mcpField
G
GuardMarket

GuardMarket

Local
github.com/zma-petterzhangBusiness & Marketing0Updated 5w ago

About GuardMarket

GuardMarket is a Python MCP client that connects to a configured backend for discovering, inspecting, and invoking commerce and data skills. It also provides authorized balance and receipt lookups; the backend runs the skills and handles accounts and billing.

Key features

  1. 01Search versioned skills and prices
  2. 02Inspect schemas, examples, and effects
  3. 03Invoke with a spending cap
  4. 04Read wallet balances
  5. 05Retrieve invocation receipts

Use cases

  1. 01Calculate a cart total from supplied data
  2. 02Validate or normalize product data
  3. 03Escape CSV formulas before export
  4. 04Review inventory thresholds or summarize numbers

MCP support

The client exposes five MCP tools for skill discovery, details, invocation, wallet balances, and receipts. It connects to a separately operated backend; discovery may be anonymous, while invocation and account tools require an API key.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbguardmarket-mcp-0.3.0.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

GuardMarket — commerce and data skills over MCP

中文 · 125-skill catalog · Releases · MCP Registry · Integration examples

GuardMarket gives MCP clients five tools to discover versioned skills, inspect their schemas and prices, invoke with an explicit spending cap, and read wallet balances and receipts. The catalog describes 125 implemented commerce, inventory, CSV, JSON, text, date, math, statistics, encoding, validation, and unit-conversion skills.

Version 0.3.0 is published: the public repository, release installation files, and 130-page documentation site are available. The MCPB has an active MCP Registry record under io.github.zma-petterzhang/guardmarket, published on 2026-09-26. This is package metadata registration; OpenAI and Claude plugin-directory approval are separate steps.

This repository is the public client and documentation. Skill execution, accounts, and billing run in a separately operated GuardMarket backend. Installing this client does not create a backend or a wallet. A public production MCP endpoint and PyPI listing are not yet provided. The static website is documentation, not an execution endpoint. The bundled examples are synthetic and contain no account data.

Install and connect

Desktop bundle: download guardmarket-mcp-0.3.0.mcpb from release v0.3.0 and verify it against SHA256SUMS. Open it in a host that supports MCPB 0.4 and its uv runtime. The host manages Python 3.11+; the bundle has no external Python dependencies. Configure your actual backend URL in the install form. Leave the masked consumer API key blank for anonymous discovery. Hosts supporting only older MCPB formats should use the wheel/stdio installation below. This bundle still needs a running backend; it does not install the marketplace server. See bundle details.

Python 3.11+ is required. The Python client has no runtime dependencies.

python3 -m venv .venv
.venv/bin/python -m pip install "git+https://github.com/zma-petterzhang/[email protected]"
.venv/bin/guardmarket-mcp --version

For installation without Git, download the wheel from release v0.3.0, verify its SHA-256 against the release SHA256SUMS, then run python -m pip install /path/to/guardmarket_mcp-0.3.0-py3-none-any.whl. Pin a version or commit in production. On Windows use .venv\Scripts\python.exe and .venv\Scripts\guardmarket-mcp.exe.

Set GUARDMARKET_URL to your backend origin, for example http://127.0.0.1:8787 for a local instance. The client accepts HTTPS origins and numeric-loopback HTTP origins only. Redirects and environment HTTP proxies are disabled.

export GUARDMARKET_URL=http://127.0.0.1:8787
.venv/bin/guardmarket-mcp

The command waits for MCP messages on standard input; it is normally launched by your AI client. market.search and market.describe work anonymously if the backend permits public discovery. The other three tools require a consumer API key created in that backend's account settings. Provide it via the client's environment (GUARDMARKET_API_KEY) or a user-owned mode-0600 regular file (--key-file /absolute/path/key). The key-file option is POSIX-only; environment variables work on Windows. Never put a real key in a committed configuration or a chat message.

Tools and billing behavior

MCP toolPurposeAuthorization
market.searchSearch current published skills and pricesAnonymous
market.describeRead a selected version's schemas, examples, effects, and priceAnonymous
market.invokeRun a user-authorized skill with max_price_micros and idempotency_keyAPI key
market.walletRead available/reserved balances and currencyAPI key
market.receiptRetrieve an owned invocation result and billing stateAPI key

First search, then describe the exact returned skill_id, then invoke. One currency unit equals 1,000,000 micros. The documented ¥0.01 example is a demo price, not a promise about a deployment. The selected backend reports its currency, test/production mode, and actual prices. Test mode uses simulated funds. Successful invocations are chargeable; a pending or uncertain invocation may retain a hold. After an uncertain result, reuse the same idempotency key and identical arguments. Do not submit a new key to “retry.”

Example use cases: calculate a cart total with supplied tax and discount rates; validate and normalize product data; escape CSV formulas before spreadsheet export; compare inventory reorder thresholds; summarize provided numbers. These tools do not obtain live tax rates, exchange rates, stock prices, or shipping quotes. The original 125 functions transform supplied data; third-party skills may have external effects, disclosed by market.describe.

Connect an AI client

Examples and verified source references cover Claude Desktop, Claude Code, Qwen Code, Qwen-Agent, and Codex. The portable plugin.json, mcp.json, and skills/ bundle also includes Codex and Claude compatibility manifests. Install the client first so guardmarket-mcp is on the launcher PATH, or use the executable's absolute path.

ChatGPT and Claude web connectors require a deployed public HTTPS MCP endpoint and its configured authorization flow. They cannot connect to a laptop's stdio command through a repository URL. A GitHub repository, documentation website, or llms.txt does not automatically install tools in any model or guarantee indexing.

Development and release

python -m unittest discover -s tests -v
python scripts/build_docs.py
python -m pip install build
python -m build
python scripts/build_mcpb.py

CI validates Python 3.11–3.13, regenerates the static site, and builds the wheel and MCPB. Pushing a v* tag creates a GitHub release with a wheel, source archive, desktop MCPB bundle, and SHA-256 checksums; it does not publish to PyPI or any AI vendor directory. The MCPB builder uses an explicit allowlist and deterministic ZIP metadata, and prints its fileSha256 for registry preparation. GitHub Pages serves docs/ using its deployment workflow. Public catalog metadata is a versioned snapshot; the chosen backend remains authoritative at invocation time.

A separate manual GitHub Actions workflow prepares and publishes the released MCPB's metadata to the MCP Registry using GitHub OIDC. It verifies the release artifact and its checksum first. The v0.3.0 registration run succeeded, and its registry record is active. The record points to the immutable v0.3.0 release artifact; documentation updates on main do not replace that artifact. MCP Registry publication is separate from OpenAI or Claude plugin-directory review.

Security · Privacy and data handling · Client terms

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.