Datavidence Financials — MCP Server

Normalized US-GAAP financial statements for every SEC XBRL filer — point-in-time, as originally reported — as MCP tools for your AI agent (or a plain REST API).

Language models are good at reasoning about financial statements and bad at getting them. SEC EDGAR has the data, but its XBRL is a thicket of inconsistent tags, renamed concepts, and per-filer quirks — so agents either hallucinate the numbers or you spend weeks building a normalization layer. This is that layer: your agent asks for a company's financials and gets a clean, consistent answer — over the Model Context Protocol, or as a plain REST API.

⭐ If this is useful, please star the repo. It's a solo, early project, and stars are how other developers find it.

Claude calls the Datavidence Financials get_revisions tool: Pfizer's FY2019 revenue as filed ($51.75B) and as it reads today ($40.9B), with links to the SEC filings

This is the open-source, AI-native layer for the Datavidence Financials API — a standalone stdio server that forwards over HTTPS to the REST API (bring your own key). The connector is MIT-licensed; the underlying API is a commercial service.

Try it in 30 seconds (no signup)

Run it with no clone using uv — it installs into a throwaway environment:

bash

FL_API_KEY=sandbox_demo_key uvx datavidence-financials

sandbox_demo_key returns sample data with no signup — good for a first run. Set FL_API_KEY to your real key for live SEC data. The server speaks MCP over stdio, so an MCP client (e.g. Claude Desktop, below) launches it.

Or install it as a persistent tool with pipx:

bash

pipx install datavidence-financials
FL_API_KEY=sandbox_demo_key datavidence-financials

Why it's different

Point-in-time, as originally reported. Pass an as_of date and you get the figures as they stood then — no look-ahead bias from later restatements. The thing backtests quietly get burned without.
Normalized to one schema. EDGAR's XBRL tags drift between filers and change over the years. We map them to a single US-GAAP income statement, balance sheet, and cash flow — including the awkward cases (noncontrolling interest, mezzanine/temporary equity, non-calendar fiscal years) — so you never parse a filing.
Every SEC XBRL filer. Coverage is the whole SEC XBRL universe (~2009 to present), fetched and normalized on demand — any CIK with XBRL facts, not a curated subset.
Source-linked, too. Turn on provenance and every value carries its SEC accession, filed date, and a direct EDGAR URL — so the model cites the filing instead of inventing a number.

Honest scope: US-GAAP only (IFRS filers return a clear "unsupported" signal rather than wrong numbers); XBRL-era history (~2009 onward). And when a company reorganizes under a new holding entity, SEC's ticker map points only at the successor CIK — the earlier years stay under the predecessor, so the error tells you and you pass cik= directly.

Tools

get_financials — normalized income statement + balance sheet + cash flow for one company and fiscal year, from SEC EDGAR XBRL. Supports as_of (point-in-time, as originally reported), include_provenance (per-value SEC accession, filed date, and direct EDGAR source URL), and include_ratios (margins, ROA/ROE, leverage).
get_financials_batch — the same, for up to 25 companies in one call (comma-separated tickers/CIKs). Per-symbol results; counts as a single quota unit, so one bad symbol never fails the batch.
list_filings — a company's recent SEC filings (newest first) from the EDGAR submissions index; filter by form (e.g. 10-K). Use it to discover which fiscal years are available before calling get_financials.
get_revisions — how a fiscal year's figures CHANGED across filings: every reported value with its filing date, form, SEC accession and EDGAR link, plus whether it was restated and by how much. Pfizer's FY2019 revenue was filed at $51.75B in 2020 and restated to $40.9B by 2022 — this is how you see that.
search_companies — find a ticker and CIK by ticker or company name, for when you have "Berkshire" and need BRK-B. Use it before the tools above, which all require an identifier.
get_usage — the key's monthly quota (tier, used, remaining, reset). Free to call; consumes no quota.

Errors surface the API's recovery_action hint, so agents self-correct.

Claude Desktop

Add to claude_desktop_config.json, then fully quit (Cmd+Q) and reopen Claude Desktop — the tools appear:

config.json

{
  "mcpServers": {
    "datavidence-financials": {
      "command": "uvx",
      "args": ["datavidence-financials"],
      "env": { "FL_API_KEY": "sandbox_demo_key" }
    }
  }
}

Then ask, e.g., "Get AAPL's FY2023 income statement, balance sheet, and cash flow with ratios." Swap in your real key for live data.

Codex

Add to ~/.codex/config.toml, then start codex — the tools appear:

toml

[mcp_servers.datavidence-financials]
command = "uvx"
args = ["datavidence-financials"]
env = { FL_API_KEY = "sandbox_demo_key" }
# uvx downloads the package on first run; allow more than Codex's 10 s default.
startup_timeout_sec = 30

Swap in your real key for live data.

Configuration (environment variables)

Variable	Default	Notes
`FL_API_KEY`	(none)	Your API key. `sandbox_demo_key` = no-signup trial (sample data).
`FL_API_BASE_URL`	`https://api.financials.datavidence.ai`	Point at a marketplace gateway for metered BYOK access.
`FL_API_KEY_HEADER`	`X-API-Key`	Header to send the key in (e.g. `X-RapidAPI-Key` for the RapidAPI gateway).
`FL_API_TIMEOUT`	`30`	Per-request timeout, in seconds.

Getting live data

The demo key returns sample data. For live figures, grab a key on RapidAPI: Free (1,500 calls/mo), Pro ($29 / 50k), Business ($99 / 300k). Full feature parity across tiers — they differ only by monthly call volume.

For a metered marketplace channel, point FL_API_BASE_URL at the gateway endpoint and set FL_API_KEY / FL_API_KEY_HEADER to the gateway's key/header (BYOK), so calls are metered by the marketplace rather than hitting the origin directly.

Run from source

Terminal

pip install -e .
FL_API_KEY=sandbox_demo_key python -m mcp_server.server   # stdio transport

Feedback

Found a company where the numbers come back wrong, or a field/tool you need? Open an issue — the normalization edge cases are exactly the feedback that makes this better, and I read every one.

Datavidence Financials README