# Datavidence Financials (pypi · datavidence-financials)

Normalized US-GAAP financial statements & fundamentals from SEC EDGAR, as MCP tools for LLM agents.

- Trust score: 67/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-21

## Components

- pypi · `datavidence-financials`: 67/100 (this document), [markdown](https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials.md), [page](https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials)

## Channel facts

- Registry: `pypi`
- Package: `datavidence-financials`
- Version: `0.1.2`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-21.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it.
  - 1 of 32 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 48/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 3 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 65/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 676 tokens (~169/item across 4 items; 4 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 67/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 4 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

**Unverified: 1 category.** A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### How do I install the Datavidence Financials MCP server?

Datavidence Financials runs locally as a PyPI package, launched with uvx datavidence-financials. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add ai-datavidence-datavidence-financials -- uvx datavidence-financials
```

### Cursor

```json
{
  "mcpServers": {
    "ai-datavidence-datavidence-financials": {
      "command": "uvx",
      "args": [
        "datavidence-financials"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "ai-datavidence-datavidence-financials": {
      "command": "uvx",
      "args": [
        "datavidence-financials"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add ai-datavidence-datavidence-financials -- uvx datavidence-financials
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ai-datavidence-datavidence-financials": {
      "type": "local",
      "command": [
        "uvx",
        "datavidence-financials"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ai-datavidence-datavidence-financials --command uvx --arg datavidence-financials
```

### Hermes

```yaml
mcp_servers:
  ai-datavidence-datavidence-financials:
    command: "uvx"
    args: ["datavidence-financials"]
```

### Netclaw

```json
{
  "McpServers": {
    "ai-datavidence-datavidence-financials": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "datavidence-financials"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add ai-datavidence-datavidence-financials -t stdio -c uvx -a datavidence-financials
```

### Other

```json
{
  "mcpServers": {
    "ai-datavidence-datavidence-financials": {
      "command": "uvx",
      "args": [
        "datavidence-financials"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-17 (score 67)

First indexed and scored.

## MCP tools (4)

### `get_financials` (~198 tokens)

Retrieve normalized US-GAAP financial statements (income statement, balance
sheet, cash flow) for one company and fiscal year, from SEC EDGAR XBRL.

Identify the company by `ticker` OR `cik`. Use `as_of` (ISO YYYY-MM-DD) to get
the figures as originally reported on that date — no look-ahead bias — for
backtests. Set `include_provenance=true` to attach, for every value, the SEC
accession, filed date, and a direct EDGAR source URL for citation. Set
\`include_ratios=true` for margins, ROA/ROE, and leverage derived from the same
statements. Returns the normalized data object.

Input parameters:

- `as_of`
- `cik`
- `include_provenance` (boolean)
- `include_ratios` (boolean)
- `ticker`
- `year` (integer, required)

### `list_filings` (~134 tokens)

List a company's recent SEC filings (newest first) from the EDGAR
submissions index — a lean company header plus filing rows.

Identify the company by `ticker` OR `cik`. Optionally filter by `form` (e.g.
"10-K", prefix-matched so it includes amendments like "10-K/A") and cap the
number of rows with `limit` (1-100). Use this to discover which fiscal years
or filings are available before calling get_financials.

Input parameters:

- `cik`
- `form`
- `limit` (integer)
- `ticker`

### `get_financials_batch` (~222 tokens)

Retrieve normalized US-GAAP financials for MANY companies in one call — use
this to compare peers or scan a set for a single fiscal year.

Pass companies as comma-separated `tickers` (e.g. "AAPL,MSFT,GOOGL") and/or
\`ciks`; up to 25 symbols total, all for the same `year`. `as_of`,
\`include_provenance`, and `include_ratios` behave as in get_financials and
apply to every symbol. Each company returns its own result — either `data` or
an `error` with a recovery hint — so one bad symbol never fails the batch. The
whole call counts as a SINGLE request against your monthly quota, so prefer it
over many get_financials calls when you need several companies.

Input parameters:

- `as_of`
- `ciks`
- `include_provenance` (boolean)
- `include_ratios` (boolean)
- `tickers`
- `year` (integer, required)

### `get_usage` (~71 tokens)

Report the calling API key's current monthly quota: tier, monthly limit,
requests used and remaining this billing month, and when it resets.

Free to call — it does NOT consume quota. Check it before a large batch or a
long run so you can pace requests and avoid a hard rate-limit (429).

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials#diagnostics

## Score history

- 2026-09-21: 67
- 2026-09-20: 67
- 2026-09-19: 67
- 2026-09-18: 67
- 2026-09-17: 67

## Common questions

### What is the Datavidence Financials MCP server?

Datavidence Financials is an MCP server listed in the public MCP registry as ai.datavidence/datavidence-financials. Normalized US-GAAP financial statements & fundamentals from SEC EDGAR, as MCP tools for LLM agents. This page covers its PyPI package (datavidence-financials).

### Is the Datavidence Financials MCP server safe to use?

Datavidence Financials scores 67 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the Datavidence Financials MCP server expose?

Datavidence Financials exposes 4 tools: get_financials, list_filings, get_financials_batch, get_usage. Their descriptions and schemas cost roughly 625 tokens of context every time the server is loaded.

### Is the Datavidence Financials MCP server still maintained?

Datavidence Financials is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

### What licence is the Datavidence Financials MCP server under?

Datavidence Financials declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- PyPI project: https://pypi.org/project/datavidence-financials/
- Socket report: https://socket.dev/pypi/package/datavidence-financials
- Repository: https://github.com/datavidence/datavidence-financials-mcp
- Website: https://financials.datavidence.ai/
- Changelog RSS feed: https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials.json
- HTML version of this page: https://verifymcp.io/servers/ai-datavidence-datavidence-financials/datavidence-financials