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Glama

Facebook_ads

by io.github.pipeworx-io

Fb List Adsets

fb_list_adsets
Read-onlyIdempotent

List ad sets in a campaign (e.g., account_id: '123456789', campaign_id: '987654321'). Returns names, IDs, status, budgets, and targeting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 25)
fieldsNoComma-separated fields (default: "id,name,status,daily_budget,lifetime_budget,targeting,optimization_goal")
campaign_idYesCampaign ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNoList of ad sets
errorNoError code if connection failed
pagingNoPagination info
messageNoError message if connection failed

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "List of ad sets",
+      "items": {
+        "properties": {
+          "daily_budget": {
+            "description": "Daily budget in cents",
+            "type": "number"
+          },
+          "id": {
+            "description": "Ad set ID",
+            "type": "string"
+          },
+          "lifetime_budget": {
+            "description": "Lifetime budget in cents",
+            "type": "number"
+          },
+          "name": {
+            "description": "Ad set name",
+            "type": "string"
+          },
+          "optimization_goal": {
+            "description": "Optimization goal",
+            "type": "string"
+          },
+          "status": {
+            "description": "Ad set status",
+            "type": "string"
+          },
+          "targeting": {
+            "description": "Targeting specifications",
+            "type": "object"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "error": {
+      "description": "Error code if connection failed",
+      "type": "string"
+    },
+    "message": {
+      "description": "Error message if connection failed",
+      "type": "string"
+    },
+    "paging": {
+      "description": "Pagination info",
+      "properties": {
+        "cursors": {
+          "type": "object"
+        },
+        "next": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "campaign_id": "120393847102340"
+  },
+  {
+    "campaign_id": "120393847102340",
+    "fields": "id,name,status,daily_budget,lifetime_budget,targeting,optimization_goal,created_time",
+    "limit": 50
+  }
+]
  3. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare the tool as read-only and idempotent. The description adds value by specifying the returned fields (names, IDs, status, budgets, targeting), providing transparency about the output without contradicting annotations.

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, concise sentence with an inline example, efficiently conveying the tool's purpose and usage without unnecessary words.

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 listing tool with an output schema, the description adequately covers the essential information. It does not address pagination or limit handling, but the schema provides these details, so it is sufficiently complete.

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

Parameters2/5

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

While the input schema has 100% description coverage, the description includes an example with 'account_id' which is not a parameter in the schema, potentially misleading agents. The description does add context beyond the schema but introduces inaccuracy.

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 'List ad sets in a campaign' with a specific example, and the tool name is self-explanatory. It distinguishes from sibling tools like fb_list_campaigns and fb_list_ad_accounts by specifying the resource type (ad sets) and the required campaign context.

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

Usage Guidelines3/5

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

The description implies usage for listing ad sets within a specific campaign but does not provide explicit guidance on when to choose this tool over alternatives like fb_list_campaigns or fb_list_ad_accounts, nor does it mention prerequisites or limitations.

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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TDQS

A3.6/5.0
Disambiguation4/5

The 11 fb_* Facebook tools are clearly separated by resource (account vs campaign vs adset) and action (list vs get vs create), and the Pipeworx research tools each have distinct roles (router, grounded, profile, compare, research). However, ask_pipeworx, ask_pipeworx_beta, and deep_research overlap in routing/fan-out behavior, and ai_visibility_check vs scan_competitor_ai_presence are near-identical in purpose, creating some ambiguity.

Naming Consistency3/5

The 11 fb_* tools follow a consistent fb_verb_noun pattern (except fb_get_campaign vs fb_list_*), but the remaining 25+ tools mix verb-first (ask_pipeworx, compare_entities, resolve_entity), noun-first (entity_profile, recent_changes, polymarket_edges), and generic names (forget, recall, remember). Pipeworx tools use verb_noun mostly consistently (ask_pipeworx, discover_tools, resolve_entity) but the overall set blends two naming cultures without a unifying prefix or pattern.

Tool Count3/5

36 tools is on the heavy side for one server. The Facebook ads domain only needs ~11 tools, while the rest are a sprawling Pipeworx research/meta platform (memory, subscription, prediction-market, web-tooling, AI-visibility) that feels like several servers merged into one. Each area is internally coherent, but as a single MCP server the count is bloated.

Completeness4/5

The Facebook ads surface covers list accounts/campaigns/adsets and read campaigns/insights, but notably lacks create/update/delete operations for campaigns and adsets, so the ad-management workflow has dead ends. The Pipeworx research side is extremely complete for data lookup (router, grounded, deep research, entity profiles, comparisons, verification), though the memory/subscription tools introduce a separate domain that is only thinly supported.

Resources