Top MCP financial data providers for Claude

by Nicolae Buldumac
· 05/26/2026 06:45 · 12 min read
Top MCP financial data providers for Claude

Anthropic's Claude has become the default AI workspace for institutional finance — and the data providers it plugs into now define the quality of the answers it gives. Here's how Global Database, Moody's, MonetaiQ, LSEG, Dun & Bradstreet, FactSet, and S&P Capital IQ actually compare, with primary sources and no marketing fluff.

If you're building a workflow on Claude that touches company data — KYB onboarding, credit analysis, supplier verification, M&A diligence, equity research — you don't have a model problem anymore. Claude Opus 4.7 currently leads the Vals AI Finance Agent benchmark at 64.4% accuracy. The bottleneck is the data layer underneath it.

An AI agent reasoning over stale, scraped, or unattributed data produces confident-sounding answers that auditors and regulators will reject. The shift toward Model Context Protocol (MCP) — the open standard Anthropic introduced in November 2024 and donated to the Linux Foundation in late 2025 — has turned data partnerships into the central battleground. Whoever's MCP server Claude can call is whoever's data shapes the decision.

This article is a practical, source-verified comparison of seven providers that financial professionals actually use through Claude in 2026: Global Database, Moody's, MonetaiQ, Dun & Bradstreet, FactSet, S&P Capital IQ, and LSEG. Each occupies a different lane. None of them does everything. The point is to match the provider to the workflow.

How to read this list. Every provider on this list has an official, vendor-built MCP integration with Claude. We deliberately excluded Bloomberg: there's no official Anthropic–Bloomberg partnership as of mid-2026, only a community-built wrapper that requires a Terminal licence and ships without enterprise support. If real-time markets data inside Claude is your primary need, LSEG is the institutional answer covered below.

The landscape: why data partners suddenly matter

Anthropic launched Claude for Financial Services in July 2025, expanded it in October 2025, and shipped a full agent suite in May 2026 with roughly ten pre-built agent templates for banking, asset management, and insurance workflows. The launch partners — and the partners added since — read like a list of every serious institutional data vendor: FactSet, S&P Global, Moody's, LSEG, Morningstar, PitchBook, Daloopa, MSCI, Chronograph, MT Newswires, Aiera, Third Bridge, and in the May 2026 expansion Dun & Bradstreet, Verisk, Experian, Fiscal AI, Guidepoint, GLG, and IBISWorld.

That's not a coincidence. Every one of these vendors has independently concluded that "be available inside Claude" is now a survival requirement for institutional data businesses. The competitive question shifted from who has the data to whose data the agent can call without a re-build.

The first-principles point: An LLM is only as good as the data it can reach. The trust, governance, and provenance characteristics of that data feed directly into every downstream output — including the ones a regulator, auditor, or court will eventually ask about.

What separates a good financial data MCP from a mediocre one

Cutting through the noise, four things actually matter for production finance workflows:

  1. Provenance. Where does the underlying data come from, and can each field be traced back to a primary source — a regulator, a registry, a filing — or is it scraped, aggregated, or AI-derived?
  2. Coverage depth. How many entities, how many jurisdictions, how far back, and how granular (parent/subsidiary, UBO, historicals)?
  3. Refresh cadence. Daily? Quarterly? On-demand? Stale ownership data invalidates a sanctions check even if everything else is correct.
  4. Auditability. Can the output of an agent run be defended in front of a regulator with timestamps, source IDs, and a chain back to the original document?

Provider claims on these dimensions vary widely. The visual below is what the comparison actually looks like once you strip out the marketing language.

Figure 1
Coverage breadth vs. data provenance — seven providers compared
Coverage breadth →Data provenance →NARROWBROADUNIVERSALAGGREGATEDMIXEDREGISTRYGlobal Database600M+ profilesMoody's600M entitiesMonetaiQ400M+ privateS&P Capital IQPublic + select privateFactSetPublic co. focusLSEGMarkets dataDun & Bradstreet~500M records
Position reflects publicly stated coverage and sourcing model. Bubble size approximates dataset volume.

1. Global Database — registry-sourced company intelligence

2. Moody's — credit, risk & KYC at native-app depth

3. MonetaiQ — deep financial statements at private-company scale

MonetaiQ
MCP server Financial statements 400M+ private companies 60,000 public companies

What it is: A specialist financial-data provider focused on standardised financial statements for private and public companies at scale. While most institutional providers either cover public companies deeply (FactSet, Capital IQ) or private companies shallowly, MonetaiQ's focus is the harder problem: comprehensive financial statements for the 400 million+ private companies most platforms underserve.

