Optimized MCPs with Unified Code Mode

Try it yourself! This example is available as an executable Jupyter notebook.

This example demonstrates building an optimized MCP gateway using pctx (Port of Context). The pctx runtime aggregates multiple MCP servers into a single endpoint, exposing them through a powerful "Code Mode" interface that reduces token usage by up to 98%.

Understanding Code Mode

With traditional MCP, each tool call requires an LLM round-trip, which means expensive tokens with context flying in and out:

LLM: "I'll call add(42, 8)"      -> Tool executes -> Result
LLM: "Now I'll call multiply"    -> Tool executes -> Result
LLM: "Now I'll call power"       -> Tool executes -> Result
LLM: "Now I'll call uppercase"   -> Tool executes -> Result
LLM: "Now I'll call word count"   -> Tool executes -> Result
LLM: "Here's the final answer"

With Code Mode, the agent writes TypeScript that executes all tools in one call which means we save significant amount of tokens and time:

LLM: "I'll call the following set of mcpservers in the code mode execution sandbox:

    '''
        const sum = await calc.add({a: 42, b: 8});
        const product = await calc.multiply({x: sum, y: 2});
        const squared = await calc.power({base: product, exponent: 2});
        const wordCount = await text.word_count({text: "The answer is " + squared});
        const result = await text.uppercase({text: "result: " + squared + " (words: " + wordCount + ")"});
        return result;
    '''
        -> Tool executes -> Result
LLM: "Here's the final answer"

This reduces:

• LLM round-trips: From N+1 to 2 (code generation + final response)
• Token usage: Up to 98% reduction for complex workflows
• Latency: Dramatically faster for multi-step operations

Architecture

Why pctx?

pctx aggregates multiple MCP servers and exposes them through TypeScript namespaces. Each server becomes a namespace (e.g., calc.add(), text.uppercase()), enabling complex multi-tool orchestration in a single code block.

The pctx config supports:

• Multiple servers: Aggregate any number of MCP servers
• Custom namespaces: Server names become TypeScript namespaces
• Authentication: Add bearer tokens or custom headers
• External servers: Reference any HTTP MCP endpoint

Prerequisites

• KAOS operator installed (Installation Guide)
• kaos-cli installed
• Access to a Kubernetes cluster

Setup

First, let's set up the environment and create a namespace:

import os
os.environ['NAMESPACE'] = 'pctx-gateway-example'

kubectl create namespace $NAMESPACE 2>/dev/null || true
kubectl config set-context --current --namespace=$NAMESPACE

Step 1: Create a ModelAPI

Create a ModelAPI in Proxy mode (we'll use mock responses for determinism):

kaos modelapi deploy gateway-api --mode Proxy --wait

Step 2: Create Upstream MCP Servers

We'll create two MCP servers showing both deployment methods.

Calculator MCP Server (CLI method)

Deploy using the kaos mcp deploy command with --wait flag:

export CALC_FUNCS='
def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

def multiply(x: int, y: int) -> int:
    """Multiply two numbers."""
    return x * y

def power(base: int, exponent: int) -> int:
    """Raise base to the power of exponent."""
    return base ** exponent
'

kaos mcp deploy calculator \
    --runtime python-string \
    --params "$CALC_FUNCS" \
    --wait

Text Utils MCP Server (CRD method)

MCP Servers and other resources can also be deployed directly with the k8s CRD instead of the CLI:

kubectl apply -f - << EOF
apiVersion: kaos.tools/v1alpha1
kind: MCPServer
metadata:
  name: textutils
spec:
  runtime: python-string
  params: |
    def uppercase(text: str) -> str:
        """Convert text to uppercase."""
        return text.upper()

    def reverse(text: str) -> str:
        """Reverse the characters in text."""
        return text[::-1]

    def word_count(text: str) -> int:
        """Count the number of words in text."""
        return len(text.split())
EOF

kubectl wait mcpserver/textutils --for=jsonpath='{.status.ready}'=true --timeout=180s

Step 3: Create the pctx Gateway

Create the pctx gateway that aggregates both upstream servers:

export PCTX_CONFIG='
{
  "name": "unified-gateway",
  "version": "1.0.0",
  "servers": [
    {
      "name": "calc",
      "url": "http://mcpserver-calculator.'$NAMESPACE'.svc.cluster.local:8000/mcp"
    },
    {
      "name": "text",
      "url": "http://mcpserver-textutils.'$NAMESPACE'.svc.cluster.local:8000/mcp"
    }
  ]
}'

kaos mcp deploy unified-gateway --runtime pctx --params "$PCTX_CONFIG" --wait

Step 4: Create the Agent

Create an agent connected to the pctx gateway. The mock responses demonstrate Code Mode - the agent writes TypeScript that calls multiple tools in sequence using pctx's execute tool:

# Define the mock code response with formatted TypeScript
# pctx exposes the "execute" tool for running TypeScript code
MOCK_CODE="{\
  \"tool\": \"execute\",\
  \"arguments\": {\
    \"code\": \"\
    const sum = await calc.add({a: 42, b: 8});\
    const product = await calc.multiply({x: sum, y: 2});\
    const squared = await calc.power({base: product, exponent: 2});\
    const wc = await text.word_count({text: 'The answer is ' + squared});\
    const result = await text.uppercase({text: 'RESULT: ' + squared + ' (' + wc + ' words)'});\
    return result;\"\
  }\
}"

MOCK_END='{}'
MOCK_FINAL='I executed a complex calculation chain: 42+8=50, 50*2=100, 100^2=10000. The final result formatted in uppercase is "RESULT: 10000 (4 WORDS)".'

kaos agent deploy gateway-agent \
    --modelapi gateway-api \
    --model mock-model \
    --mcp unified-gateway \
    --instructions "You use Code Mode to orchestrate multiple tools efficiently." \
    --mock-response "$MOCK_CODE" \
    --mock-response "$MOCK_END" \
    --mock-response "$MOCK_FINAL" \
    --expose \
    --wait

Step 5: Invoke the Agent

Send a request that triggers multi-tool orchestration:

kaos agent invoke gateway-agent --message "Calculate (42+8)*2, square it, count the words in the result description, and format everything in uppercase"

Step 6: Verify Agent Status

Check that the agent has discovered tools from the pctx gateway:

kaos agent status gateway-agent

Verify the output shows tool_execution capability:

kaos agent status gateway-agent --json | grep -q "tool_execution" || exit 1

Step 7: Verify Memory Events

Check that tool calls were recorded in memory and no errors occurred:

kaos agent memory gateway-agent

Verify a tool_call event exists and no errors were recorded:

# Check tool_call event exists
kaos agent memory gateway-agent --json | grep -q "tool_call" || exit 1
# Check no tool errors occurred
kaos agent memory gateway-agent --json | grep -q "tool_error" && exit 1 || true

Cleanup

kubectl delete namespace $NAMESPACE

Next Steps

• KAOS Monkey - Chaos engineering with Kubernetes MCP
• MCPServer CRD Reference - Full pctx configuration
• pctx Documentation - Upstream project