intpot

Why intpot

A useful Python function often needs three interfaces: a command for people, an HTTP endpoint for applications, and an MCP tool for AI agents. Maintaining three copies means three signatures, three sets of descriptions, and three places for behavior to drift.

intpot gives you two ways out:

The output is ordinary Python. Ejected and converted apps do not depend on intpot.

Quick start

Install every runtime for the complete experience:

pip install "intpot[all]"

Write once, serve everywhere

Save this as app.py:

from intpot import App

app = App("my-app")

@app.tool()
def add(a: int, b: int) -> int:
    """Add two numbers together."""
    return a + b

@app.tool()
def greet(name: str, greeting: str = "Hello") -> str:
    """Greet someone by name."""
    return f"{greeting}, {name}!"

Then serve in any mode:

intpot serve app.py --cli          # Run as Typer CLI
intpot serve app.py --api          # Run as FastAPI on port 8000
intpot serve app.py --mcp          # Run as MCP server for AI agents

In CLI mode, everything after the flags belongs to your app:

$ intpot serve app.py --cli add 2 3
5
$ intpot serve app.py --cli greet World --greeting Hi
Hi, World!

With API mode running, call the same names as POST routes with JSON request bodies:

$ curl -s -X POST http://127.0.0.1:8000/add \
    -H "Content-Type: application/json" \
    -d '{"a": 2, "b": 3}'
5

MCP mode exposes add and greet as FastMCP tools with schemas derived from their type annotations and defaults.

Or eject to standalone framework code:

intpot eject app.py --to api       # Export as standalone FastAPI app
intpot eject app.py --to cli       # Export as standalone Typer CLI
intpot eject app.py --to mcp       # Export as standalone FastMCP server

Scaffold a new project

intpot init my-server --type mcp
intpot init my-app --type cli
intpot init my-api --type api

Install only the frameworks you need if you do not want the full extra:

pip install intpot          # Core commands and Typer output
pip install "intpot[mcp]"   # Add FastMCP support
pip install "intpot[api]"   # Add FastAPI support

Convert an existing app

intpot detects Typer, FastAPI, and FastMCP apps from a Python file and supports every conversion between them:

# MCP server -> Typer CLI
intpot to cli server.py

# CLI app -> FastMCP server
intpot to mcp app.py

# CLI app -> FastAPI app
intpot to api app.py

# Write output to a file
intpot to cli server.py --output cli_app.py

# Convert all apps in a directory
intpot to cli ./myproject/
intpot to mcp ./myproject/ --output ./converted/

Use intpot inspect app.py first when you want to see the normalized tool definitions before generating code. Converted output is readable Python that you can review, test, and change.

Give your coding agent intpot context

Install project-local instructions for Claude Code, Cursor, Windsurf, GitHub Copilot, Cline, or OpenAI Codex:

# Auto-detect agents in your project
intpot add skills

# Target a specific agent
intpot add skills --agent claude
intpot add skills --agent cursor
intpot add skills --agent windsurf
intpot add skills --agent copilot
intpot add skills --agent cline
intpot add skills --agent codex

# Specify a project directory
intpot add skills --path ./myproject/

What intpot handles

• One definition, three live interfaces: serve an intpot.App through Typer, FastAPI, or FastMCP.
• Six conversion directions: move existing apps between all three frameworks.
• Standalone output: eject or convert to normal framework code with no intpot runtime dependency.
• Behavior-aware transforms: preserve function bodies and imports, and translate framework conventions such as typer.echo() into return values.
• Interface fidelity: carry types, defaults, and async functions through supported conversions, and apply FastAPI parameter sources when generating an API.
• Programmatic access: use intpot.load() and normalized ToolInfo objects from Python.
• Project tooling: scan directories, scaffold projects, and install agent guidance for six coding agents.

Python API

Universal App (write once, serve everywhere)

from intpot import App

app = App("my-app")

@app.tool()
def greet(name: str, greeting: str = "Hello") -> str:
    """Greet someone."""
    return f"{greeting}, {name}!"

