funda-data

name: funda-data description: > 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 agent_chat tool — OR the REST API at https://api.funda.ai/v1 with FUNDA_API_KEY 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.

Funda AI Skill

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

Both require an active Funda AI subscription.

Step 1: Decide Which Surface

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.

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.

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