mcpipe 0.1.0

Plugin-based MCP server framework — routes any CLI tool through Source → Executor → Sink pipelines

mcpipe

• How it works
• Install
• Usage
  • MCP server (for LLM clients)
  • CLI
• Configuration
• Built-in plugins
  • Git
  • Docker
  • Docker Compose
  • Filesystem
• Transforms
  • Built-in transforms
  • Meta-params
  • Custom transforms
• Writing plugins
  • Public API
    • @tool(description, *, ...)
    • Cmd(*args: str)
    • ToolOutput
    • @transform(description)
    • TransformStep(name, params)
  • Examples
  • Pure Python tools
  • Fuller example — git log
  • Default output filters
  • Opting out of meta-params
• LLM self-authoring
  • How it works
  • Example: LLM creates a kubectl plugin
• Development
• License

Plugin-based MCP server that keeps CLI output out of your context window.

Any command-line tool can be exposed as an MCP tool. Output gets cached to disk — the LLM gets back a handle and uses generic framework tools (view, search) to read what it needs instead of having the full dump shoved into the conversation.

Zero dependencies. Python 3.12+.

How it works

LLM calls:  git_log(since="1week")
Returns:    { handle: "git_log_1716000000_a1b2c3d4", lines: 847, preview: "..." }

LLM calls:  view(handle="git_log_1716000000_a1b2c3d4", _search="auth")
Returns:    matching lines only

One tool produces. Generic tools consume. Plugins don't implement search or pagination.

Install

# From PyPI
pip install mcpipe

# From source
pip install .

Usage

MCP server (for LLM clients)

mcpipe server

Speaks JSON-RPC 2.0 over stdio. Point your MCP client at it.

CLI

mcpipe run git_log since="1 week ago"
mcpipe run docker_ps all=true
mcpipe view <handle> -T search pattern="error"
mcpipe list

Configuration

mcpipe reads configuration from (highest priority first):

1. Environment variables (MCPIPE_*)
2. Config file: $XDG_CONFIG_HOME/mcpipe/config.toml (default: ~/.config/mcpipe/config.toml)
3. Defaults

[cache]
dir = "/custom/cache/path"     # default: $XDG_RUNTIME_DIR/mcpipe
ttl = 7200                     # seconds, default: 3600
inline_threshold = 100         # lines, default: 50

[authoring]
enabled = true                 # default: false

[paths]
allowed = ["/home/user/code", "/data"]  # default: CWD only

Environment variable	Config key	Description
MCPIPE_CACHE_DIR	cache.dir	Cache directory
MCPIPE_CACHE_TTL	cache.ttl	Default cache TTL (seconds)
MCPIPE_INLINE_THRESHOLD	cache.inline_threshold	Lines below which output is inline
MCPIPE_ENABLE_AUTHORING	authoring.enabled	Enable authoring tools (1 to enable)
FS_ROOTS	paths.allowed	Colon-separated allowed filesystem roots

Built-in plugins

Git

git_status, git_log, git_diff, git_diff_unstaged, git_diff_staged, git_show, git_branch, git_add, git_commit, git_reset, git_create_branch, git_checkout, git_fetch, git_pull, git_push, git_stash_push, git_stash_pop, git_stash_list, git_tag, git_blame, git_cherry_pick, git_revert, git_remote, git_merge

Docker

docker_ps, docker_logs, docker_images

Docker Compose

compose_ps, compose_logs, compose_up, compose_down, compose_restart, compose_stop, compose_start, compose_config, compose_top, compose_images, compose_pull, compose_build, compose_exec, compose_run

Filesystem

fs_read, fs_ls, fs_stat, fs_find, fs_grep, fs_roots, fs_write, fs_mkdir, fs_rm, fs_mv, fs_cp

Access is restricted to allowed directory trees. Configure via paths.allowed in your config file, or set FS_ROOTS to a colon-separated list of paths.

Transforms

Output post-processing is pluggable. Transforms are pure functions — lines in, lines out. They run after caching and never mutate the cache.

