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Silicon MCP

Silicon MCP

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by Silicon-Store
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TypeScript
Hybrid

Look up product

get_product

Fetch price, availability, images, specs, and identifiers (GTIN/MPN) from hundreds of online stores by providing a product URL.

Instructions

Look up a product by its URL — price, availability, images, specs, and identifiers (GTIN/MPN) — from hundreds of online stores.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "get_product"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesThe product page URL to look up.
fieldsNoOptional subset of fields to return.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It conveys that this is a read-only operation ('look up') and adds context about scope ('from hundreds of online stores') and output fields. However, it does not disclose potential limitations such as ambiguity in whether results are aggregated across stores, latency, or failure modes for invalid URLs, leaving some behavioral traits implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the main action and lists key data points in a dash-separated list. Every word contributes meaning, with no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and no output schema, the description is adequately complete. It specifies the input (URL), the output categories (price, availability, images, specs, identifiers), and the scope (hundreds of stores). It doesn't cover edge cases like invalid URLs, but that's a minor gap for a lookup tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage with descriptions for both parameters (url and fields). The description adds no additional parameter-level semantics, so the baseline score of 3 is appropriate since the schema already handles the burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Look up a product by its URL' with a specific verb and resource. It lists the exact data returned (price, availability, images, specs, identifiers), distinguishing it from sibling tools like search_products which likely search by query, and compare_prices which focuses on price comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: use this tool when you have a product URL and want detailed product information. It does not explicitly name alternatives or exclusions, so it falls short of a 5, but the condition ('by its URL') makes the usage context unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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