Check an x402 seller before paying: verdict, failure rate and evidence from paid calls on Base.
Feegate AI Agent data marketplace catalog & platform metrics is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
What it does
Pay-per-call MCP data products listed on Feegate. Free discovery, x402 USDC paid queries on Base.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http feegate-ai-agent-data-marketplace-catalog-pla-mcp https://feegate.org/mcp
# 2 — ask your agent for something these tools do
# ping, fee_quote, list_products and 3 more
Feegate AI Agent data marketplace catalog & platform metrics costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http feegate-ai-agent-data-marketplace-catalog-pla-mcp https://feegate.org/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
6 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
健康检查,免费。返回服务状态、收款地址、网络与价目表。
No parameter schema on file.
Feegate 合作方案与结算规则报价(免费):三档抽成(Self-serve 50% / Managed 40% / Enterprise 30%)、一次性服务费、四项单项服务价格、结算周期与最低结算额。给数据持有方推荐合作方式前先调这个拿权威报价。
No parameter schema on file.
列出 Feegate 数据市场全部在售数据产品(免费):id、名称、供应商、类目、单价、是否认证。付费深查前先调这个拿准确的 product_id。
No parameter schema on file.
查单个数据产品的完整档案(按次计费 $0.002 USDC):提供的全部 MCP 工具清单、累计调用次数、买家数、销售额、可用率、数据许可、结算地址、简介。示例: product_detail(product_id='gpu-market')。
| Parameter | Type | Required | Notes |
|---|---|---|---|
| product_id | string | yes | — |
Feegate 数据市场全量目录(按次计费 $0.005 USDC):全部在售数据产品及其类目、单价、工具清单、调用/买家/销售额、可用率。适合做市场全景分析或采购比价。
No parameter schema on file.
Feegate 平台自身运营指标(按次计费 $0.005 USDC,30 天窗口):真实访问量、独立访客、AI 爬虫 / 搜索引擎抓取明细(哪家 AI 来抓过、抓了多少次)、热门页面、地区分布。衡量'这个市场有没有真实 Agent 流量'时用。
No parameter schema on file.
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Feegate AI Agent data marketplace catalog & platform metrics or to call it, at any volume.
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| coverage-honesty | manifest | inspection depth (manifest) — how much of the target the scan could see | −4 |
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
The scan read each tool’s name, description, input schema and annotations exactly as the server listed them the last time it answered us; no code was reviewed. We ask the server again about once a month, and a list that changed is graded again.
The implementation source was not read — this reflects the declared tool metadata only.
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| ping | no tags | |
| fee_quote | no tags | |
| list_products | no tags | |
| product_detail | no tags | |
| marketplace_catalog | no tags | |
| platform_stats | no tags |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.0.1.
No release note was published with this version.
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Any signed-in account that does not publish Feegate AI Agent data marketplace catalog & platform metrics can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.