Pagination and Large Result Sets in MCP

When your MCP server handles large datasets - whether listing thousands of files, database records, or search results - you need pagination to manage memory efficiently and provide responsive user experiences. This guide covers how to implement and use pagination in MCP.

Why Pagination Matters

Without pagination, large responses can cause:

Memory exhaustion - Loading millions of records at once
Slow response times - Users wait while all data loads
Timeout errors - Requests exceed timeout limits
Poor AI performance - LLMs struggle with massive context

MCP uses cursor-based pagination for reliable, consistent paging through result sets.

How MCP Pagination Works

The Cursor Concept

A cursor is an opaque string that marks your position in a result set. Think of it like a bookmark in a long book.

sequenceDiagram
    participant Client
    participant Server
    
    Client->>Server: tools/list (no cursor)
    Server-->>Client: tools [1-10], nextCursor: "abc123"
    
    Client->>Server: tools/list (cursor: "abc123")
    Server-->>Client: tools [11-20], nextCursor: "def456"
    
    Client->>Server: tools/list (cursor: "def456")
    Server-->>Client: tools [21-25], nextCursor: null (end)

Pagination in MCP Methods

These MCP methods support pagination:

Method	Returns	Cursor Support
`tools/list`	Tool definitions	✅
`resources/list`	Resource definitions	✅
`prompts/list`	Prompt definitions	✅
`resources/templates/list`	Resource templates	✅

Server Implementation

Python (FastMCP)

from mcp.server import Server
from mcp.types import Tool, ListToolsResult
import math

app = Server("paginated-server")

# Simulated large dataset
ALL_TOOLS = [
    Tool(name=f"tool_{i}", description=f"Tool number {i}", inputSchema={})
    for i in range(100)
]

PAGE_SIZE = 10

@app.list_tools()
async def list_tools(cursor: str | None = None) -> ListToolsResult:
    """List tools with pagination support."""
    
    # Decode cursor to get starting index
    start_index = 0
    if cursor:
        try:
            start_index = int(cursor)
        except ValueError:
            start_index = 0
    
    # Get page of results
    end_index = min(start_index + PAGE_SIZE, len(ALL_TOOLS))
    page_tools = ALL_TOOLS[start_index:end_index]
    
    # Calculate next cursor
    next_cursor = None
    if end_index < len(ALL_TOOLS):
        next_cursor = str(end_index)
    
    return ListToolsResult(
        tools=page_tools,
        nextCursor=next_cursor
    )

TypeScript

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { ListToolsResultSchema } from "@modelcontextprotocol/sdk/types.js";

const server = new Server({
  name: "paginated-server",
  version: "1.0.0"
});

// Simulated large dataset
const ALL_TOOLS = Array.from({ length: 100 }, (_, i) => ({
  name: `tool_${i}`,
  description: `Tool number ${i}`,
  inputSchema: { type: "object", properties: {} }
}));

const PAGE_SIZE = 10;

server.setRequestHandler(ListToolsResultSchema, async (request) => {
  // Decode cursor
  let startIndex = 0;
  if (request.params?.cursor) {
    startIndex = parseInt(request.params.cursor, 10) || 0;
  }
  
  // Get page of results
  const endIndex = Math.min(startIndex + PAGE_SIZE, ALL_TOOLS.length);
  const pageTools = ALL_TOOLS.slice(startIndex, endIndex);
  
  // Calculate next cursor
  const nextCursor = endIndex < ALL_TOOLS.length ? String(endIndex) : undefined;
  
  return {
    tools: pageTools,
    nextCursor
  };
});

Java (Spring MCP)

@Service
public class PaginatedToolService {
    
    private static final int PAGE_SIZE = 10;
    private final List<Tool> allTools;
    
    public PaginatedToolService() {
        // Initialize large dataset
        this.allTools = IntStream.range(0, 100)
            .mapToObj(i -> new Tool("tool_" + i, "Tool number " + i, Map.of()))
            .collect(Collectors.toList());
    }
    
