FROM_ROOT_DOTENV:<path> — load once before calling:
    export FUNDA_API_KEY=$(grep -E "^FUNDA_API_KEY=" <path> | head -1 | cut -d= -f2- | sed 's/^["'\'']//;s/["'\'']$//')
    
  • KEY_NOT_SET — ask the user for their key. They can either export FUNDA_API_KEY="..." or add FUNDA_API_KEY=... to .env at the repo root (preferred for worktrees).

3b. Find the right endpoint

Match the user's request to a category and read the corresponding reference file for full parameters and response schemas.

CategoryEndpoint familyReference
Real-time / batch / aftermarket quotes/v1/quotes?type=...references/market-data.md
Historical EOD, intraday candles, technical indicators/v1/stock-price, /v1/chartsreferences/market-data.md
Commodity / forex / crypto quotes/v1/quotes?type=commodity-quotesreferences/market-data.md
Income / balance / cash flow / metrics / ratios/v1/financial-statementsreferences/fundamentals.md
Company profile, peers, shares float, search, screener, list/v1/company-profile, /v1/company-details, /v1/search, /v1/companiesreferences/fundamentals.md
Analyst estimates, price targets, grades, DCF, ratings/v1/analyst?type=...references/fundamentals.md
Options chain, greeks, GEX, IV, max pain, flow, screener/v1/options/...references/options.md
Supply-chain KG: suppliers, customers, competitors, partners/v1/supply-chain/...references/supply-chain.md
Twitter, Reddit, Polymarket, government trading, ownership/v1/twitter-posts, /v1/reddit-posts, /v1/polymarket/..., /v1/government-trading, /v1/ownershipreferences/alternative-data.md
AI-enriched news + aggregated sentiment + event timeline/v1/news/ticker, /v1/news/timeline, /v1/news/sentimentreferences/news-enriched.md
SEC filings, earnings/podcast transcripts, research reports/v1/sec-filings, /v1/transcripts, /v1/investment-research-reportsreferences/filings-transcripts.md
Earnings / dividend / IPO / splits / economic calendar/v1/calendar?type=...references/calendar-economics.md
Treasury rates, GDP/CPI indicators, FRED, risk premium/v1/economics, /v1/fredreferences/calendar-economics.md
Stock news, gainers/losers, ETF holdings, ESG, COT, bulk, market hours/v1/news, /v1/market-performance, /v1/funds, /v1/esg, /v1/cot-report, /v1/bulk, /v1/market-hoursreferences/other-data.md
AI-company hiring signals (OpenAI, Anthropic, Google, xAI, Mercor, SurgeAI)/v1/recruit-...references/recruit.md
Claude API proxy via Bedrock/v1/claude/v1/messagesreferences/claude-proxy.md

3c. Call the endpoint

curl -s -H "Authorization: Bearer $FUNDA_API_KEY" \
  "https://api.funda.ai/v1/<endpoint>?<params>" | python3 -m json.tool

All responses are {"code": "0", "message": "", "data": ...}. A non-zero code is an error — read message.

List endpoints paginate: {"items": [...], "page": 0, "page_size": 20, "next_page": 1, "total_count": N}. Pages are 0-based; next_page is -1 when exhausted.

For broad ticker overviews ("tell me about AAPL"), combine a few REST calls: /v1/company-profile for sector/CEO/mcap/price + /v1/financial-statements?type=key-metrics-ttm + /v1/analyst?type=price-target-summary.


Step 4: Respond to the User

  • For MCP synthesis: surface with structure (tables, bullets, headings) — don't dump the raw blob. Preserve the Funda disclaimer; never repackage analysis as a recommendation, price target, or trade signal.
  • For MCP responses, cite https://funda.ai/agent-chat?c={conversation_id} so the user can inspect the agent's full timeline.
  • For REST responses, format numbers cleanly (prices to 2 decimals, ratios to 2-4, large numbers with commas or abbreviations like $2.8T). Use tables for comparative data; summarize trends rather than dumping time series.
  • For DCF / valuation work, surface the assumptions Funda used so the user can adjust them.
  • Note the source: "Funda AI" (whether MCP or REST).
  • Never provide trading recommendations — present the data and let the user draw conclusions.

Reference Files

MCP path:

  • references/research-topics.md — categorized example questions and tips for framing agent_chat queries.

REST path:

  • references/market-data.md — quotes, historical prices, charts, technical indicators
  • references/fundamentals.md — financial statements, company profile/details, search/screener, analyst, companies list
  • references/options.md — chains, greeks, GEX, flow, IV, screener, contract-level data
  • references/supply-chain.md — supply-chain KG, relationships, graph traversal
  • references/alternative-data.md — Twitter, Reddit, Polymarket, government trading, ownership
  • references/news-enriched.md — AI-enriched news, event timeline, aggregated sentiment
  • references/filings-transcripts.md — SEC filings, earnings/podcast transcripts, research reports
  • references/calendar-economics.md — calendars, economics, treasury, FRED
  • references/other-data.md — news, market performance, funds, ESG, COT, bulk, market hours
  • references/recruit.md — AI-company hiring signals, JD classifications, product clusters, launch probabilities
  • references/claude-proxy.md — Claude API proxy via Bedrock

Frequently asked questions about Funda AI

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