bigdata-skill

name

bigdata-skill

description

Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.

Bigdata.com SDK + REST Toolkit

Get the structured substrate the Bigdata.com MCP server doesn't hand over. The MCP returns clean prose and pre-synthesized tearsheets, but its search tool gives chunks with no per-chunk sentiment or entity spans, and its tearsheets give aggregate values — not the fiscal-period time series, universe screener, or per-field JSON you'd build a pipeline on. The official bigdata-client SDK plus a thin REST passthrough over the same backend, same JWT reach the official /v1/* endpoints that hold it. This skill bundles a toolkit that does exactly that — already debugged, already cost-guarded — so you don't re-pay the discovery cost.

The core problem this solves (read this first)

The Bigdata MCP server answers "what's the sentiment around NVIDIA?" with a readable paragraph or a pre-synthesized tearsheet — genuinely useful for a chat turn. But the moment you need the machine-readable substrate to build a pipeline on, the MCP doesn't hand it over:

• its search tool returns chunks with text + relevance only — no per-chunk sentiment number, no entity character spans;
• its tearsheets give aggregate values (a single sentiment score, a summary of estimates) — not a fiscal-period time series you can compute on, a universe screener, or per-field JSON.

The fix is a general pattern, not a Bigdata trick:

When an MCP data source returns only synthesized output but you need the structured fields underneath, drop to the vendor SDK or REST. MCP optimizes for a chat turn, not a pipeline.

Crucially, for Bigdata these structured fields are official, publicly documented REST endpoints (docs.bigdata.com/api-reference/...), not a hidden backend — and Bigdata is sunsetting the SDK (EOL 2026-12-31) in favour of this REST API, so the REST layer here is the forward-compatible path, not a hack. The SDK (bigdata_client.Bigdata) covers search + knowledge-graph; bd._api.http reaches every /v1/* endpoint the SDK never wrapped. The bundled bigdata_toolkit packages both behind one BigdataClient.

When to use this skill

Trigger on any of these, in any language:

• The user is using Bigdata.com / RavenPack and the MCP result feels thin — "where's the sentiment score?", "I need entity-level data", "the calendar".
• They want forward / structured financials for a ticker: analyst estimates, earnings or event calendar, earnings surprise, analyst ratings, price targets, a company screener / universe.
• They want annotated news chunks with numeric sentiment + entity spans, or a sentiment time series / co-mention graph.
• They mention a bd_v2_ API key, rp_entity_id, query_unit / chunk cost, bigdata-client, or "the bigdata MCP isn't enough".
• They're building an investment-research dataset and need a reusable, cost-aware data-pull layer rather than one-off MCP calls.

Setup (one time)

1 — API key (never hardcode it). The client fail-fasts if it's missing:

export BIGDATA_API_KEY=bd_v2_xxxxxxxx

2 — An isolated Python env with the official SDK. The bundled toolkit imports bigdata_client; install it once:

uv venv .venv --python 3.12
uv pip install --python .venv/bin/python bigdata-client
# Behind a slow/blocked PyPI (e.g. mainland China) add a mirror, and unset any
# outbound proxy for the install step so uv reaches the index directly:
#   --index-url https://pypi.tuna.tsinghua.edu.cn/simple

3 — Outbound proxy (only if your network needs one to reach api.bigdata.com). Two equivalent options — the official SDK accepts both: an env var, or BigdataClient(proxy=...) in code. The env var is simplest:

export HTTPS_PROXY=http://<host>:<port>     # plus WSS_PROXY for chat/WebSocket

If a proxy does TLS interception (self-signed CA) and you hit SSL handshake errors, the official fix is BigdataClient(verify_ssl="<proxy-CA>.pem") — not blind retries.

4 — Make the bundled package importable by putting this skill's scripts/ on PYTHONPATH (or sys.path.insert(0, "<this-skill>/scripts")).

Smoke-test the whole path (entity resolve + quota are free; --with-search adds one ~1 query_unit chunk search):

BIGDATA_API_KEY=bd_v2_xxx PYTHONPATH=scripts .venv/bin/python scripts/probe_example.py

Quickstart

import sys
sys.path.insert(0, "<this-skill>/scripts")          # so `import bigdata_toolkit` resolves
from bigdata_toolkit import (
    BigdataClient, EntityResolver, AnnotatedSearcher,
    StructuredDataREST, CostTracker, CostModel, rc,   # rc = SSL-retry wrapper
)

c  = BigdataClient()                                  # SDK + REST escape hatch, one object
er = EntityResolver(c)
nvda = rc(lambda: er.resolve_id("NVIDIA", country="US"))   # -> 'E09E2B'  (rp_entity_id is the gateway key)

