Beyond MCP

It's time to push beyond MCP Servers... Right?

Let's breakdown real engineering trade offs between MCP, CLI, File System Scripts, and Skills based approaches for building reusable toolsets for your AI Agents.

Watch the full video breakdown here: Beyond MCP

Purpose of this Repo

• MCP Servers are the standard way to build reusable toolsets for your AI Agents. But they are not the only way.
• MCP Servers come with a massive cost - instant context loss.
• When you have a single, or a few MCP Servers, this is not a big deal. But as you scale to many agents, many tools, and many contexts - this cost quickly becomes a bottleneck.
• So what are the alternatives that big players are using to build powerful, reusable, context preserving toolsets for their AI Agents?

Here we explore 4 concrete approaches in this repo, all implementing access to Kalshi prediction market data.

The 4 Approaches

apps/1_mcp_server/ - MCP Server

apps/2_cli/ - CLI

apps/3_file_system_scripts/ - File System Scripts

apps/4_skill/ - Skill

Quick Start

1. MCP Server

cp .mcp.testing .mcp.json

claude --mcp-config .mcp.json

prompt: "kalshi: get exchange status"

2. CLI

# or by agent
claude

prompt: "/prime_kalshi_cli_tools"

prompt: "kalshi: Get exchange status"

prompt: "kalshi: List events"

prompt: "kalshi: List events in JSON"

prompt: "kalshi: List events in JSON, limit 100"

# or by hand
cd apps/2_cli
uv sync
uv run kalshi status
uv run kalshi events
uv run kalshi events --json
uv run kalshi events --json --limit 100

3. File System Scripts

# by agent
claude

prompt: "/prime_file_system_scripts"

prompt: "kalshi: Get exchange status"

prompt: "kalshi: List events"

...

# or by hand
cd apps/3_file_system_scripts/scripts

uv run status.py

uv run *.py

4. Skill

cd apps/4_skill/

claude

prompt: "kalshi markets: Get exchange status"

prompt: "kalshi markets: search for events about 'best ai'" # Note this will trigger the cache build on first run which will take several minutes

...

The 4 Approaches In Detail

• apps/1_mcp_server/ - MCP Server
• apps/2_cli/ - CLI
• apps/3_file_system_scripts/ - File System Scripts
• apps/4_skill/ - Skill

1. MCP Server (apps/1_mcp_server/)

Classic Model Context Protocol implementation

• ✅ Standardized integration - Works with any MCP-compatible client
• ✅ Tool discovery - Auto-exposes 15 tools to LLMs
• ✅ Clean abstractions - MCP protocol handles complexity
• ❌ Instant context loss - Every tool call loses conversational context
• ❌ Wrapper overhead - Delegates to CLI via subprocess

Architecture:

Claude/LLM → MCP Protocol → MCP Server → subprocess → CLI → Kalshi API

Key files:

• server.py - FastMCP server with 15 tool definitions
• Wraps CLI commands in MCP tool interface
• Each tool call is stateless

When to use: Building tools for multiple LLM clients, need standardized protocol, context loss is acceptable.

2. CLI (apps/2_cli/)

Direct HTTP API access via command-line interface

• ✅ Single source of truth - Direct API calls, no wrappers
• ✅ Dual output modes - Human-readable or pure JSON
• ✅ Smart caching - Pandas-based search with 6-hour TTL
• ✅ Minimal overhead - Direct httpx calls, no SDK
• ✅ Improved Context - Agent reads ~half as much context as the MCP Server

Architecture:

Claude → subprocess → CLI (13 commands) → Direct HTTP → Kalshi API

Key files:

• kalshi_cli/cli.py - All 13 commands (552 lines)
• kalshi_cli/modules/client.py - HTTP

... View full README on GitHub