MCPInstall

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
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads funda-data automatically when relevant.

  3. 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:

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

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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