What's in it: Balance sheets, income statements, cash flow statements, P&L breakdowns, and key financial ratios for 400M+ private companies and 60,000 publicly listed firms across 100+ countries. Multi-year historicals support trend analysis. Public data is sourced from stock exchanges and regulatory filings (10-K, 10-Q, annual reports); private data from government registries, verified disclosures, and trusted databases.

Claude integration: MCP server available for direct integration with Claude and other MCP-compatible clients. The API also supports identifier-based lookup (company name, website, LinkedIn URL, registration number, VAT) — useful for AI agents that need to resolve entity references before fetching financials. GDPR compliant, with full data ownership rights for licensees.

Where it wins
  • Deep financial statements for private companies at scale — where FactSet, Capital IQ, and LSEG have shallow coverage.
  • Standardised P&L, balance sheet, and cash flow data across 400M+ private firms — harder to find anywhere else.
  • Identifier-flexible lookup — resolve companies by name, website, LinkedIn, registration number, or VAT before pulling financials.
  • Full data ownership rights — AI training and redistribution permitted, unlike most institutional providers.
  • Bulk feed and API options with flexible delivery (JSON, CSV, XML, custom).
Where it doesn't
  • Not a real-time markets terminal — public-company data refreshes quarterly, not intraday.
  • No credit ratings, no analyst estimates, no earnings transcripts — this is a financials dataset, not a research platform.
  • Less name-recognition in enterprise procurement than Moody's, FactSet, or Capital IQ — expect to justify the choice internally.
  • No native Claude app (unlike Moody's) — integration is connector-level, not UI-embedded.
How Global Database fits in
The only company data Claude can cite back to a public authority

AI changed the rules. An auditor doesn't care that Claude is confident — they care whether the underlying data is defensible. Aggregated, AI-enriched, or scraped sources fail that test. Global Database is built differently: every field on every record traces back to a named government registry, with a timestamp and a public-source URL. That's the data layer regulators recognise — and the one your KYB, AML, and onboarding agents can actually be defended on.

4. Dun & Bradstreet — entity resolution at scale

Dun & Bradstreet
MCP connector (added May 2026) D-U-N-S Number ~500M records

What it is: The classic business-identity infrastructure provider, anchored by the D-U-N-S Number — a 9-digit identifier that has become a default standard for vendor onboarding, supply-chain mapping, and procurement. D&B was added as a Claude data partner in the May 2026 expansion of Claude for Financial Services.

What's in it: Credit data, supply-chain information, and entity-resolution against the D-U-N-S corpus. The pitch is helping firms "connect systems of record and scale AI-enabled workflows" — i.e., resolving the "is this entity in this database the same as that entity in another database" problem at scale.

Claude integration: MCP-based connector announced as part of the May 2026 finance-agent launch. D&B team members are also using Claude in their own workflows.

Where it wins
  • Procurement, vendor master, and supply-chain risk — D-U-N-S Number is the lingua franca for business identity.
  • Entity resolution at scale — matching the same entity across multiple internal systems.
  • Persistent identifier — the D-U-N-S Number is stable across mergers, rebrands, and address changes.
Where it doesn't
  • Centralised proprietary identifier system — not registry-direct.
  • Curated, aggregated, and editorial — weaker provenance than government registries for regulator-facing workflows.
  • Refresh cadence is variable; some fields lag significantly.
  • Best treated as a complement to registry data, not a substitute.

5. FactSet — fundamentals and analyst estimates

FactSet
MCP connector Fundamentals Analyst estimates Public co. focus

What it is: A foundational financial-data and analytics platform for institutional investors, asset managers, hedge funds, and banks. FactSet has been one of the most embedded Claude data partners since the July 2025 launch and continues to expand its agent-marketplace integration.

What's in it: Comprehensive public-company fundamentals, analyst estimates, market data, and research analytics. FactSet's positioning is "foundational market data, research, and analytics" that institutional clients want to reason over using AI in the tools they already use.

Claude integration: MCP connector for real-time access to FactSet data inside Claude, plus a financial-services marketplace where FactSet ships pre-built Claude agents. Anthropic explicitly names FactSet as the partner for fundamentals and analyst estimates in the agent suite.

