How to decide between syncing and MCP for enterprise search

As you build an enterprise search platform, you’ll have two options for implementing it.

You can store and index the data (i.e., perform syncs). This involves copying content from each source into your own search index on a schedule.

And you can use the Model Context Protocol (MCP), or a live connection, which requires calling the source system’s API every time a user searches.

The two methods solve different problems. A stored index is extremely fast and supports rich ranking and analytics; while MCP results are always current but are limited by the source API, network latency, and rate limits.

We’ll further explore the pitfalls of MCP for enterprise search, the situations that still call for it, and when (and why) syncs often work better.

Considerations in using MCP for enterprise search

While MCP can provide real-time data, it faces multiple structural challenges that make it slower and less reliable than pre-synced data. 

Let’s review MCP’s drawbacks in several areas.

Semantic search

Semantic search, which aims to understand the intent of a user’s search (versus string-matching), is now a standard expectation for enterprise search. 

MCP can’t adopt it because the APIs that support search will only support exact or fuzzy string-matching. In addition, without full syncs, you won’t be able to generate and update embeddings in real time because of rate limits and cost constraints.

For example, if a user searches “company policy vocation,” MCP would simply correct the typo and return a “company policy vacation” document. Since it won’t surface and reference information from related documents, the user’s question will likely remain unanswered, forcing them to run additional searches or ask colleagues for help.

Latency

You would expect that MCP outperforms syncs here, but the reality plays out differently. 

If you’re using MCP to search for the “Q4 revenue forecast” in SharePoint, for example, each /search/query could return 120 matches in ~500 ms. However, each file then requires a separate download (~400 ms each, 10 at a time due to rate limits). This can bring the total sync time to ~25–30 seconds before processing even starts. 

In short, MCP latency compounds with every file, while pre-synced data delivers instant results regardless of dataset size.

Rate limits

If you’re making complex MCP searches for Box, you can quickly hit the 100K monthly request cap. And with the “uncapped spend” option, overages can generate unexpected charges for customers, which is impossible with pre-synced data.

Similarly, SharePoint has a rate limit of 3K requests per user per 5 minutes, but practical limits are often about 1,800 to 2,100 requests. A few large MCP searches can hit that ceiling fast, throttling performance. Pre-sync avoids consuming limits on each users’ individual queries and allows you to optimize the limit.

These are just a few examples. There are countless others that make MCP impractical for enterprise search at scale. 

Syncing and indexing, while slightly less fresh, delivers sub-second search latency, robust queries, and scalability for millions of documents and heavy aggregations.

Choosing the right approach for each query

Despite its issues, MCP can still be useful for enterprise search. 

Here are scenarios that call for MCP and others that call for syncs.

Queries that are suited for MCP

• Check whether a purchase order is approved
• Retrieve the latest status of a support ticket 
• Show today's GitHub commits for a repository 
• Get the current PTO balance for an employee 
• Fetch the last message posted in a Slack channel 

Queries that require sync

Since these queries can be more wide ranging, we broke them down by job titles.

CRO

• Show which AEs have the longest average response time to prospect inquiries
• Surface common objections in lost deals from enterprise prospects this quarter
• Identify deals stuck in the same stage for more than thirty days with no recent communication

Customer Success Manager

• List all customer conversations about authentication issues in the last week
• Compare sentiment in support tickets before and after the latest release
• Find documentation gaps based on common support questions
• Generate a trend report of feature requests mentioned in customer meetings this quarter

CISO

• Locate every file shared externally that contains PHI or PII across Drive, Dropbox, and Notion
• Show open vulnerability tickets filed in the last ninety days across Jira and GitHub
• Audit accounts for employees who left the company in the last month but still have active access

Product Manager

• Aggregate user feedback about onboarding friction across Intercom and Slack from the past thirty days
• Rank the top feature requests by frequency and customer spend
• Summarize bug reports linked to the latest release and group them by severity

Finance Analyst

• Consolidate invoices over ten thousand dollars across NetSuite and QuickBooks for the last quarter
• Compare forecasted versus actual spend by vendor and highlight variances
• List contracts expiring next quarter along with renewal terms

As a general rule of thumb, aim to keep your main search experience on a stored index and reserve live calls for the few look‑ups that have to be current. 

To help you pick the right approach for the queries your enterprise search would support, you can also reference this table:

How Merge makes syncs easy

Building sync connectors in house means learning dozens of auth flows, pagination schemes, and edge cases. 

Merge handles that for you by providing:

• Unified schemas that let you treat individual file storage, knowledge base, and communication systems the same way
• The ability to scope syncs to exactly the content customers want to make available
• Bulk endpoints and webhooks to keep your index fresh without hammering vendor APIs
• Built‑in retries and backoffs to prevent rate‑limit errors
• Normalized permissions that help you mirror the source ACL model

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