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jmacaggi-gfm

ads-mcp-server

by jmacaggi-gfm

get_campaign_settings

Retrieve current campaign settings and 56-day change history for Google Ads, Meta, or both platforms.

Instructions

Current campaign settings + 56-day change history per platform.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYes

Implementation Reference

  • The `get_campaign_settings_impl` function is the handler for the 'get_campaign_settings' tool. It accepts a `platform` parameter (google|meta|both), delegates to `get_google_ads_report_impl` or `get_meta_ads_report_impl` with `include_campaign_settings=True`, and returns the campaign_settings sub-object for each platform.
    def get_campaign_settings_impl(platform: str) -> dict[str, Any]:
        if platform not in ("google", "meta", "both"):
            return error_response(platform, "platform must be google|meta|both")
        out: dict[str, Any] = {"fetched_at": _now_iso()}
        if platform in ("google", "both"):
            out["google"] = get_google_ads_report_impl(
                "yesterday", "account", include_campaign_settings=True
            ).get("campaign_settings", [])
        if platform in ("meta", "both"):
            out["meta"] = get_meta_ads_report_impl("yesterday", "account").get(
                "campaign_settings", []
            )
        return out
  • The tool is registered as an MCP Tool in the `list_tools` handler with name='get_campaign_settings', description about current campaign settings + change history, and an inputSchema requiring a 'platform' enum (google|meta|both).
    Tool(
        name="get_campaign_settings",
        description="Current campaign settings + 56-day change history per platform.",
        inputSchema={
            "type": "object",
            "properties": {
                "platform": {
                    "type": "string",
                    "enum": ["google", "meta", "both"],
                }
            },
            "required": ["platform"],
        },
    ),
  • In the `call_tool` handler, when the tool name is 'get_campaign_settings', the function dispatches to `get_campaign_settings_impl` with the 'platform' argument from the request.
    elif name == "get_campaign_settings":
        result = get_campaign_settings_impl(arguments["platform"])
  • The `error_response` helper used by the handler to return error dicts when platform is invalid.
    def error_response(platform: str, message: str, **extra: Any) -> dict[str, Any]:
        return {
            "error": message,
            "platform": platform,
            **extra,
        }
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It discloses that the tool returns both settings and a 56-day history, but does not indicate whether it is read-only, any authentication needs, rate limits, or what triggers destructive actions. The behavior is partially described but lacks completeness.

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

Conciseness3/5

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

The description is a single sentence, making it very concise, but it omits important details such as parameter semantics and usage context. Conciseness is achieved at the expense of completeness.

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

Completeness2/5

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is insufficient. It does not explain the output structure, whether the data is aggregated per platform, or provide examples. The lack of detail makes it hard for an AI agent to fully understand the tool's capabilities.

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

Parameters1/5

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

The input schema has one parameter 'platform' with enum values, but the description does not explain the parameter's meaning beyond 'per platform'. It fails to clarify what 'both' implies or how the parameter affects the output. With 0% schema description coverage, the description adds no value over the schema itself.

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

Purpose4/5

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

The description clearly states the tool retrieves 'current campaign settings + 56-day change history per platform', which is a specific verb+resource. However, it does not explicitly differentiate itself from sibling tools like get_google_ads_report or get_meta_ads_report, which are focused on ads reports rather than settings.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as get_google_ads_report or get_meta_ads_report. It does not mention exclusion criteria or context for usage.

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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