
Funda AI
FreeAccess comprehensive financial data and analysis.
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 source1. Install with the skills CLI
npx skills add himself65/finance-skills/funda-data --agent claude-code2. 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 himself65Funda AI Skill
Funda AI exposes two complementary surfaces backed by the same data:
| Surface | Best for | Auth | Output |
|---|---|---|---|
MCP agent_chat at https://funda.ai/api/mcp | Research, analysis, synthesis | OAuth (auto via claude mcp add) | Synthesized text with disclaimer |
REST /v1/* at https://api.funda.ai | Raw structured data | FUNDA_API_KEY Bearer | JSON |
Both require an active Funda AI subscription.
Step 1: Decide Which Surface
| User wants | Surface |
|---|---|
| DCF / comps walkthrough, sector view, transcript synthesis, company primer | MCP |
| Earnings preview/recap with judgment, beat-miss decomposition, narrative framing | MCP |
| Real-time or intraday quote, EOD price history | REST |
| Raw options chain snapshot, greeks, GEX time series | REST |
| Specific line item from a financial statement (single number, JSON) | REST |
| 13F filings, insider trades, congressional trades as rows | REST |
| News with structured sentiment / event timeline (JSON) | REST |
| Bulk dataset downloads | REST |
| 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 asmcp__funda__agent_chat. Continue. FUNDA_MCP_NOT_CONNECTED→ ask the user to install:
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.claude mcp add --transport http funda https://funda.ai/api/mcp
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 wants | Question 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 ishttps://funda.ai/agent-chat?c=<id>(the/agent-chatroute redirects to/agent-chat-v2?c=<id>)._meta["funda.io/timed_out"]—trueif 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:
FUNDA_API_KEYenvironment variableFUNDA_API_KEYin.envin the current directoryFUNDA_API_KEYin.envat 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_KEYdirectly 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 eitherexport FUNDA_API_KEY="..."or addFUNDA_API_KEY=...to.envat 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.
| Category | Endpoint family | Reference |
|---|---|---|
| Real-time / batch / aftermarket quotes | /v1/quotes?type=... | references/market-data.md |
| Historical EOD, intraday candles, technical indicators | /v1/stock-price, /v1/charts | references/market-data.md |
| Commodity / forex / crypto quotes | /v1/quotes?type=commodity-quotes | references/market-data.md |
| Income / balance / cash flow / metrics / ratios | /v1/financial-statements | references/fundamentals.md |
| Company profile, peers, shares float, search, screener, list | /v1/company-profile, /v1/company-details, /v1/search, /v1/companies | references/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/ownership | references/alternative-data.md |
| AI-enriched news + aggregated sentiment + event timeline | /v1/news/ticker, /v1/news/timeline, /v1/news/sentiment | references/news-enriched.md |
| SEC filings, earnings/podcast transcripts, research reports | /v1/sec-filings, /v1/transcripts, /v1/investment-research-reports | references/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/fred | references/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-hours | references/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/messages | references/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 framingagent_chatqueries.
REST path:
references/market-data.md— quotes, historical prices, charts, technical indicatorsreferences/fundamentals.md— financial statements, company profile/details, search/screener, analyst, companies listreferences/options.md— chains, greeks, GEX, flow, IV, screener, contract-level datareferences/supply-chain.md— supply-chain KG, relationships, graph traversalreferences/alternative-data.md— Twitter, Reddit, Polymarket, government trading, ownershipreferences/news-enriched.md— AI-enriched news, event timeline, aggregated sentimentreferences/filings-transcripts.md— SEC filings, earnings/podcast transcripts, research reportsreferences/calendar-economics.md— calendars, economics, treasury, FREDreferences/other-data.md— news, market performance, funds, ESG, COT, bulk, market hoursreferences/recruit.md— AI-company hiring signals, JD classifications, product clusters, launch probabilitiesreferences/claude-proxy.md— Claude API proxy via Bedrock
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