
funda-data
Query Funda AI financial data via two surfaces: the MCP server at https://funda.ai/api/mcp for analyst-grade research synthesis (DCF, comps, earnings previews/recaps, sector deep-dives, SEC filings, transcripts, supply-chain mapping, ownership flow, macro framing) via the agentchat tool — OR the REST API at https://api.funda.ai/v1 with FUNDAAPIKEY for raw data (real-time quotes, intraday candles, EOD prices, financial statements, options chains/greeks/GEX, supply-chain KG, social sentiment, news, calendars, FRED, ESG, congressional trades, AI hiring signals). Triggers: "funda", "funda.ai", real-time quote, stock price, intraday, balance sheet, income statement, options chain, DCF, comps, earnings preview/recap, analyst estimates, 10-K/10-Q/8-K, transcript, ownership flow, gamma exposure, supply chain, sector deep-dive, congressional trades, FRED. Prefer MCP for synthesis/analysis questions; use REST for raw structured data the MCP declines.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/himself65/finance-skills --skill funda-data- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads funda-data automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Funda 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
FAQ
What does the funda-data skill do?
Query Funda AI financial data via two surfaces: the MCP server at https://funda.ai/api/mcp for analyst-grade research synthesis (DCF, comps, earnings previews/recaps, sector deep-dives, SEC filings, transcripts, supply-chain mapping, ownership flow, macro framing) via the agentchat tool — OR the REST API at https://api.funda.ai/v1 with FUNDAAPIKEY for raw data (real-time quotes, intraday candles, EOD prices, financial statements, options chains/greeks/GEX, supply-chain KG, social sentiment, news, calendars, FRED, ESG, congressional trades, AI hiring signals). Triggers: "funda", "funda.ai", real-time quote, stock price, intraday, balance sheet, income statement, options chain, DCF, comps, earnings preview/recap, analyst estimates, 10-K/10-Q/8-K, transcript, ownership flow, gamma exposure, supply chain, sector deep-dive, congressional trades, FRED. Prefer MCP for synthesis/analysis questions; use REST for raw structured data the MCP declines.
How do I install funda-data?
Run npx skills add https://github.com/himself65/finance-skills --skill funda-data in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/funda-data/ (personal) or .claude/skills/funda-data/ (project).
Which AI agents can use funda-data?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is funda-data safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains funda-data?
It is published by himself65 in himself65/finance-skills. It has 1,159 recorded installs.
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