Optimizing Latency with MCP Caching

I recently spent a frustrating afternoon debugging a support bot built on top of LangGraph, where the bot would occasionally take an inordinate amount of time to respond to user queries. The bot's workflow involved multiple tool calls to external services, such as entity disambiguation and sentiment analysis, which were implemented using the Model Context Protocol (MCP). After digging into the logs, I noticed that the bot was spending a significant amount of time waiting for these tool calls to complete, even when the input data was identical to previous requests. This was causing the bot's overall latency to spike, leading to a poor user experience.

The root cause of the issue was that the bot was not leveraging MCP's caching mechanisms for frequent tool calls. Every time the bot needed to make a tool call, it would create a new request and wait for the response, even if the same request had been made before. This was not only inefficient but also unnecessary, as the results of these tool calls were often deterministic and didn't change between requests.

To address this issue, I started by identifying the most frequently called tools in the bot's workflow and configuring MCP to cache their results. This involved adding a cache layer on top of the tool calls, which would store the results of previous requests and return them immediately if the same request was made again. I used the MCP_CACHE environment variable to enable caching for the specific tools that needed it.

Here's an example of how I implemented caching for a sentiment analysis tool using MCP's caching mechanisms:

import os
import langgraph as lg
from langgraph.tools import MCPTool

# Enable caching for the sentiment analysis tool
os.environ['MCP_CACHE'] = 'sentiment_analysis_tool'

# Define the sentiment analysis tool
class SentimentAnalysisTool(MCPTool):
    def __init__(self):
        super().__init__('sentiment_analysis_tool')

    def run(self, input_text):
        # Make the tool call and cache the result
        cached_result = self.cache.get(input_text)
        if cached_result is not None:
            return cached_result

        # If the result is not cached, make the tool call and store the result
        result = self.make_tool_call(input_text)
        self.cache.set(input_text, result)
        return result

# Create a LangGraph workflow that uses the sentiment analysis tool
workflow = lg.StateGraph()
sentiment_tool = SentimentAnalysisTool()

# Add a node to the workflow that uses the sentiment analysis tool
workflow.add_node('sentiment_analysis', sentiment_tool.run)

# Add conditional edges to the workflow based on the sentiment analysis result
workflow.add_conditional_edges('sentiment_analysis', [
    (lambda x: x > 0.5, 'positive_sentiment'),
    (lambda x: x < -0.5, 'negative_sentiment')
])

# Run the workflow and print the result
result = workflow.run('This is a great product!')
print(result)

By leveraging MCP's caching mechanisms, I was able to significantly reduce the latency of the support bot and improve the overall user experience. One practical gotcha to watch out for when implementing caching is to ensure that the cache is properly invalidated when the underlying data changes. In this case, I made sure to invalidate the cache whenever the sentiment analysis model was updated or retrained.

As we continue to build more complex agentic systems, optimizing latency and performance will become increasingly important. Tomorrow, we'll explore another critical aspect of building scalable agentic systems, and how to apply the lessons learned from this experience to tackle even more challenging problems.