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ScrapeCreators MCP Server

ScrapeCreators MCP Server

by thenavidm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Marketplace Location Search

facebook_marketplace_marketplace_location_search

Search Facebook Marketplace locations or cities to obtain coordinates for the Marketplace Search endpoint. This paid API call requires confirm=true.

Instructions

Searches Facebook Marketplace locations/cities and returns coordinates you can use with the Marketplace Search endpoint. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

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": {
      "query": "<query>"
    },
    "name": "facebook_marketplace_marketplace_location_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesLocation search query
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.2/5.0
Behavior4/5

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

Adds real value beyond annotations: it warns about paid credit consumption, the confirm=true requirement, and clarifies the read-like POST non-publishing behavior, which explains why readOnlyHint=false despite looking like a read. Does not cover rate limits or error behavior, so not a 5.

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?

Two dense sentences with zero filler; the output/downstream use is front-loaded and the cost/confirm constraints follow. Every clause carries actionable information.

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 3-param tool with no output schema, the description covers the return value (coordinates), the cost model, and the confirmation requirement. Only minor gaps remain (e.g. what happens if confirm is omitted, coordinate format).

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 description coverage is 100%, so the schema already documents query, account, and confirm. The description restates the confirm=true constraint but adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb and resource ('Searches Facebook Marketplace locations/cities') plus the output ('returns coordinates') and its downstream use ('with the Marketplace Search endpoint'). This distinguishes it clearly from the sibling facebook_marketplace_marketplace_search, which consumes those coordinates.

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?

Explains the role in a workflow (prerequisite for Marketplace Search) and states a hard precondition ('requires confirm=true'). It lacks explicit when-not-to-use guidance, but the context is clear enough to route correctly.

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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