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merch-connector

merch-connector

by grahamton
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

ask_page

Ask natural language questions about a storefront page and get answers grounded in product data, facets, performance metrics, and a screenshot. Reuses cached scrapes to save time.

Instructions

Ask any natural language question about a storefront page. The AI sees the full product data, facets, performance metrics, and a screenshot. If scrape_page was called on this URL within the last 10 minutes, the cached data is reused — no re-scrape. Use this for ad-hoc questions like "which products are on sale?", "can users filter by size?", "what's the average price?", or "is this page fast enough?". Tip: for slow or local AI models, call scrape_page first, then ask_page — the scrape will be reused.

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>",
      "question": "<question>"
    },
    "name": "ask_page"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesFull http/https URL to scrape and ask about.
depthNoPages of pagination to scrape first (default 1).
questionYesYour question about the page, in plain language.
max_productsNoMax products per page (default 10).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does well: it discloses caching behavior (10-minute reuse), what data the AI sees (product data, facets, performance metrics, screenshot), and a practical usage tip. No side effects or contradictions are present.

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

Conciseness4/5

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

The description is slightly long but every sentence adds value: what it does, what the AI sees, caching, use cases, and a tip. It is well-structured and front-loaded with the core purpose, though it could be trimmed slightly without losing essential info.

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?

Given no output schema and no annotations, the description adequately covers the tool's behavior, data exposure, caching, and ideal use cases. It does not mention error handling or edge cases, but for a query tool with 4 parameters and no output schema, it is sufficiently complete.

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?

Schema coverage is 100%, so each parameter is already described. The description adds some context by explaining that the AI sees full product data and that a cached scrape may be reused, but it does not elaborate on depth or max_products beyond the schema. Baseline 3 is appropriate.

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 a specific verb ('Ask') and resource ('storefront page'), and differentiates from siblings like scrape_page and interact_with_page by emphasizing natural language ad-hoc questions. It also lists concrete example questions, making the tool's purpose unmistakable.

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 usage context with example questions and a performance tip about calling scrape_page first. However, it does not explicitly state when not to use this tool or compare it to alternatives like analyze_products, so it lacks explicit exclusions.

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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