Where it wins
  • Equity research, comparable-company analysis, and DCF modelling — institutional default for buy-side and sell-side analysts.
  • Analyst estimates breadth — one of the deepest consensus-estimate datasets in the market.
  • Modular pricing — lower entry point than Bloomberg for equivalent core functionality.
  • Excel and PowerPoint integration via Claude add-ins — data flows directly into modelling workflows.
Where it doesn't
  • Public-company centric — private-company coverage is shallow outside the largest names.
  • Not a KYB or compliance dataset.
  • Licence terms restrict AI training and redistribution.
  • "Fully loaded" configurations escalate quickly into Bloomberg-tier territory.

6. S&P Capital IQ — curated financials & transcripts

S&P Global / S&P Capital IQ
MCP via Kensho Capital IQ Financials Earnings transcripts Tear sheets

What it is: S&P Global's flagship financial-analysis dataset, delivered to Claude through an MCP server built by Kensho, S&P's AI innovation arm. The integration was announced in July 2025 and has expanded to support Claude in Excel, PowerPoint, and Cowork.

What's in it: S&P Capital IQ Financials, earnings call transcripts, M&A and transactions data, industry data, and the curated tear-sheet content that Capital IQ Pro users rely on. Kensho's LLM-ready API is the underlying delivery mechanism.

Claude integration: Native MCP connector inside Claude. An S&P Global plugin for Claude Cowork was added in early 2026, and S&P Global is also available through the Claude add-ins for Excel and PowerPoint via the MCP connector.

Where it wins
  • Sell-side and buy-side equity research — the curated tear-sheet experience institutional analysts rely on.
  • M&A target profiling and transactions data — deep transaction history with editorial enrichment.
  • Earnings call transcript dataset — significant differentiator for fundamental research.
  • Native Cowork plugin and Excel/PowerPoint integration via the Kensho MCP server.
Where it doesn't
  • Curated institutional data, not registry-direct — weaker provenance for KYB/AML workflows.
  • Team-based licensing limits flexibility for distributed AI-agent deployments.
  • AI training and redistribution restricted by licence.
  • UI has been criticised as less polished than FactSet or Bloomberg.

7. LSEG — markets data with enterprise Claude integration

LSEG (London Stock Exchange Group)
MCP server Workspace Markets data Fixed income / FX / Equities

What it is: The post-Refinitiv markets data and analytics business of the London Stock Exchange Group. LSEG announced its Anthropic collaboration on October 27, 2025, the same day Claude for Financial Services was expanded — with its MCP server going live in the Claude MCP Partner Directory simultaneously.

What's in it: Live market data including fixed income pricing, equities, foreign exchange rates, macroeconomic indicators, and analyst estimates. The integration is built around LSEG's flagship Workspace product and Financial Analytics, with additional data categories rolling out in phases. Decades of curated, AI-ready content underlie the feed.

Claude integration: Dedicated MCP server, live in the Claude MCP Partner Directory since October 2025. Part of LSEG's broader "LSEG Everywhere" AI strategy, which also includes partnerships with Microsoft, Snowflake, Databricks, and Rogo.

Where it wins
  • Deepest official Claude integration for real-time markets data — the institutional alternative to Bloomberg inside an AI workflow.
  • Live bond pricing, FX rates, equity ticks, macro indicators — bundled in one MCP server.
  • Trading, treasury, and macro-aware research workflows are natural fits.
  • Decades of curated, AI-ready content — LSEG's "LSEG Everywhere" strategy explicitly prioritises AI distribution.
Where it doesn't
  • Not a registry-direct provider for KYB or compliance.
  • Private-company and global registry coverage is shallower than dedicated company-intelligence providers.
  • Licence terms restrict AI training and redistribution.
  • Enterprise-only — not available to individual practitioners.

Side-by-side: how the seven providers compare

The table below adds the two dimensions providers most often gloss over: refresh cadence (how stale the data gets between updates) and indicative pricing. Pricing ranges are based on publicly reported figures and third-party cost benchmarks where direct vendor pricing isn't disclosed.