# Serve as any framework
app.serve(mode="cli")                         # Run as Typer CLI
app.serve(mode="api", port=8000)              # Run as FastAPI on 127.0.0.1
app.serve(mode="mcp")                         # Run as MCP server

# Eject to standalone code
cli_code = app.eject("cli")                   # Returns Typer code string
api_code = app.eject("api")                   # Returns FastAPI code string

# Access normalized tool definitions
for tool in app.tools:
    print(tool.name, tool.parameters)

Conversion API (convert existing framework code)

import intpot

# From a file
app = intpot.load("mcp_server.py")
cli_code = app.to_cli()
api_code = app.to_api()

# From a live instance
from fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def greet(name: str) -> str:
    return f"Hello, {name}!"

app = intpot.load(mcp)
print(app.to_cli())

# Write directly to a file
app.write("output/cli_app.py", "cli")
app.write("output/api_app.py", "api")

App (universal runtime):

• .tool(name=None, description=None) — decorator to register functions as tools; both arguments override the defaults taken from the function name and docstring. Functions with *args or **kwargs are rejected because those parameters have no consistent CLI, HTTP API, and MCP representation.
• .serve(mode, host, port) — serve as CLI, API, or MCP
• .eject(target) — generate standalone framework code
• .tools — list of normalized ToolInfo objects

IntpotApp (conversion wrapper, returned by intpot.load()):

• .to_cli(), .to_mcp(), .to_api() — return generated code as strings
• .write(path, target) — generate and write to a file in one step
• .tools — list of normalized ToolInfo objects
• .source_type — detected framework type

What conversion preserves

intpot is designed to produce code you can own, rather than hide conversion behind a runtime adapter. Its normalized ToolInfo schema carries the parts that the three frameworks share:

• tool names, descriptions, types, defaults, and async behavior;
• recoverable function bodies and the direct imports they reference;
• framework metadata that has a target equivalent, including supported Query, Header, Path, and Body parameter sources when generating FastAPI;
• scalar returns translated into a shape the target framework can serve correctly.

Frameworks do not have one-to-one equivalents for every feature. Review generated code when a source uses nested Typer command groups, Annotated[..., Body(...)], FastAPI Depends(), Pydantic model parameters, streaming, background tasks, or framework-specific error handling. Transitive imports, external services, and configuration are not provisioned for you. If intpot cannot recover a body, it emits a # TODO: implement stub instead of inventing behavior.

[!IMPORTANT] Detection imports the source module, so only inspect or convert code you trust. intpot is alpha software: compile the generated file, import it with its dependencies, and exercise a real CLI command, API request, or MCP tool before shipping it.

Architecture

Both halves of intpot meet at one normalized schema, ToolInfo. Everything upstream produces it; everything downstream consumes it.

   @app.tool()                        source .py file
   (intpot.App)                    (Typer / FastMCP / FastAPI)
        |                                    |
        |                              1. DETECT
        |                              2. INSPECT
        |                                    |
        +--------------> ToolInfo[] <--------+
                              |
              +---------------+---------------+
              |                               |
        build a live                    3. GENERATE
      framework instance            (render a template)
              |                               |
       serve --cli/--api/--mcp        .py output on disk
                                    (to cli/mcp/api, eject)

Conversion follows three stages: detect the framework, inspect its tools into ToolInfo[], then render the target framework. The runtime side starts at the same schema: @app.tool() creates ToolInfo directly, then serve builds a live framework instance or eject sends it through the generators.

Conversion examples

The repository includes checked-in source and generated output for every direction:

examples/ also contains advanced inputs and outputs with direct imports, async tools, request bodies, Depends(), path parameters, and multiple HTTP methods. Run bash scripts/demo.sh to regenerate all twelve conversions locally.

CLI Reference

intpot --version (or -V) prints the installed version; intpot <command> --help works for any command below.

intpot serve

Serve an intpot App as CLI, API, or MCP server.

intpot serve <source> --cli|--api|--mcp [--host <host>] [--port <port>] [--] [args...]

