MCP HeartbeatTHE MCP OBSERVATORY
Public workspace

Explore

Evidence for your next connection.

Independent measurements of remote MCP servers. Every observation has a timestamp.

Read-only probes

MCP server · Code & DevOps

io.github.vistoya/market

Search and get fashion products recommendations across multiple e-ecom stores

Checks passed
13 / 22

1 failed · 8 not measured

Median response
412 ms

Median of successful probes over 7 days

30-day uptime
Not measured

Share of countable probes over 30 days, including daily rollups. Auth challenges are excluded.

Discovered tools
6

Tool definitions returned by this server

Independent monitoring

Health & checks

Status history

Responded

The server completed its last measured handshake. This is an observation, not a guarantee.

Last checked
Check summary by test group
Test areaPassedFailedUnmeasured
ConnectionCan a client connect and discover tools?
500
Reliability & speedDoes it respond consistently and quickly?
004
Access & authenticationWhat is required to use this server?
102
Tool qualityAre the tool definitions usable by an agent?
400
Publisher identityCan the publisher be traced to its source?
312

Unmeasured means there is not enough evidence to decide. How these checks work

Inspect individual checks 22 recorded

Core

  • Pass
    Reachable

    Latest probe returned HTTP 200 in 412 ms.

  • Pass
    Valid handshake

    protocolVersion 2025-11-25, serverInfo.name v-marketplace.

  • Pass
    Lists tools

    tools/list returned 6 tools.

  • Pass
    Current protocol

    Negotiated 2025-11-25. The bar is 2025-06-18 or newer.

  • Pass
    HTTPS

    The endpoint URL is https.

Performance

  • Not measured
    Fast (p50)

    Median latency 412 ms over 1 samples in 7 days. The bar is 1000 ms. Need at least 5 samples.

  • Not measured
    Consistent (p95)

    95th percentile 412 ms over 1 samples in 7 days. The bar is 3000 ms. Need at least 20 samples.

  • Not measured
    Reliable (uptime)

    1 of 1 attempts returned a tool list in 30 days (100%). The bar is 99%. Need at least 30 samples.

  • Not measured
    Stable tool list

    Fewer than two successful probes in 30 days, so tool-list stability is not measured.

Access

  • Pass
    Open access

    tools/list succeeded with no credential, and the record declares none.

  • Not measured
    Standard auth

    Auth is not required, so the OAuth metadata check does not apply.

  • Not measured
    Free tier cited

    No publisher quote has been checked.

Tools

  • Pass
    Tools described

    Every tool description is at least 20 characters.

  • Pass
    Valid input schemas

    Every inputSchema is a JSON Schema object.

  • Pass
    Parameters documented

    48 of 49 parameters have a description (98%). The bar is 80%.

  • Pass
    Safety hints

    Every tool sets readOnlyHint or destructiveHint.

Publisher

  • Pass
    Verified namespace

    Listed in the Official MCP Registry.

  • Not measured
    Website matches publisher

    This namespace is not a reverse domain, so the website comparison does not apply.

  • Pass
    Source repo linked

    A repository URL is on file.

  • Pass
    Repo owner matches

    The GitHub repository owner matches the namespace login.

  • Fail
    Recently updated

    Updated 2026-04-11T20:40:35.292116Z, which is older than 180 days.

  • Not measured
    Open-source licence

    The GitHub licence lookup has not returned an OSI result yet.

Start using io.github.vistoya/market

Connect to this server

Choose your client, copy the configuration, then enable the server in your client’s MCP settings.

Run this command in your terminal, then restart Claude Code.

Claude Code

claude mcp add --transport http io-github-vistoya-market https://api.vistoya.com/mcp

Capabilities

Tools 6

discover_brands

Find fashion brands using natural language, structured filters, or both. Best for queries like "Italian streetwear brands", "Scandinavian minimalist brands", "Japanese technical outerwear", "brands with avant-garde tailoring", or qualified similarity such as "brands like Rick Owens for technical outerwear". For a plain "brands like X" request, use find_similar_brands. Country adjectives ("Italian", "Scandinavian", "Nordic", "Japanese", "Iberian", "Benelux") are parsed server-side into shipping-origin filters; you don't need to translate them to ISO codes. `query` is optional — provide a query, structured filters, or both. Brand country/shipping signals are best-effort and separate from product availability.

Discovered
discover_products

Find fashion products using natural language and/or structured filters. Provide a `query` for semantic ranking via multimodal text+image embeddings ("oversized wool coat", "black leather jacket", "minimalist gold jewelry", "linen shirt for a beach wedding under $200") — best for open-ended discovery. Keep queries concrete: noun-led with up to one or two modifiers works best ("summer linen shirt" beats "breathable linen shirt perfect for summer"). Provide only structured filters (category, brand, colors, gender, price, etc.) for pure browse — results are recency-ranked and paginate cleanly. Combine both for filtered semantic search. At least one of query or a filter must be provided. Example calls (notice the sparse filter population — descriptive attributes stay in `query`, not in structured fields): - "linen wedding guest dress under $200" → {query: "linen wedding guest dress", gender: "women", max_price: 200, materials: ["linen"]} - "wool coat under $300" → {query: "wool coat", gender: "women", max_price: 300, materials: ["wool"]} - "browse women's black dresses $100-$300" → {gender: "women", category: "clothing/dresses", colors: ["black"], min_price: 100, max_price: 300} - "Acne Studios outerwear" → {query: "outerwear", brand: "Acne Studios", gender: "women"} Returns compact product cards: AI-generated summary, price, images, tags, and compact availability by color/size; variant price differences are nested under the availability dimension that determines price. For merchant description, store info, SKU-level variants, exact variant prices, and all product images, call get_product with a product ID from these results. Multi-currency prices supported (e.g. "under 200 zł" or min_price=200 + currency="PLN"); returned prices render in the requested currency when provided.

Discovered
find_similar_brands

Given an active catalog brand name or merged alias, find similar brands using brand-profile vectors generated during product indexing. Unknown or ambiguous seeds return no brands. Returns up to 20 brands.

Discovered
find_similar_products

Given a product ID, find similar products across the entire catalog. Useful for "more like this" recommendations or finding alternatives. Returns compact product cards, not full variant detail; call get_product for SKU-level variants, exact variant prices, merchant description, store info, and all images. Returns page and hasNextPage. Returns up to 20 results per page, paginated (max 3 pages).

Discovered
get_filters

Returns available filter values in the catalog. By default returns categoryTree plus brands, colors, materials, genders, occasions, seasons, styles, silhouettes, currencies, and price range. Use "fields" to request only specific dimensions — faster and less data. "categoryTree" is a flat DFS-ordered list of { value, label } entries; hierarchy is encoded in the value slug (e.g. "clothing/jackets/bomber-jackets"), parents appear before descendants, and every value can be passed directly to discover_products.category. Use "brand_search" to search brands by prefix instead of listing all. Pass "gender" to scope categoryTree to that gender (women/men/girls/boys); omit to see the merged union.

Discovered
get_product

Get the detailed response for a specific product ID. Use this after discover_products or find_similar_products when you need merchant description, store info, all images, SKU-level availability variants, SKU, colorKey/size matrix, exact variant prices/compareAtPrice in the requested currency, and the direct link to purchase.

Discovered

Recent measurements

Probe history

View status
Latest stored probes. Raw probe rows are kept for 14 days.
CheckedResultResponseTools
Connected412 ms6