# --- Structured financials the MCP does NOT expose (REST escape hatch) ---
rest = StructuredDataREST(c)
est  = rc(lambda: rest.analyst_estimates(nvda, period="quarter", limit=5))  # forward consensus
surp = rc(lambda: rest.latest_surprise(nvda))                               # last EPS/revenue surprise
cal  = rc(lambda: rest.events_calendar(nvda, categories=["earnings-call"],
                                       start_date="2026-06-01", end_date="2026-12-31"))

# --- Annotated chunks the MCP STRIPS: sentiment + entity spans (cost-guarded) ---
s    = AnnotatedSearcher(c)
docs = rc(lambda: s.search_entity(nvda, keyword="data center", chunk_limit=10))
# each chunk dict: {"sentiment": float, "entities": [{"key": rp_id, "start", "end"}], "text", ...}

# --- Always know your spend (chunk-billed; see Cost discipline) ---
ct = CostTracker(c); ct.snapshot()
# ... run a batch ...
print(ct.delta())     # {'delta_chunks':..., 'delta_query_units':..., 'usd_fast':...}

Wrap every network call in rc(lambda: ...) — a first-handshake SSL: UNEXPECTED_EOF is common and the SDK's internal retry doesn't cover it.

Routing — which capability answers the question

The user wants…	Use	Module
Company name / ISIN / CUSIP / SEDOL → rp_entity_id	EntityResolver.resolve_id / .resolve_by_isin	kg.py (SDK)
Forward analyst consensus (revenue/EPS by fiscal period)	StructuredDataREST.analyst_estimates	rest_ext.py
Latest earnings surprise (actual vs estimate)	.latest_surprise	rest_ext.py
Upcoming earnings / event calendar (one name or whole market)	.events_calendar	rest_ext.py
Analyst ratings / price-target consensus	.analyst_ratings / .price_target	rest_ext.py
Full financial statements (income / balance / cash-flow, multi-year)	.income_statement / .balance_sheet / .cash_flow_statement	rest_ext.py
TTM valuation metrics & ratios (EV/EBITDA, ROE, P/E, margins)	.key_metrics_ttm / .company_ratios_ttm	rest_ext.py
Company profile (CEO, sector, employees, IPO date)	.company_profile	rest_ext.py
Daily OHLC prices / dividend history	.daily_prices / .dividends	rest_ext.py
Revenue by geography / product segment	.revenue_geographic_segments / .revenue_product_segments	rest_ext.py
Daily entity-sentiment time series (don't self-aggregate from chunks!)	.entity_sentiment	rest_ext.py
Co-mention graph (supply-chain / competitor / customer — ⚠️ chunk-billed)	.connected_entities	rest_ext.py
Build a universe by market-cap / sector / country	.company_screener	rest_ext.py
News/filing/transcript chunks with sentiment + entity spans	AnnotatedSearcher.search_entity	search.py (SDK)
Bulk-pull many searches 50% cheaper (portfolio backfill)	BatchSearch (create→upload→poll→download)	rest_ext.py
Track / forecast quota spend before a backfill	CostTracker / CostModel	cost.py
Hit an endpoint the toolkit hasn't wrapped yet	client.http.post("v1/<resource>/query", body)	client.py

income/balance/cash-flow/daily-prices/dividends/revenue-segments return {fields, values} — wrap them in fields_values_to_records() to get [{field: value}]. The *_ttm / company_profile endpoints are already flat. All structured endpoints above are free (0 chunks) except connected_entities and AnnotatedSearcher (chunk-billed).

The two data faces (do NOT say "Bigdata fails for Chinese / A-shares")

This split is the most important non-obvious conclusion — state it precisely:

Face	Path	A-share / Chinese verdict
Structured financial (estimates, calendar, surprise, ratings, target, screener, financials, prices, dividends, revenue segments, daily entity-sentiment)	REST (rest_ext.py)	Works — via rp_entity_id resolved from the English name or ISIN (not the Chinese name). Data is fresh. Minor holes (some A-share price-targets return the entity with no numeric target). The daily entity_sentiment series lives here and works for any resolvable entity — it is not the dead end below.
Unstructured Chinese NLP (Chinese-news entity detection, per-chunk Chinese sentiment)	SDK search (search.py)	Dead end — a data-source-level gap, not an SDK bug: Chinese entity detection ≈ 0, per-chunk CJK sentiment is a doc-level inherited value, and language mislabels Chinese filings as English. Pair Bigdata with a China-domestic source for Chinese-language chunk content; use Bigdata for the structured face (incl. aggregate entity_sentiment) + ISIN/KG crosswalk + English-language chunk sentiment.