Built-in transforms

All built-ins can be overridden — a user @transform with the same name replaces them (user extensions load after builtins).

Transform	Description	Params
search	Filter lines by regex pattern (case-insensitive)	pattern: str
limit	Return at most N lines from the start	n: int = 50
offset	Skip the first N lines	n: int = 0
head	Return the first N lines	n: int = 10
tail	Return the last N lines	n: int = 10

Meta-params

Any tool call can include transform meta-params prefixed with _:

{ "name": "git_log", "arguments": { "since": "1week", "_search": "fix", "_limit": 10 } }

These are desugared into transform steps before dispatch — plugins never see them.

Custom transforms

Custom transforms use the @transform decorator and live in ~/.config/mcpipe/transforms/:

from typing import Annotated
from mcpipe import transform

@transform("Sort lines alphabetically")
def sort(
    lines: list[str],
    reverse: Annotated[bool, "Sort in reverse order"] = False,
) -> list[str]:
    return sorted(lines, reverse=reverse)

The first argument must be lines: list[str], and the function must return list[str]. Additional parameters become the transform's config schema.

Writing plugins

A plugin is a Python file in mcpipe/plugins/ (built-in) or ~/.config/mcpipe/plugins/ (user).

Public API

Everything a plugin needs is importable from mcpipe:

from mcpipe import tool, Cmd, transform, TransformStep, ToolOutput

@tool(description, *, ...)

Decorator that registers a function as an MCP tool. The function name becomes the tool name. Type hints generate the JSON Schema.

The decorated function must return Cmd (to run a subprocess) or str (direct output).

@tool(
    description: str,           # Tool description shown to LLM
    *,
    read_only: bool = False,    # Tool only reads, never modifies
    destructive: bool = True,   # Tool may cause irreversible changes
    idempotent: bool = False,   # Safe to call repeatedly with same args
    open_world: bool = True,    # Tool interacts with external world
    ttl: int | None = None,     # Cache TTL in seconds (None = default)
    output_filter: list[TransformStep] | None = None,  # Default transforms
    meta_params: bool = True,   # Inject _search/_limit/etc. into schema
)

Cmd(*args: str)

Return from a @tool function to run a subprocess. Args are passed to asyncio.create_subprocess_exec.

Cmd("git", "-C", repo_path, "log", "--max-count=10")

ToolOutput

Structured result from tool execution (returned by execute()). You don't construct this directly — the framework builds it.

Field	Type	Description
handle	str	Cache handle for the output
total_lines	int	Total line count before transforms
text	str | None	Full output (inline if small or transformed)
preview	str | None	First few lines (large non-transformed output)
is_error	bool	Whether the tool failed

@transform(description)

Decorator that registers a transform function. Must accept lines: list[str] as the first argument and return list[str]. If a transform with the same name already exists, it is overwritten.

@transform(
    description: str,  # Transform description
)

Example:

@transform("Sort lines alphabetically")
def sort(lines: list[str], reverse: bool = False) -> list[str]:
    return sorted(lines, reverse=reverse)

TransformStep(name, params)

A single transform invocation, used in output_filter:

output_filter=[TransformStep("head", {"n": 10})]

Examples

from typing import Annotated
from mcpipe import Cmd, tool

@tool("List running containers", read_only=True, destructive=False, idempotent=True)
def docker_ps(
    all: Annotated[bool, "Show all containers (including stopped)"] = False,
    format: Annotated[str, "Go template for output format"] = "",
) -> Cmd:
    args = ["docker", "ps"]
    if all:
        args.append("--all")
    if format:
        args.extend(["--format", format])
    return Cmd(*args)

Return Cmd to run a subprocess, or str for direct output. Type hints generate the MCP input schema automatically. Use Annotated[type, "description"] to add descriptions to parameters — these appear in the tool's JSON Schema.

