Remember the frustration of connecting devices? We had a tangle of HDMI, VGA, USB-A, USB-B, and micro-USB cables, each with its own shape and quirks. It was clunky and inefficient until USB-C arrived in 2014 and changed everything: suddenly, connections became simple and seamless.
Enterprise AI today faces a similar challenge. Businesses aren’t struggling with a lack of data or tools. The real problem lies in stitching siloed data together, so AI agents can act with context and intelligence.
To bypass these integration challenges, Model Context Protocol (MCP), an open standard and open source framework by Anthropic, was introduced in 2024. Think of it as the USB-C port for AI agents. It standardizes tool integration and enables agents to take actions.
But, MCP alone isn’t enough for enterprise-scale complexity. While you get to data and tools, the true business value lies in understanding context, correlating scattered information, and turning knowledge into intelligent action.
This raises critical questions every enterprise must ask about its AI agents and systems:
- Can your agents understand context, not just pull raw facts?
- Can they correlate data across silos and surface the right insights securely?
- Can they scale as information grows more complex?
In this blog, you will learn why connecting data isn’t enough and why true enterprise AI needs context, insight, and intelligence.
What MCP gets right
APIs allow you to connect tools seamlessly, but interfacing with each tool via custom code is time-consuming and error-prone. Companies, on the other hand, are looking for a low-code way to talk to tools.
That’s where MCP comes in: a universal adapter that lets AI agents access tools without custom glue code.
Think about the last time you searched for flights online: juggling tabs, dates, filters, and price graphs. Now imagine if you could just ask in plain English and get curated options instantly, without hopping between sites.
That’s what AI orchestration can look like.
For example, Google’s Flight Deals (beta) shows how natural-language search can simplify complex tasks: users simply have to type “I want to go on a 10-day ski trip to a resort” or “Week-long trip this winter to a city with great food, nonstop only,” and instantly see the best matches.
In a similar way, MCP enables AI agents to coordinate across multiple systems in real time—calling APIs, retrieving data, and using LLMs to rank and refine the results—all through a single, standardized interface.
But why is MCP alone not enough when it comes to enterprise intelligence?
Where MCP falls short (and why)
While MCP excels in connecting tools without a custom code and performing simple tasks, such as “create a ticket” or “send a message, " enterprise AI needs more than just access.
An AI agent that understands the context, tracks history, recognizes relationships, and reasons across multiple sources. Here’s where MCP alone may not suffice:
1. System fragmentation makes reasoning hard
Although MCP connects tools, it still treats each system in isolation. Data pulled from a CRM has no inherent connection to a bug in a project management tool or a conversation in a support platform.
Say you’re trying to assess how a bug impacted high-revenue customers and how support responded. The data exists, but it’s scattered. You’d need to manually stitch together CRM data, ticket logs, sentiment, and release notes.
2. No built-in memory or correlation
MCP connects the client and server for a continuous, seesawing information exchange. However, it doesn’t hold long-term memory or context across queries. Each query is taken like a blank slate; it starts from zero and fetches information from past queries, tracks patterns, or builds a historical view of the customer.
For example, MCP can tell you “these tickets are open,” but it can’t answer:
- Has this issue come up before?
- What’s the customer’s sentiment over the last 6 months?
- Which tickets are related to the same bug or high-priority customer?
The limitation isn’t access; MCP can fetch data, old or new, if the API exposes it. The gap is context and correlation. Enterprise intelligence requires systems that recognize patterns, connect the dots, and deliver insights, not just isolated facts.
3. Latency adds up fast
Every time MCP answers a query, it makes live API calls to the connected tools. On its own, a single call might only add a fraction of a second. However, when the question touches 8+ tools: CRM, support tickets, feedback repositories, and internal docs, those delays stack quickly.
Let’s say that tool A takes 200 ms, tool B takes 300 ms, and tool C takes 500 ms. Individually, they seem small. Together, they create noticeable lag. For workflows where seconds matter, like incident response or churn prediction, that lag is costly.
4. Token costs can add up
Although MCP itself doesn’t consume tokens to connect to APIs and fetch information, the cost begins when the fetched data is sent to an LLM. Summarizing unstructured inputs, reasoning across systems, and prioritizing results are all LLM tasks, and that’s where tokens add up.
