Which integrations are available for this server?

Provides a Python execution environment that allows for the programmatic orchestration of multiple MCP tools within a single script, enabling batch processing and complex logic without multiple model round-trips.

How do I use PTC-MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@PTC-MCP Batch compare the quarterly revenue trends for AMZN, MSFT, and GOOG." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

PTC-MCP

Programmatic Tool Calling for Claude Code via MCP.

Claude Code on subscription plans lacks the Anthropic API's programmatic tool calling (PTC) feature, where Claude can write Python scripts that call multiple tools in a single execution. Without it, every tool invocation is a full model round-trip — intermediate results enter the context window, consuming tokens and adding latency.

PTC-MCP fixes this. It's an MCP server that exposes a single execute_program tool. Claude writes a Python script, the server runs it with MCP tools injected as async functions, and only stdout comes back. Intermediate tool results stay in the Python runtime and never enter the conversation.

How it works

Claude Code │ │ Writes a Python script using namespaced tool names. │ Calls execute_program(code="...") │ ▼ PTC-MCP Server │ ├── Tool Registry │ Connects to configured MCP servers at startup. │ Fetches their tools, applies allow/block filters, │ and registers each as a namespaced async function. │ ├── Execution Engine │ Wraps the script in an async function, injects │ the tool namespace, captures stdout, enforces │ timeout and output size limits. │ └── Returns stdout with a status prefix. Only this enters Claude Code's context.

At startup, PTC-MCP connects to your configured MCP servers as a client, discovers their tools, and makes them callable as mcp__<server>__<tool>() async functions inside scripts. When Claude calls execute_program, the script runs in-process with those functions available. Tool calls proxy to the real MCP servers, results stay local, and only print() output goes back.

Example

Claude decides comparing three tickers benefits from batched execution:

execute_program(code=""" tickers = ["AMZN", "MSFT", "GOOG"] for t in tickers: data = await mcp__financial_data__query_financials( ticker=t, statement="income", period="quarter", limit=4 ) revenues = [q["revenue"] for q in data] trend = " → ".join(f"${r/1e9:.1f}B" for r in revenues) print(f"{t}: {trend}") """)

Three tool calls happen inside the script. Claude sees only:

[Script executed successfully] AMZN: $170.0B → $165.3B → $158.9B → $149.2B MSFT: $65.6B → $62.0B → $59.1B → $56.5B GOOG: $96.5B → $88.3B → $85.0B → $80.5B

Setup

Requires Python 3.11+.

uv venv && uv pip install -e ".[dev]"

Configuration

Create a config.yaml (or set PTC_MCP_CONFIG to point elsewhere):

servers: - name: financial-data transport: stdio command: node args: ["./financial-data-mcp/dist/index.js"] - name: internal-apis transport: sse url: "http://localhost:8080/mcp" tools: block: - "mcp__internal_apis__delete_resource" execution: timeout_seconds: 120 max_output_bytes: 65536

servers — MCP servers to bridge. Supports stdio and sse transports.
tools.allow / tools.block — Whitelist or blacklist namespaced tool names (mutually exclusive). Omit both to allow everything.
execution — Timeout and output size limits for execute_program.

The server starts fine with no config file or an empty servers list.

Running

# Directly uv run python -m ptc_mcp # Or via the installed entry point ptc-mcp

The server communicates over stdio (JSON-RPC). Add it to your Claude Code MCP settings to use it.

Testing

uv run pytest tests/ -v

Tests include unit tests for config parsing, the execution engine, registry filtering/namespacing, and end-to-end integration tests that spin up a real mock MCP server.