Anything intpot doesn't recognise is forwarded, so your own options work as-is. Use -- when your app defines a flag intpot also defines:

intpot serve app.py --cli -- greet World --port 5

intpot inspect

Show the tools intpot extracts from a source, without generating anything. Useful for checking what a conversion will see before you run it.

intpot inspect <source> [--json] [--verbose]

$ intpot inspect mcp_server.py

Source: mcp_server.py (mcp)
┏━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┓
┃ Name  ┃ Description            ┃ Parameters            ┃ Return Type ┃ Async ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━┩
│ greet │ Greet someone by name. │ name: str, greeting:  │ str         │ No    │
│       │                        │ str='Hello'           │             │       │
└───────┴────────────────────────┴───────────────────────┴─────────────┴───────┘

intpot eject

Export an intpot App as standalone framework code.

intpot eject <source> --to <cli|mcp|api> [--output <path>]

intpot init

Scaffold a new project from a template.

intpot init <name> --type <mcp|cli|api>

intpot to cli / to mcp / to api

Convert a source file — or every app in a directory — to the target framework.

intpot to cli <source> [--output <path>] [--dry-run] [--verbose]
intpot to mcp <source> [--output <path>] [--dry-run] [--verbose]
intpot to api <source> [--output <path>] [--dry-run] [--verbose]

All three take the same arguments:

to cli accepts MCP or API sources, to mcp accepts CLI or API, to api accepts CLI or MCP. A source that already matches the target is skipped.

--dry-run shows generated output without writing generated files, but it is not a sandbox: detection still imports the source and executes arbitrary module-level code. Only point intpot at source you trust:

$ intpot to cli mcp_server.py --dry-run
# --- Would generate: mcp_server_cli.py ---
"""CLI app generated by intpot."""
...

intpot add skills

Install intpot skills/rules for AI coding agents. Auto-detects which agents are configured in the project, or specify one explicitly.

intpot add skills [--agent <name>] [--path <dir>]

Supported agents and output locations:

Codex has to be asked for by name — intpot add skills --agent codex. It reads AGENTS.md, but so does nearly every other tool now, so the presence of that file says nothing about whether you use Codex. Since installing appends to it, guessing wrong would edit your own documentation. The same reasoning is why Copilot keys off .github/copilot-instructions.md rather than .github/, which only means the project is on GitHub.

Running with no --agent and no detected marker exits without writing anything. Copilot and Codex installations use bounded intpot-managed blocks, so rerunning the command updates or repairs intpot guidance while preserving surrounding project content. For Codex, intpot warns when the resulting AGENTS.md exceeds Codex's default 32 KiB instruction limit.

Contributing

intpot has a small core with clear extension points: inspectors turn frameworks into ToolInfo, generators turn ToolInfo into source, and runtime builders expose live interfaces. Contributions can improve conversion fidelity, add real-world examples, strengthen generated-code tests, or make the developer experience clearer.

• Start with a good first issue.
• Browse work where help is wanted.
• Read CONTRIBUTING.md for the pull request process and docs/reviewing.md for the contracts changes must preserve.

Development setup

Requires uv.

git clone https://github.com/tugrulguner/intpot.git
cd intpot
uv sync --all-extras
uv run pre-commit install

Run the full check suite:

make check   # lint + typecheck + test

Individual targets:

make lint              # ruff check + format check
make typecheck         # pyright
make test              # pytest
make format            # auto-format code
make changelog-draft   # preview the next release section
make changelog         # assemble changelog.d/ into CHANGELOG.md (release only)

Changelog entries are written as one fragment file per PR in changelog.d/ rather than by editing CHANGELOG.md, and CI asks every PR for one. See changelog.d/README.md — it's short.

Roadmap

See ROADMAP.md for the current polish backlog and the planned full AST transform pipeline.

Support intpot

If intpot removes a duplicate interface from your project, consider starring the repository. Stars help other Python developers find the project, while issues and pull requests make it better.

License

MIT