    @McpMethod("tools/list")
    public ListToolsResult listTools(@Param("cursor") String cursor) {
        // Decode cursor
        int startIndex = 0;
        if (cursor != null && !cursor.isEmpty()) {
            try {
                startIndex = Integer.parseInt(cursor);
            } catch (NumberFormatException e) {
                startIndex = 0;
            }
        }
        
        // Get page of results
        int endIndex = Math.min(startIndex + PAGE_SIZE, allTools.size());
        List<Tool> pageTools = allTools.subList(startIndex, endIndex);
        
        // Calculate next cursor
        String nextCursor = endIndex < allTools.size() ? String.valueOf(endIndex) : null;
        
        return new ListToolsResult(pageTools, nextCursor);
    }
}

Client Implementation

Python Client

from mcp import ClientSession

async def get_all_tools(session: ClientSession) -> list:
    """Fetch all tools using pagination."""
    all_tools = []
    cursor = None
    
    while True:
        result = await session.list_tools(cursor=cursor)
        all_tools.extend(result.tools)
        
        if result.nextCursor is None:
            break
        cursor = result.nextCursor
    
    return all_tools

# Usage
async with client_session as session:
    tools = await get_all_tools(session)
    print(f"Found {len(tools)} tools")

TypeScript Client

import { Client } from "@modelcontextprotocol/sdk/client/index.js";

async function getAllTools(client: Client): Promise<Tool[]> {
  const allTools: Tool[] = [];
  let cursor: string | undefined = undefined;
  
  do {
    const result = await client.listTools({ cursor });
    allTools.push(...result.tools);
    cursor = result.nextCursor;
  } while (cursor);
  
  return allTools;
}

// Usage
const tools = await getAllTools(client);
console.log(`Found ${tools.length} tools`);

Lazy Loading Pattern

For very large datasets, load pages on-demand:

class PaginatedToolIterator:
    """Lazily iterate through paginated tools."""
    
    def __init__(self, session: ClientSession):
        self.session = session
        self.cursor = None
        self.buffer = []
        self.exhausted = False
    
    async def __anext__(self):
        # Return from buffer if available
        if self.buffer:
            return self.buffer.pop(0)
        
        # Check if we've exhausted all pages
        if self.exhausted:
            raise StopAsyncIteration
        
        # Fetch next page
        result = await self.session.list_tools(cursor=self.cursor)
        self.buffer = list(result.tools)
        self.cursor = result.nextCursor
        
        if self.cursor is None:
            self.exhausted = True
        
        if not self.buffer:
            raise StopAsyncIteration
        
        return self.buffer.pop(0)
    
    def __aiter__(self):
        return self

# Usage - memory efficient for large datasets
async for tool in PaginatedToolIterator(session):
    process_tool(tool)

Pagination for Resources

Resources often need pagination for directories or large datasets:

from mcp.server import Server
from mcp.types import Resource, ListResourcesResult
import os

app = Server("file-server")

@app.list_resources()
async def list_resources(cursor: str | None = None) -> ListResourcesResult:
    """List files in directory with pagination."""
    
    directory = "/data/files"
    all_files = sorted(os.listdir(directory))
    
    # Decode cursor (file index)
    start_index = int(cursor) if cursor else 0
    page_size = 20
    end_index = min(start_index + page_size, len(all_files))
    
    # Create resource list for this page
    resources = []
    for filename in all_files[start_index:end_index]:
        filepath = os.path.join(directory, filename)
        resources.append(Resource(
            uri=f"file://{filepath}",
            name=filename,
            mimeType="application/octet-stream"
        ))
    
    # Calculate next cursor
    next_cursor = str(end_index) if end_index < len(all_files) else None
    
    return ListResourcesResult(
        resources=resources,
        nextCursor=next_cursor
    )

Cursor Design Strategies

Strategy 1: Index-Based (Simple)

# Cursor is just the index
cursor = "50"  # Start at item 50

Pros: Simple, stateless Cons: Results can shift if items are added/removed

Strategy 2: ID-Based (Stable)