ProviderSourcingRefresh cadenceClaude integrationIndicative pricing
Global DatabaseRegistry-direct (400 official sources)Daily (registry-driven)MCP server · API · PlatformLicense-based, annual. Free trial: 100 requests
Moody'sCurated + modelled, 600M entitiesContinuous (varies by product)Native MCP appEnterprise quote-only; tied to user/AUM/lookup limits
MonetaiQExchanges, filings & registriesQuarterly (public) / as filed (private)MCP serverTiered by coverage, users, export queries
Dun & BradstreetD-U-N-S identifier, curated/editorialVariable; some fields lagMCP connector (May 2026)Enterprise quote-only; volume-based
FactSetMixed: exchanges + curatedReal-time (markets) / variable (fundamentals)MCP connector + agents marketplace~$4k–$50k per user/year (modular)
S&P Capital IQMixed: exchanges + curatedReal-time (markets) / variable (fundamentals)MCP via Kensho + Cowork plugin~$25k per team/year (Capital IQ Pro)
LSEGExchange + analyst feedsReal-time (markets)MCP server (Partner Directory)~$15k–$22k per user/year (Workspace)

FactSet pricing range from CostBench (March 2026). LSEG Workspace pricing from third-party benchmarks; actual enterprise contracts (10–25 users) typically range $150k–$400k total per Vendr data. S&P Capital IQ figure is widely reported third-party data; enterprise contracts vary significantly. Moody's and D&B do not publish list prices; both use quote-based enterprise licensing tied to seats, AUM, or lookup volumes.

Figure 2
Which provider fits which workflow
WORKFLOWGDBMoody'sMonetaiQD&BFactSetCap IQLSEGKYB onboardingAML & sanctions screeningCredit risk & ratingsUBO & ownership mappingSupplier & vendor verificationEquity research & DCFsM&A target profilingEarnings analysisPrivate-company financialsLive markets & tradingFixed income, FX & macroProcurement & vendor masterBest fitStrongPartialOut of scope

How to actually choose: a decision framework

The honest answer is that you probably don't pick one. Mature finance teams stack providers — registry-direct data for the compliance and KYB layer, curated institutional data for the research and modelling layer, entity-resolution data to connect everything together. The question is which one anchors which workflow.

If you only have 30 seconds, match your role to the row below.

If you are…Your core problemAnchor providerAdd on top
Compliance / KYB lead
Fintech, bank, payment co.
Auditable counterparty & UBO dataGlobal DatabaseMoody's for adverse media; D&B for D-U-N-S resolution
Credit / risk analyst
Lender, insurer, FI
Ratings, probability of default, exposureMoody'sGlobal Database for registry-backed financials
Equity research analyst
Buy-side, sell-side, hedge fund
Fundamentals, estimates, transcriptsFactSet or S&P Capital IQLSEG for live markets data inside Claude
Trader / markets analyst
Trading desk, treasury, FX
Real-time markets data inside ClaudeLSEG (official MCP)FactSet or Capital IQ for fundamentals overlay
Investment banker
M&A, advisory, PE
Target profiling, pitch-book dataS&P Capital IQMonetaiQ for private-company financials; Global Database for registry diligence
Private-equity analyst
PE, VC, growth equity
Private-company financial statements at scaleMonetaiQGlobal Database for ownership & registry data; PitchBook for deal flow
Procurement / 3rd-party risk
Manufacturer, enterprise IT
Vendor master, supply-chain riskDun & BradstreetGlobal Database for non-US registry coverage; MonetaiQ for vendor financial health
Data engineer / AI builder
Building agents on Claude
Clean, structured, MCP-callable feedsMatch to the workflow aboveAlways require provenance metadata in the response

The deeper question: why not just buy Moody's?

This is the question every buyer with a budget asks. Moody's covers 600 million entities, has the deepest native Claude integration of any provider on this list, and bundles credit ratings — a dataset nobody else owns. Why bother with anything else?

Two reasons, both structural:

The data is curated, not registry-direct. Moody's "600M entities" figure aggregates and models data across dozens of underlying sources. That's a feature for credit analysis (the editorial layer adds value) and a bug for KYB and AML (regulators want to see the underlying public-source record, not a derived dataset). When a regulator asks "where did this ownership data come from?", "Moody's Orbis says so" is a weaker answer than "INPI filing dated 12 March 2025, here's the URL."

The pricing model isn't built for the AI use case. Moody's licences are tied to seat counts, AUM, or lookup volumes. Running an AI agent that performs thousands of automated lookups per day against a per-lookup-priced contract is a fast way to blow your data budget. Registry-direct providers like Global Database are built for high-volume programmatic access — that's the entire point of having an MCP server.

This is why mature finance teams almost always stack: Moody's for ratings and credit overlay, Global Database for the registry-direct compliance layer, FactSet or Capital IQ for the research layer. The question is never which one — it's which one anchors which workflow, and where you avoid double-paying for overlapping coverage.

