When to MCP, When to Not

GovCons are adopting LLMs fast. Teams are building their BD and proposal processes around apps like ChatGPT, Claude and Gemini, and it's never been easier to connect those apps to the rest of your systems. With an MCP, the AI can search SAM.gov, read your inbox, update your CRM or pull a past performance write-up from a shared drive, all from a plain-language request.

But connecting a system is only the first decision. Every MCP call has a cost, and every connection decides what data the AI can see and where that data goes. GovCons need to understand both before they build a process on top of them.

Here's what using an MCP for BD and proposal work looks like, where it falls short, and what it takes to get data you can rely on.

How an MCP works

An MCP, short for Model Context Protocol, is a standard way to connect an LLM app to your other systems. Once a system is connected, you ask for what you want in plain language. The model decides which tool to use, what to ask for and what to do with the answer.

That's what makes MCPs easy to set up. It also means the model is in charge of every piece of data you pull.

What it looks like in practice

For finding and qualifying, a team might connect a SAM.gov MCP and ask for new notices in its NAICS codes, then ask which ones fit its past performance. The same connection can pull an agency's award history, so the AI can explain how that agency buys: who holds the work, what it spends by NAICS and which contracts are ending soon.

To manage opportunities and relationships, the team connects email, calendar and meeting transcripts. The AI can then sum up what's happened on an opportunity, who the team has talked to at the agency and what was said. That feeds next steps, capture strategy and updates to the CRM.

For proposals, the team connects the shared drive where its library lives. The AI pulls past performance, résumés and sections from past proposals to draft a response.

If you work this way, connect only the folders and mailboxes each task needs, pull specific documents instead of whole folders, and give read-only access unless the AI has to write.

Where relying on an MCP falls short

Every one of those pulls depends on the model. That shows up in five places.

Cost. Everything an MCP returns runs through the model, and you pay for every token, whether the data was useful or not. The app also loads a description of every connected tool before you ask anything. Anthropic, the company that created MCP, showed how much this adds up: in one example, filtering data in code before it reached the model cut token use from 150,000 to 2,000.

Completeness. The model decides how many results to pull and when it has found enough. A search for new notices can stop at the first page, and an award history can come back as a sample instead of the full record. Some sources, like GSA eBuy and SEWP, have no public API to connect to at all.

Consistency. The same question can come back with different results from one run to the next. That makes it hard to compare this week's pipeline to last week's, or to trust a pattern in award data.

Security. A connection can usually see everything in that system, not just what the task needs. An email or document with hidden instructions can also steer the AI into misusing its other connections, a risk called prompt injection. In May 2026, the NSA's AI Security Center warned that MCP security hasn't kept pace with how fast it's being adopted. And CUI should never go into a tool that isn't approved for it.

What happens to the data. It lands in a chat as text. It isn't saved as an opportunity record, linked to the agency or contacts, or shared with your team. The next time you need it, you pull it again and pay for it again.

Mapped integrations give you data you can depend on

A purpose-built solution gets the same data another way. It uses integrations built for each source, like APIs, data feeds and email alerts, and pulls and filters the data in code before the AI sees it. That costs far less, because sorting doesn't use tokens. It's more complete, because every notice and award comes in, not just what fits in one response. And it runs the same way every time.

The integrations are also mapped to the fields BD work depends on: the opportunity, agency, NAICS code, contract and contact. So the data comes in as connected records instead of text in a chat. Your team can compare opportunities side by side and track how an agency buys over time, and the AI gets the full, correct picture for every task.

How GrowthStudio puts the data to work

GrowthStudio is built on mapped integrations, and the data they bring in feeds every part of BD and proposal work.

For finding and qualifying, GrowthStudio pulls opportunities from federal and state sources, including SAM.gov. It brings in GSA eBuy through email forwarding and browser automation, and each notice arrives with its supporting documents. Your relevance profiles, built from your past performance, score every opportunity. Award history is tracked across every agency, down to how an agency has awarded by NAICS for the opportunity you're pursuing, and it sits on the opportunity next to the AI summary, contacts and agency details.

For managing opportunities and relationships, the Outlook integration attaches emails to the right contacts and pulls real interaction data from your calendar. The relationship map shows your people next to the agency's people, with signals on each relationship, so your capture strategy is based on what actually happened.

For proposals, the solicitation and everything from capture carry straight into the proposal. Your reference library is tagged by document type, contents and bottom line up front (BLUF), so each section pulls the past performance closest to the work you're bidding on.

Unanet builds and maintains the integrations. AI use is included in GrowthStudio's fixed price, no matter how many opportunities you pursue, bids you submit or analyses you run. GrowthStudio has achieved FedRAMP Moderate Equivalency, every customer gets its own private cloud instance, and Unanet doesn't train AI on customer data.

The bottom line

MCPs make it easy to connect an LLM to your systems. But relying on them for BD means paying the model to sort data, getting partial and inconsistent results, and starting over in every chat. Mapped integrations bring in the data GovCons need for less, more completely, and as records your team and the AI can depend on. That's how GrowthStudio is built.