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Emberfeed

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Get required + optional fields per ad platform

get_platform_specs
Read-only

Returns the field requirements for a given target platform (Meta / Google / TikTok / Heureka / Zboží) and catalog type (product / hotel / flight / destination / vehicle / home_listing / media). Use this before building an output schema or running validation so you know exactly which g:* fields the platform expects.

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": {
      "platform": "<platform>"
    },
    "name": "get_platform_specs"
  }
}

Input Schema

NameRequiredDescriptionDefault
platformYesTarget ad platform — one of meta, google, tiktok, heureka, zbozi.
catalogTypeNoCatalog vertical. Default "product" covers ~95% of feeds.product

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

The annotations already establish that this is a read-only, non-open-world lookup. The description adds that the result specifies required and optional g:* fields per platform and catalog type, but it does not describe the response structure or any caching/rate-limit behavior. With annotations carrying the safety profile, this is adequate but not rich.

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 definition is two sentences and front-loads the return value before giving usage guidance. The parenthetical platform and catalog-type lists slightly duplicate the schema enums, but the structure remains clear and efficient.

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 read-only, two-parameter lookup with fully documented inputs, the description covers purpose and usage well. It could say more about the response shape, but the title already conveys that the result includes required and optional fields, and no output schema is available to explain further.

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%, and both parameters have enum descriptions that explain their allowed values. The description restates the platform and catalog-type options but does not add meaning beyond what the schema already provides. The baseline of 3 is appropriate when the schema does the parameter documentation.

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 uses a specific verb and resource, "Returns the field requirements," and names both dimensions: target platform and catalog type. It also states that the output identifies which g:* fields are expected, which clearly separates it from siblings like update_output_schema and get_validation.

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?

It gives clear timing guidance: "Use this before building an output schema or running validation." That tells the agent exactly when the tool is relevant. It does not explicitly say when not to use it or name alternatives, but the context is strong enough for reliable selection.

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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