Pure Python tools

Return str instead of Cmd for tools that don't need a subprocess:

from typing import Annotated
from mcpipe import tool

@tool("Count lines in a file", read_only=True, destructive=False)
def count_lines(
    path: Annotated[str, "File path"],
) -> str:
    with open(path) as f:
        return str(sum(1 for _ in f))

Fuller example — git log

A more complete example showing a helper function, input validation, and how Annotated args map to the subprocess call:

from typing import Annotated
from mcpipe import Cmd, tool

def _git(*args: str, repo_path: str = ".") -> Cmd:
    """Build a git Cmd with -C repo_path prefix."""
    return Cmd("git", "-C", repo_path, *args)

def _validate_ref(value: str, label: str = "value") -> None:
    """Reject refs starting with '-' to prevent flag injection."""
    if value.startswith("-"):
        raise ValueError(f"Invalid {label}: '{value}' — cannot start with '-'")

@tool("Show commit log", read_only=True, destructive=False, idempotent=True)
def git_log(
    repo_path: Annotated[str, "Path to the git repository"] = ".",
    max_count: Annotated[int, "Number of commits to show"] = 10,
    since: Annotated[str, "Show commits after this date (e.g. '1 week ago')"] = "",
    path: Annotated[str, "Limit to commits touching this path"] = "",
) -> Cmd:
    args = ["log", f"--max-count={max_count}"]
    if since:
        _validate_ref(since, "since")
        args.extend(["--since", since])
    if path:
        _validate_ref(path, "path")
        args.extend(["--", path])
    return _git(*args, repo_path=repo_path)

When an LLM calls git_log(max_count=5, since="1 week ago"), this builds and runs: git -C . log --max-count=5 --since '1 week ago'.

Default output filters

Tools can declare default transforms that apply when the caller doesn't send any _meta params. Useful for keeping verbose output short by default:

from mcpipe import Cmd, tool
from mcpipe.transform import TransformStep

@tool(
    "Push commits to remote",
    read_only=False,
    output_filter=[TransformStep("head", {"n": 10})],
)
def git_push(...) -> Cmd:
    ...

Caller-provided transforms replace defaults entirely — no merging.

Opting out of meta-params

By default, every tool gets transform meta-params (_search, _limit, etc.) injected into its schema. Tools that shouldn't be filtered (config, help, authoring) can opt out:

@tool("Show help text", read_only=True, meta_params=False)
def my_help() -> str:
    return "..."

LLM self-authoring

An LLM connected to mcpipe can create its own tools and transforms at runtime — no restarts, no manual file editing. This is the core design: if a tool doesn't exist yet, the LLM writes it, reloads, and uses it immediately.

Authoring tools are disabled by default. Enable with authoring.enabled = true in your config or MCPIPE_ENABLE_AUTHORING=1.

How it works

mcpipe exposes framework tools for managing user extensions:

Tool	Purpose
authoring_help	Returns the full plugin/transform API guide
write_plugin	Creates or overwrites a user plugin file
write_transform	Creates or overwrites a user transform file
read_extension	Reads a user plugin/transform source file
list_user_extensions	Lists files in the user config dirs
delete_plugin / delete_transform	Removes a file
reload	Hot-reloads all modules to pick up changes

User extensions live in ~/.config/mcpipe/ (or $XDG_CONFIG_HOME/mcpipe/):

• plugins/*.py — tools
• transforms/*.py — output transforms

Example: LLM creates a kubectl plugin

User: "I need to check my Kubernetes pods"

LLM:  1. Calls authoring_help(topic="plugin") — reads the API
      2. Calls write_plugin(name="kubectl", content="...")
      3. Calls reload() — new tools are live
      4. Calls kubectl_get_pods(namespace="production")
      5. Calls view(handle="...", _search="CrashLoopBackOff")

The plugin persists across sessions. Next time the LLM connects, kubectl_get_pods is already available.

Development

uv sync --dev          # install deps
uv run poe hooks       # install git hooks
uv run poe check       # lint + typecheck + tests
uv run poe test        # tests only
uv run poe lint        # ruff
uv run poe format      # ruff format
uv run poe release     # bump version, changelog, tag, push

Commits follow Conventional Commits. Versioning and changelogs are managed by commitizen.

License

MIT