Since MCP doesn’t cache or pre-index, each query restarts the process. Agents may also retry for failed responses, iterate to refine answers, or run background fetches for added context. Barclays analysts estimated that enterprise-grade AI queries can consume up to 10,000 tokens per query, which is 25 times that of chatbots, significantly increasing reasoning costs.
5. Lack of data ranking
While MCP fetches data in real time, it doesn’t rank, correlate, or normalize the results it retrieves. When an agent queries five different systems, say Confluence, Salesforce, Jira, Asana, and Intercom, each returns results using its own internal logic, with no shared notion of relevance or priority.
A single multi-system query, such as “What were the top complaints about the mobile app last quarter?” triggers parallel API calls. The overall response time is then gated by the slowest endpoint, and the agent receives a mix of unranked outputs from each tool.
This is where things get tricky. Ranking and correlation become your responsibility. You can solve it either by building specialized layers into the architecture, which is complex. Or you can delegate ranking to an LLM, which can introduce inconsistency, higher latency, and inefficiency at scale.
6. Unstructured data isn’t processed
MCP works best with structured fields, form-based data, and even unstructured data as long as the APIs expose it. However, it doesn’t parse, normalize, or structure the unstructured input. That burden falls to downstream components, such as LLMs, increasing cost and variability.
This matters because 90% of enterprise knowledge lives in messy, human formats: email threads, tickets, transcripts, and product notes. MCP passes this data through “as-is,” leaving the heavy lifting—extracting nuance, tone, patterns, or intent—to the LLM, which adds up token costs.
These gaps aren’t about lack of access; they’re about lack of enterprise intelligence.
Computer Memory: the missing link in enterprise AI
Unlike API calls that fetch isolated fragments in real time, DevRev’s patented technologies, Computer AirSync and Computer Memory, eliminate the headache of connecting to multiple data sources. It turns fragmented data into structured, mapped, contextual, and relational knowledge for AI to reason with. This context-rich structure allows AI agents to move from searching to truly reasoning.
Here’s how Computer Memory fills the gap:
- Keeps context alive: So agents don’t start from zero with every query.
- Speeds up response: Reduces latency with pre-indexed insights.
- Makes sense of mess: Parses long, human inputs from emails, chats, and notes.
- Ranks what matters: Prioritizes answers based on revenue, urgency, or sentiment.
- Protects data: Offers permission-aware insights by design.
It doesn’t replace MCP, but completes it.
Enterprise intelligence meets MCP
Most vendors stop at access, letting AI agents call APIs or schedule meetings. But intelligent actions require understanding:
- Which tickets are related to a bug
- Which customer matters more
- Who owns what across CRM, project management tool, and support ticket platform?
For those actions to be smart, you need a strong data foundation. DevRev’s Computer Memory, together with MCP, makes the agentic workflows faster, more relevant, and secure.
Here’s how:
1. A unified foundation: AirSync + Computer Memory
While MCP connects tools, AirSync, a bidirectional sync engine, syncs every system, doc, and conversation in real time. The data isn’t just moved; it’s cleaned, ranked, synced, and made permission-aware before living inside Computer Memory.
Unlike static databases, this living network is built around what matters most: your products and customers. Thus, your agents never have to start from zero with every query. Instead, they work with full context from the start without waiting on multiple live API calls.
2. DevRev connecting to other MCP servers
Computer doesn’t just connect to MCP servers; it orchestrates them.
It uses DevRev’s Memory to understand context, relationships, and history, and then uses MCP to take real actions across external apps and services. So you get:
- Deep context from the Computer Memory
- Open, flexible execution through MCP integrations
So enterprises can bring all their intelligence together in one place, while still acting seamlessly across their entire ecosystem.
3. DevRev as MCP server
We also expose Computer Memory, as an MCP-compatible server, so AI assistants like Claude can:
- Search across CRM, project management tool, support tickets, conversation platform, and Drive
- Get context-aware results
- Do it all securely and fast
Your agent can query one place and understand everything.
Just like USB-C changed how we connect devices, DevRev is redefining how enterprise systems connect, search, and act with context, intelligence, and speed.
Experience how Computer by DevRev brings intelligence to every interaction. Get in touch.
