Short answer
Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that connects AI assistants like Claude to business systems through small programs called MCP servers. For a private equity (PE) finance team, an MCP server can give AI read access to the deal CRM (such as DealCloud), licensed data from providers like PitchBook, Crunchbase, and Fitch, internal drives, and portfolio KPI data, so the AI can pull pipeline and draft memo sections with cited figures. Lattice Partners is an AI implementation partner based in Los Angeles and New York that helps companies build an AI strategy, trains their teams on AI tools, and builds custom agents and software, including MCP integrations for finance teams.
- With MCP, each system needs one connector instead of a custom integration per AI product.
- Third-party data access depends on each provider's API license. Check the terms before connecting anything.
- Start read-only, use scoped credentials per user, and log every call.
- MCP also lets you give the AI your firm's own definitions, so it uses your terms for metrics, sectors, and deal stages.
What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard for building two-way connections between AI applications and the systems where data lives. Anthropic announced and open-sourced MCP on November 25, 2024, with prebuilt servers for systems like Google Drive, Slack, and GitHub (TechCrunch).
There are two sides to the setup. An MCP server exposes a system's data and actions, such as "search the pipeline" or "get company financials," and an MCP client like Claude calls those servers when a user asks a question. Because the standard is open, other AI vendors' tools can use the same servers.
The glossary entry for MCP has a shorter definition.
What does an MCP server for a PE firm connect to?
An MCP server for a PE firm usually connects four kinds of systems: the deal CRM, market and credit data providers, internal file stores, and portfolio KPI data. Each connection exposes a small set of named tools the AI can call.
- Deal CRM. DealCloud or a similar system holds pipeline, contacts, notes, and stage history, exposed through searches like "deals in diligence in industrials."
- Market data providers. PitchBook says its API lets customers call specific PitchBook data into internal business systems on demand (PitchBook). Crunchbase's API documentation describes read-only access to the data behind crunchbase.com, with a key required for every call and access tied to the license tier (Crunchbase).
- Ratings agencies. Fitch Solutions offers API access to its data and flat-file feeds for loading into a client's own database (Fitch). An MCP server can sit on top of whichever channel your license covers.
- Internal drives and portfolio data. Past memos, models, data room exports, and monthly KPI packages from portfolio companies.
Many data contracts restrict redistribution or use in AI tools, so confirm the terms with each provider before you connect.
How firms use these connections across the deal lifecycle is covered in AI for private equity firms.
What tasks can AI do once it's connected through MCP?
Once AI is connected through MCP, it can do multi-step finance tasks that would otherwise mean switching between several systems. The user asks in plain language and the AI calls the right tools.
| Task | Systems called | Output |
|---|---|---|
| Pull the pipeline for Monday's deal meeting | CRM | A list of active deals by stage with last activity and owner |
| Enrich a new target | CRM, Crunchbase or PitchBook | Company profile, funding history, and comparable companies, written back to a draft record for review |
| Draft a memo section with cited data | CRM, data provider, internal drive | Market and company section, with each figure linked to its source |
| Prepare a portfolio KPI update | Portfolio KPI files, budget files | Variance table and draft commentary for the operating partner |
"Once Claude or ChatGPT can pull straight from your CRM or your Snowflake, you understand your business faster and spend less time checking spreadsheets."
Martin Anquetil, Co-Founder and Managing Partner, Lattice Partners
Memo and board pack workflows built on these connections are covered in AI tools for investment memos and board materials.
Which vendors provide AI that understands company-specific terms?
AI understands company-specific terms when it's given the firm's own definitions as context, and MCP is one of the cleanest ways to supply them. Which model vendor you pick matters less than whether the AI can see your KPI definitions and sector taxonomy.
A PE firm's MCP server can expose a definitions resource: what "adjusted EBITDA" means in your models, how you define each sector, what each CRM deal stage means, and how portfolio companies map their line items to your KPIs. The AI reads those definitions before it answers, so "show me platform deals in business services" returns what your team means by those words. General assistants can do a lighter version with uploaded glossaries, but an MCP server keeps the definitions in one maintained place.
How do you keep an MCP integration secure?
You keep an MCP integration secure by starting read-only, scoping credentials to each user's existing permissions, logging every call, and respecting each data provider's license. An MCP server lets an AI model read from your production systems, so it deserves the same security review as any other production integration before it touches live data.
- Read-only first. Add write access later, as drafts a person approves.
- Scoped credentials. Each user connects with their own account, so the AI sees only what that person can already see in DealCloud or the shared drive.
- Audit logs. Record which user called which tool, with what inputs, and when.
- Data licensing. Keep provider data inside the uses your contracts allow, and don't cache licensed data longer than the terms permit.
How do you build an MCP integration for a finance team?
A first MCP integration for a finance team usually takes weeks, starting with one system and a few read-only tools. Each system you add after that goes faster, since the logging and credential setup already exist.
| Step | What happens | Who's involved |
|---|---|---|
| 1. Pick the workflow | Choose one high-value task, like pipeline review or target enrichment | Deal team lead, builder |
| 2. Inventory systems and licenses | List the systems the task needs and confirm API access and data terms | Operations, compliance, data providers |
| 3. Define tools | Write the small set of named actions the AI can call, with inputs and outputs | Builder, analysts |
| 4. Build read-only server | Connect the APIs with scoped credentials and logging | Builder |
| 5. Test with real questions | Analysts run real tasks and flag wrong or missing results | Analysts, builder |
| 6. Train and roll out | Hands-on sessions using the team's own deals | Builder, deal team |
| 7. Add systems and writes | Add the next data source, then draft-only write actions | Builder, compliance |
For timelines, see how long custom AI software takes to build.
How Lattice Partners does this
Lattice Partners builds MCP integrations for PE firms and finance teams that connect Claude and other AI assistants to CRMs like DealCloud, licensed data providers, internal databases, and shared drives. We start with Learn, mapping the finance workflows and data contracts, and we'll say so if an off-the-shelf connector already covers the need. Tailor shapes the tool set around the firm's permissions and the terms its team uses. A read-only first version usually ships within a few weeks of Build and Ship, and we train the team hands-on with their own pipeline and documents. From there, Iterate adds systems and actions based on what people use; most products go through two to three iterations.
The integration is a one-time build and the firm owns the code. There's no recurring per-seat or per-module license from us, and we can stay on after launch to support and improve it.
Frequently asked questions
What does MCP stand for?
MCP stands for Model Context Protocol. It's an open standard, introduced by Anthropic in November 2024, for connecting AI assistants to data sources and business tools.
Can Claude connect to DealCloud?
Claude can connect to DealCloud through an MCP server that calls DealCloud's API with the user's own credentials. The firm controls which records and actions the server exposes.
Can AI tools integrate with Crunchbase or Fitch Ratings data?
AI tools can integrate with Crunchbase and Fitch data through each provider's API or data feed, wrapped in an MCP server. Access depends on the firm's license with each provider, so confirm that AI use is allowed under the contract first.
Is MCP only for Anthropic's Claude?
MCP isn't limited to Claude. The protocol is open, OpenAI and Google DeepMind have adopted it, and it's now governed under the Linux Foundation's Agentic AI Foundation.
Related reading
About Lattice Partners
Lattice Partners is an AI implementation partner based in Los Angeles and New York. We help companies build an AI strategy, train their employees on AI tools like ChatGPT and Claude, and build custom AI agents and software. Clients own the code we build. Lattice Partners was founded by Colin McDonnell, Martin Anquetil, and Christian Yeghnazar. Contact us to talk about your use case.