If your problem is "private companies and global registries"

Most institutional data providers are public-market centric or have shallow private-company coverage outside the US. Registry-direct providers like Global Database fill this gap because they connect to the actual national registriesKVK in the Netherlands, the Delaware Division of Corporations, INPI in France, and on — rather than reselling curated aggregates.

Use Global Database with Claude
Three ways teams use registry-direct data with AI

The same first-party data, three deployment patterns that other providers on this list don't all allow.

Pattern 01
MCP server for Claude

Add Global Database as a custom MCP connector in Claude Code, Desktop, or Enterprise. Five tools: profile, financials, shareholders & UBO, group structure, identifier lookup. Every response carries source attribution and a timestamp.

Pattern 02
AI training rights

Use registry-direct data to fine-tune or ground proprietary models. Unlike Bloomberg, Moody's, or FactSet — whose terms restrict AI training — Global Database licenses include AI training rights by default.

Pattern 03
Reseller & embed rights

Build the data into a customer-facing product or resell it as part of your own AI offering. Full reseller rights are available, with no per-end-user metering. Standard with most providers it isn't.

Frequently asked questions

What is the best financial data provider for Claude in 2026?

There is no single best provider — the right choice depends on the workflow. For KYB, AML, and compliance work, registry-sourced providers like Global Database give the auditability and provenance regulators require. For credit and risk, Moody's offers the deepest native Claude integration with ratings and 600M+ entities. For private-company financial statements at scale, MonetaiQ covers 400M+ private firms. For equity research and modelling, FactSet and S&P Capital IQ are the institutional defaults. For entity resolution and procurement, Dun & Bradstreet's D-U-N-S identifier remains the standard. For real-time markets data inside Claude, LSEG has the deepest official integration. Most enterprise teams use a combination.

Which financial data providers have official Claude MCP integrations?

As of mid-2026, the confirmed Claude for Financial Services data partners include FactSet, S&P Global (Capital IQ via Kensho), Moody's, LSEG, Morningstar, PitchBook, Daloopa, MSCI, Chronograph, Aiera, Third Bridge, MT Newswires, Egnyte, Box, Databricks, Snowflake, and — added in May 2026 — Dun & Bradstreet, Verisk, Fiscal AI, Experian, GLG, Guidepoint, and IBISWorld. Global Database is available as a custom MCP connector for KYB and registry data.

What is Claude for Financial Services?

Claude for Financial Services is Anthropic's vertical solution for institutional finance, launched in July 2025 and expanded with a full agent suite in May 2026. It packages Claude with pre-built data connectors, agent templates for banking, asset management, and insurance workflows, and the MCP integrations with FactSet, Moody's, LSEG, S&P Global, and others. It is available on Claude Enterprise and Team plans. The underlying model in mid-2026 is Claude Opus 4.7, which leads the Vals AI Finance Agent benchmark at 64.4% accuracy.

What is the difference between a Claude MCP connector and a native MCP app?

A connector gives Claude governed, real-time access to a provider's data through tool calls — Claude reasons over the data and renders the answer itself. A native MCP app goes a step further: it embeds the provider's own interactive UI directly inside Claude, so users see the provider's workflow surfaces (charts, screens, forms) rendered natively in the Claude interface. Moody's Agentic Solutions is the most prominent example of a native MCP app in financial services.

Is Global Database an alternative to Moody's or Dun & Bradstreet?

Global Database overlaps with Moody's and D&B on company profiles, ownership, and financials, but the underlying sourcing model is different. Global Database connects directly to 400 official government registries covering 600M+ companies; Moody's and D&B are curated, aggregated datasets layered with proprietary identifiers and ratings. For compliance and KYB workflows where regulators expect to see registry-direct provenance, Global Database is typically used as the primary source. For credit ratings or D-U-N-S-based vendor identity, Moody's and D&B remain the standards.

Which is better for KYB: Global Database or Moody's?

For pure KYB onboarding and AML compliance, Global Database is typically the better anchor — because regulators want to see the underlying public-source record, not a curated overlay. Global Database's data carries source attribution and timestamps from each of the 400 government registries it connects to, which is what makes outputs defensible in an audit. Moody's is the better anchor when the workflow extends from KYB into credit assessment, sanctions/adverse media monitoring, or ongoing risk scoring — areas where Moody's curated overlay (ratings, KYC integration, ownership modelling) adds value beyond raw registry data. Many enterprise teams use both: Global Database as the primary KYB source, Moody's as the credit and adverse-media layer.

What is the Kensho MCP server, and how does it relate to S&P Capital IQ?

