Your data is right. Is the AI’s answer?
AccurateMCP is the first independent accuracy audit for financial data APIs and the MCP servers built on them. We check your stock market data against official filings, find where AI assistants misread it, and show you how to fix it.
Read-only access. Nothing to install. Findings stay private to your team.
- Question
- What was Apple’s revenue in fiscal 2024?
- AI answer
- $383.3B fiscal 2023 value
- Source
- $391.0B
- Form 10-K, fiscal year ended Sep 28, 2024
- Finding
- Period mismatch, high severity
- Fix
- Label the fiscal period in the tool response.
Built for financial data providers
- Stock market API providers
- Fundamentals and financial statement APIs
- Market data platforms for prices, FX and crypto
- Fintech platforms launching AI assistants
Your numbers pass through a model before they reach the analyst
Your data can be correct at the source and still arrive wrong. The risk sits in the last three steps, where nobody is checking today.
Your database
Reported figures, prices, corporate actions
Verified by youYour financial data API
Fundamentals, prices and corporate actions your engineers test
Verified by youYour MCP server
Tool names, descriptions and response labels the AI must interpret
Ambiguity entersAI model
Chooses the tool, ticker, period and field, then reads the units
Misreads happenAnalyst’s answer
One confident number, with your brand behind it
No warning shownWrong period
Fiscal and calendar years, quarters and TTM get mixed up, especially for companies with non-December year-ends.
Asked for fiscal 2024, answers with fiscal 2023.
Unit and currency slips
Raw values get read in the wrong scale or currency, and the AI states them with full confidence.
Reports $391 million instead of $391 billion.
Similar fields confused
Financial data has many similarly named series. The AI can pick a close neighbour of the right one.
Reports basic EPS when diluted EPS was asked for.
AI accuracy falls when fiscal years don’t end in December
Leading AI model with structured data access via MCP, by company fiscal year-end
The evidence is already public
Independent research shows the same pattern: even with good data behind an MCP server, AI answers break on financial conventions.
of errors in the best setup came from fiscal versus calendar period confusion.
was the best accuracy reached with MCP data access, which the researchers say is not yet reliable enough for unsupervised use.
Same source as the chart.
What we check
Built around how financial and stock market data actually breaks, across your API and your MCP server.
Fiscal periods
Fiscal versus calendar years, quarters, TTM and non-December year-ends.
Units and currency
Thousands, millions and billions, reporting currency and per-share values.
Corporate actions
Splits, dividends, and adjusted versus unadjusted prices.
Identifiers
Tickers, exchanges, share classes and non-US listings.
Statement integrity
Balance sheets that balance, cash flows that tie, signs that make sense.
Field meaning
Basic versus diluted, gross versus net, totals versus sub-components.
Coverage
Common analyst questions your server can’t answer yet.
Regressions
Answers that were right last month and broke after a release.
API data integrity
Your API responses checked directly against filings and accounting rules, before any AI is involved.
API and MCP parity
Your MCP server returns the same values as your REST API for the same request.
Covers stock market APIs and financial data APIs: fundamentals and financial statements, end-of-day and historical prices, earnings, dividends and splits, and ETF, FX and crypto data where you offer it.
What you get
A clear, auditable picture of how accurate your financial data is, through your API and through AI assistants, and what to change.
- An accuracy scoreBenchmarked against independent sources, with sub-scores by check type.
- Findings with proofEach issue with its severity, the expected value and its source, and steps your team can rerun in minutes.
- A recommended fix for each issueSpecific changes your engineers can make, in priority order.
- Ongoing monitoringRegular checks, plus a run after every MCP update, so regressions are caught before your users see them.
- Hands-on fixesFor busy teams, we resolve the issues with you and re-run the audit to prove the improvement.
How it works
The audit runs from the outside, the same way your users reach your data.
Share read access
Your MCP server endpoint and a test key. Nothing to install or change on your side.
We run the audit
Realistic analyst questions across your key tools, tickers and periods, each answer checked against independent sources.
You get the report
Score, findings, proof and fixes. Then we keep monitoring, so you know it stays accurate.
Our audit principles
Trust comes from method, not logos. Every audit follows the same rules, whoever the client is.
One method for every client
The same checks and scoring rules apply to every provider, so scores are comparable and never negotiated.
Evidence before scores
Every finding comes with the question, tool call, response and source value. Your team can rerun any of them.
Independent ground truth
Answers are checked against official company filings and accounting rules, not only against your own API.
Read-only access
We never need write access or changes to your systems, and we respect the rate limits you set.
Confidential by default
Findings are never published or used in any material without your written agreement.
Deleted on request
Audit data and traces are removed whenever you ask, with confirmation in writing.
Public filings and benchmarks our checks build on
Public sources only. No affiliation or endorsement implied.
Become a founding client
We’re working with a small group of financial data providers as founding clients.
- First audit on a complimentary basis
- Shape the checks around the tools and markets you care about
- Findings stay strictly between us
Questions data teams ask
Do you need write access or any changes to our systems?
No. The audit uses read-only access to your MCP server, the same way your users connect. A dedicated test key is ideal, so audit usage stays separate.
How is our data and access handled?
Findings are shared only with your team and never published without your written agreement. Audit data is deleted on request, and we respect any rate limits you set.
Which AI assistants do you test with?
Widely used AI assistants, the same ones your users connect to your MCP server. If you know which ones matter most to your users, we prioritise those.
Do you audit our API, or only the MCP server?
Both. We check your financial data API responses against official filings and accounting rules, then check what AI assistants say when they use that data through your MCP server.
We don’t have an MCP server yet. Is this still useful?
Yes. The API data checks work on their own, and the audit shows what to get right before you launch an MCP server.
Does this replace our own testing?
No, it complements it. Your tests check that your API and MCP server work. We check that the financial data is right against independent sources, and what AI assistants actually say to your users from it.
What if you find something we disagree with?
Every finding includes the question, the tool call, the response and the source value, so your team can rerun it and judge it directly.
Who’s behind it
AccurateMCP is built by Pranjal, a data scientist with more than eight years of experience in financial data and AI.
- 8+ yearsin data science, working hands-on with financial data APIs, stock market APIs and MCP servers
- OpenAI certifiedTechnical Practitioner and Consultative Solutions Practitioner
- Published on MCP accuracyTests financial MCP servers on real analyst tasks and writes about the results