SKILL.md

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 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 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 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:

FUNDA_API_KEY environment variable

FUNDA_API_KEY in .env in the current directory

FUNDA_API_KEY in .env at the git repo root (so a worktree inherits the key from the main checkout)

!`if [ -n "$FUNDA_API_KEY" ]; then echo "KEY_FROM_ENV_VAR"; elif [ -f .env ] &#x26;&#x26; grep -qE "^FUNDA_API_KEY=" .env; then echo "KEY_FROM_LOCAL_DOTENV:$(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 "$ROOT/.env" ] &#x26;&#x26; grep -qE "^FUNDA_API_KEY=" "$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

funda-data

SKILL.md

Funda AI Skill

Step 1: Decide Which Surface

Step 2: MCP Flow (Research)

2a. Verify the MCP is connected

2b. Frame the question

2c. Call the tool

Step 3: REST Flow (Raw Data)

3a. Resolve FUNDA_API_KEY

3b. Find the right endpoint

3c. Call the endpoint

Step 4: Respond to the User

Reference Files

Stop writing automation&scrapers

funda-data

SKILL.md

Funda AI Skill

Step 1: Decide Which Surface

Step 2: MCP Flow (Research)

2a. Verify the MCP is connected

2b. Frame the question

2c. Call the tool

Step 3: REST Flow (Raw Data)

3a. Resolve FUNDA_API_KEY

3b. Find the right endpoint

3c. Call the endpoint

Step 4: Respond to the User

Reference Files

Let your agent run on any real-world website

Related skills

Stop writing automation&scrapers