# Cursor is the last seen ID
cursor = "item_abc123"  # Start after this item

Pros: Stable even if items change Cons: Requires ordered IDs

Strategy 3: Encoded State (Complex)

import base64
import json

def encode_cursor(state: dict) -> str:
    return base64.b64encode(json.dumps(state).encode()).decode()

def decode_cursor(cursor: str) -> dict:
    return json.loads(base64.b64decode(cursor).decode())

# Cursor contains multiple state fields
cursor = encode_cursor({
    "offset": 50,
    "filter": "active",
    "sort": "name"
})

Pros: Can encode complex state Cons: More complex, larger cursor strings

Best Practices

1. Choose Appropriate Page Sizes

# Consider the data size
PAGE_SIZE_SMALL_ITEMS = 100   # Simple metadata
PAGE_SIZE_MEDIUM_ITEMS = 20   # Richer objects
PAGE_SIZE_LARGE_ITEMS = 5     # Complex content

2. Handle Invalid Cursors Gracefully

@app.list_tools()
async def list_tools(cursor: str | None = None) -> ListToolsResult:
    try:
        start_index = int(cursor) if cursor else 0
        if start_index < 0 or start_index >= len(ALL_TOOLS):
            start_index = 0  # Reset to beginning
    except (ValueError, TypeError):
        start_index = 0  # Invalid cursor, start fresh
    # ...

3. Include Total Count (Optional)

return ListToolsResult(
    tools=page_tools,
    nextCursor=next_cursor,
    # Some implementations include total for UI progress
    _meta={"total": len(ALL_TOOLS)}
)

4. Test Edge Cases

async def test_pagination():
    # Empty result set
    result = await session.list_tools()
    assert result.tools == []
    assert result.nextCursor is None
    
    # Single page
    result = await session.list_tools()
    assert len(result.tools) <= PAGE_SIZE
    
    # Invalid cursor
    result = await session.list_tools(cursor="invalid")
    assert result.tools  # Should return first page

Common Pitfalls

❌ Returning All Results Then Paginating Client-Side

# BAD: Loads everything into memory
@app.list_tools()
async def list_tools() -> ListToolsResult:
    all_tools = load_all_tools()  # 1 million tools!
    return ListToolsResult(tools=all_tools)

✅ Paginate at the Data Source

# GOOD: Only loads what's needed
@app.list_tools()
async def list_tools(cursor: str | None = None) -> ListToolsResult:
    offset = int(cursor) if cursor else 0
    tools = await db.query_tools(offset=offset, limit=PAGE_SIZE)
    return ListToolsResult(tools=tools, nextCursor=...)

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Pagination and Large Result Sets in MCP

Why Pagination Matters

How MCP Pagination Works

The Cursor Concept

Pagination in MCP Methods

Server Implementation

Python (FastMCP)

TypeScript

Java (Spring MCP)

Client Implementation

Python Client

TypeScript Client

Lazy Loading Pattern

Pagination for Resources

Cursor Design Strategies

Strategy 1: Index-Based (Simple)

Strategy 2: ID-Based (Stable)

Strategy 3: Encoded State (Complex)

Best Practices

1. Choose Appropriate Page Sizes

2. Handle Invalid Cursors Gracefully

3. Include Total Count (Optional)

4. Test Edge Cases

Common Pitfalls

❌ Returning All Results Then Paginating Client-Side

✅ Paginate at the Data Source

What's Next

Additional Resources

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Pagination and Large Result Sets in MCP

Why Pagination Matters

How MCP Pagination Works

The Cursor Concept

Pagination in MCP Methods

Server Implementation

Python (FastMCP)

TypeScript

Java (Spring MCP)

Client Implementation

Python Client

TypeScript Client

Lazy Loading Pattern

Pagination for Resources

Cursor Design Strategies

Strategy 1: Index-Based (Simple)

Strategy 2: ID-Based (Stable)

Strategy 3: Encoded State (Complex)

Best Practices

1. Choose Appropriate Page Sizes

2. Handle Invalid Cursors Gracefully

3. Include Total Count (Optional)

4. Test Edge Cases

Common Pitfalls

❌ Returning All Results Then Paginating Client-Side

✅ Paginate at the Data Source

What's Next

Additional Resources