Cost discipline

1 query_unit = 10 chunks (official). Only chunk-search is billed — the structured /v1/* endpoints (estimates, financials, prices, calendar, surprise, ratings, the sentiment time series, screener…) are free (0 chunks, contract-tested). connected_entities (co-mentions) and AnnotatedSearcher are chunk-billed.

Three levers when you do pay for chunks:

1. ChunkLimit, never a bare int. Search.run(int) is a document limit billed by the full chunk page; ChunkLimit(n) bills per chunk. AnnotatedSearcher.search forces ChunkLimit for you. (We observed roughly a 52x gap once — a single measured data point, not stated in the official docs; treat the exact multiple as indicative. The rule "use ChunkLimit" holds regardless, because max_chunks is the official billing unit.)
2. Rerank bills only the returned chunks (official) — pass a rerank_threshold to recall broadly but pay only for the high-relevance hits.
3. Batch search is 50% cheaper ($0.0075 vs $0.015 / qu) — use BatchSearch for a large multi-query backfill.

Use CostModel to veto an over-budget job before running it, and CostTracker.snapshot() / delta() to measure real spend. Full accounting → references/cost_accounting.md.

Known pitfalls (already solved — don't re-debug these)

Each cost real debugging time and is fixed or guarded in the toolkit. Full reproductions and fixes in references/known_pitfalls.md:

1. First-handshake SSL: UNEXPECTED_EOF → wrap calls in rc(); the SDK's urllib3 retry only covers HTTP status, not the SSL EOF.
2. All(entity, Keyword(kw)) raises TypeError → combine with the & operator (entity & Keyword(kw)); All takes a single iterable. (Fixed in AnnotatedSearcher.entity_query.)
3. The 52x doc-limit billing trap → always ChunkLimit, never a bare int.
4. Closure capture in loops → bind loop vars: rc(lambda q=q, dr=dr: ...).
5. analyst_estimates(period="quarter") 400s above limit≈20.
6. company_screener filters must nest under "filters" — flat top-level keys don't 400, they're silently dropped → unfiltered universe.
7. Document.reporting_period is always None (the SDK model drops a field present on the REST wire) → fetch_reporting_period_raw.

What this skill will not do

• Never hardcode an API key. BigdataClient reads BIGDATA_API_KEY and fail-fasts if absent — no plaintext fallback (that is exactly the pattern secret scanners catch).
• Only ever reads — never writes or uploads. Every method is a read-only query (uploads is NotImplementedError in API-key mode anyway), so the toolkit can't mutate your account or push data anywhere.
• Never invent an endpoint or a schema. Every signature here is runtime L4-verified or marked L3 (doc-confirmed, not yet run); see references/verified_api_signatures.md. For a new endpoint, confirm the path via docs.bigdata.com/llms.txt rather than guessing.

File layout

bigdata-skill/
├── SKILL.md                       # this file — routing + setup + quickstart
├── scripts/
│   ├── bigdata_toolkit/           # the verified, cost-guarded package
│   │   ├── client.py              # BigdataClient: SDK (.bd) + REST escape hatch (.http/.conn)
│   │   ├── kg.py                  # EntityResolver: name/ISIN/CUSIP/SEDOL → rp_entity_id
│   │   ├── search.py              # AnnotatedSearcher: chunks + sentiment + entity spans (SDK)
│   │   ├── rest_ext.py            # StructuredDataREST (estimates/financials/prices/dividends/sentiment/co-mentions/screener) + BatchSearch + fields_values_to_records — official REST
│   │   ├── cost.py                # CostTracker + CostModel: chunk billing + budget veto
│   │   └── retry.py               # rc(): SSL/transient-error retry passthrough
│   └── probe_example.py           # runnable end-to-end smoke test
└── references/
    ├── escape_hatch_architecture.md  # WHY the MCP is lossy; bd._api.http mechanism; adding endpoints
    ├── verified_api_signatures.md    # L4/L3-verified signatures + the two data faces, with evidence
    ├── cost_accounting.md            # chunk billing, the 52x trap, CostModel/CostTracker, budgeting
    └── known_pitfalls.md             # every pitfall above, with reproduction + fix

References

Read when you need to…	File
Understand why the MCP is insufficient and how the REST escape hatch works (and how to wrap a new /v1/* endpoint)	references/escape_hatch_architecture.md
Look up an exact verified method signature + its verification level	references/verified_api_signatures.md
Budget a backfill or debug a surprise quota burn	references/cost_accounting.md
Diagnose an error you hit while pulling data	references/known_pitfalls.md