Kensho is S&P Global's AI innovation arm. It built the MCP server that exposes the Kensho LLM-ready API to Claude. That API in turn provides access to S&P Capital IQ Financials, earnings call transcripts, and other S&P Global datasets. In practical terms, the "S&P Capital IQ" connector inside Claude is the Kensho MCP server.

Can I use Claude with financial data without an enterprise plan?

Some integrations are limited to Claude for Financial Services and the Enterprise tier — particularly Anthropic's first-party finance solutions and partners like LSEG and Moody's. However, third-party MCP servers (including Global Database) can typically be added as custom connectors to Claude Pro, Max, Team, and Enterprise plans, subject to the provider's own licensing terms. Always check the specific connector's documentation.

How does Claude prevent hallucinations when using financial data?

Anthropic's approach is grounding through tool calls: Claude calls an MCP server, receives structured data, and is expected to reason over and cite that data rather than invent it. Anthropic provides hyperlinks back to source materials, and providers like Moody's emphasise "valid, explainable, and auditable" outputs. That said, hallucinations are not eliminated — they are reduced. For regulator-facing workflows, the recommended pattern remains: registry-direct data, source-attributed outputs, and human sign-off before any decision is finalised.

What financial data is available through Claude in Excel?

Claude for Excel is available in beta for Max, Enterprise, and Teams users and supports MCP connectors including S&P Global and other financial-services partners. Within Excel, Claude can read, analyse, and modify workbooks, populate templates with data from connected providers, build comparable-company models, run DCFs, and refresh data through the connector layer.

Can Claude replace Bloomberg Terminal?

Not for what Bloomberg does best. Bloomberg's depth on fixed income, options pricing, FX, and the Bloomberg Instant Message network is unmatched, and Bloomberg has no official Claude MCP integration as of mid-2026. What Claude does replace, when paired with the right data partners, is the analytical work that happens around the terminal: building DCF models (FactSet, Capital IQ via MCP), screening companies for KYB (Global Database), pulling private-company financials (MonetaiQ), summarising earnings calls (Capital IQ transcripts), and querying live markets data (LSEG MCP server). For trading-desk workflows that depend on Bloomberg's specific data and messaging, Bloomberg is irreplaceable. For research, modelling, and diligence workflows, an LSEG + FactSet + Global Database stack inside Claude is increasingly competitive — and is the institutional alternative most large firms are now testing.

How do I connect MonetaiQ's MCP server to Claude?

MonetaiQ provides an MCP server for direct integration with Claude and other MCP-compatible clients (including Claude Code, Cursor, and Aider). Setup follows the standard pattern: add it as a custom MCP connector in Claude Settings, authenticate with your MonetaiQ API token, and the tools become available in any chat. For specific endpoint URLs, authentication flow, and tool definitions, contact MonetaiQ directly — the public website lists the API documentation but the MCP-specific setup details are typically shared during onboarding. The same pattern applies to Global Database's MCP server for KYB and registry data.

Is Global Database GDPR compliant, and where is the data hosted?

Yes. Global Database is GDPR compliant. Data is cached and processed on Global Database's own infrastructure hosted in Germany (Hetzner), within the EU. Every record carries source attribution back to the originating government registry, which makes it suitable for regulator-facing KYB and AML workflows where data lineage must be demonstrable. UK GDPR equivalence applies for UK-based clients. Data processing agreements are available on request.

Is FactSet better than S&P Capital IQ for Claude workflows?

They are not directly substitutable. FactSet's strength is breadth of fundamentals, analyst estimates, and the analytics stack used by sell-side and buy-side analysts. S&P Capital IQ's strength is the curated tear sheets, earnings transcripts, and M&A transactions data that have historically defined Capital IQ Pro. Many institutions license both. For a Claude workflow, the question is which platform your team's existing analytical processes already run on.

How much does it cost to add a financial data MCP connector to Claude?

The Claude connector itself is included with the relevant Claude plan. The cost is the underlying data licence from the provider — and that varies enormously. Enterprise data licences from Moody's, FactSet, or S&P Global typically start in the tens of thousands of dollars per year for institutional users and scale with seats and data scope. Global Database is licence-based, billed annually, with pricing varying by coverage (industry, country, region), seats, and feature mix, and offers a free trial of 100 requests for evaluation.

Ready to make the move?
Add registry-direct data to your Claude workflow

Global Database's MCP server gives Claude auditable, government-sourced company intelligence on 600M+ companies across 200+ countries — with source attribution and timestamps on every field, AI training rights, and reseller rights included.

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