Market data your
AI agents can actually use.
A native MCP server connects Claude, GPT, and your own agents to prices, SEC fundamentals, holdings, and macro — point-in-time and survivorship-free. The truth your bots and your backtests both need, behind a single API key.
from synapse_discovery import SynapseClient
# One key. Point-in-time by default.
client = SynapseClient("your-api-key")
aapl = client.fundamentals("AAPL")[0]
print(f"Revenue: ${aapl.revenue/1e9:.1f}B")
# → Revenue: $383.3B
Everything You Need to
Build Financial Products
One API for all your financial data needs. No more cobbling together multiple vendors.
Equity Prices
Clean daily OHLCV with raw, split-adjusted, and total-return modes — point-in-time correct for backtesting.
SEC Fundamentals
Standardized XBRL financials from every 10-K and 10-Q since 2009, unified with earlier history from a second source.
Holdings & 13F
ETF and mutual-fund composition plus 13F institutional positions — see what funds and large investors hold.
Macro & Economic Data
FRED economic series, Treasury yield curves, and market-context snapshots alongside your equities data.
Point-in-Time & Survivorship-Free
Query data as it was known on any date, with delisted and bankrupt names kept in — no look-ahead or survivorship bias.
AI-Native (MCP)
A first-class MCP server lets Claude, GPT, and other agents query directly — plus a structured REST API and bulk Parquet.
Plug in an agent.
It already knows the data.
A native MCP server exposes typed tools to Claude, GPT, and your own agents — and every REST response carries provenance, quality, and cost metadata, so agents discover capabilities, validate queries, and audit their own outputs without hand-holding.
Capabilities Discovery
Datasets, exchanges, time ranges, update schedules - everything an agent needs to plan queries.
Field Definitions
Types, units, nullability, examples. Machine-readable JSON Schema for every field.
Fuzzy Matching
Resolve "Apple" → AAPL with confidence scores. Handle ticker changes and ambiguity.
Query → Result → Provenance
Every response includes metadata that makes AI outputs auditable and reduces hallucinated finance.
- Provenance: Source, collection time, adjustment model
- Quality: Completeness %, staleness, outlier flags
- Cost: Units consumed, cache hit/miss
- Error hints: "Did you mean AAPL?" with suggestions
{
"data": [{"date": "2026-01-24", "close": 198.42}],
"provenance": {
"source": "eod_historical",
"collection_time": "2026-01-24T06:00:00Z",
"adjustment_model": "split_adjusted"
},
"quality": {
"completeness": 0.98,
"staleness_hours": 18
},
"cost": {
"units_consumed": 1,
"cache_hit": true
}
}# Install the sync client
$ pip install synapse-discovery
# Initialize with your API key
$ synapse-discovery init --api-key sd_live_xxx
✓ Connected to Synapse Discovery
# Sync prices and fundamentals locally
$ synapse-discovery sync --to ./data --datasets prices,fundamentals
Downloading... 45.2 MB
✓ Sync complete! 8,500 files
# Query with DuckDB - no API calls!
$ duckdb -c "SELECT * FROM './data/prices/NASDAQ/AAPL/*.parquet' LIMIT 5"
Your Own
Data Lake
Sync once, update daily. Get a clean, standard Parquet layout you own - no babysitting our internal structure. Query locally with DuckDB, Polars, or Spark.
- Parquet files partitioned by exchange/symbol/year
- Incremental sync - only download what changed
- Manifest with checksums for data integrity
- Sync to S3/GCS or local directory
POST /query - The Thick Endpoint
AI agents want flexibility without 20 API calls. Query multiple tickers, mix prices with fundamentals, and get point-in-time data in a single request.
POST /api/v1/query
{
"universe": {
"tickers": ["AAPL", "MSFT", "GOOGL"]
},
"fields": [
"close_split_adj",
"volume",
"revenue",
"eps_diluted"
],
"range": {
"start": "2024-01-01",
"end": "2025-12-31"
},
"point_in_time": true,
"asof": "2026-01-01T00:00:00Z"
}
Simple, Transparent
Pricing
No hidden fees. No enterprise sales calls for basic features. Start free, scale as you grow.
Free
For personal projects and exploration
- 100 API calls/day
- 1 year historical data
- Basic fundamentals
- Community support
Developer
For developers building financial apps
- 10,000 API calls/day
- 5 years historical data
- Full fundamentals
- ETF holdings
- Bulk CSV exports
- Email support
Quant
For quant researchers and traders
- 100,000 API calls/day
- Full historical data
- Point-in-time queries
- 13F institutional data
- Parquet bulk downloads
- Delta-sync updates
- Priority support
Enterprise
For hedge funds and institutions
- Unlimited API calls
- Full data universe
- Dedicated infrastructure
- Custom integrations
- Custom SLA (by agreement)
- Dedicated support
- On-premise option
Built for
Serious Research
The things that actually matter for backtesting and AI workflows.
No survivorship bias
Delisted, bankrupt, and acquired companies stay in the archive, so your backtest universe reflects history as it actually was — not just today’s survivors.
Point-in-time by default
Every price and fundamental is queryable as-of any historical date, so you research on the data that was actually known then.
AI-native, not bolted on
A native MCP server means Claude, GPT, and other agents pull data directly — no scraping, no glue code, responses built for LLMs.
Ready to Get Started?
Join thousands of developers and quants building with Synapse Discovery.