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Glama

Facebook_ads

by io.github.pipeworx-io

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "key": "user_research_topic"
+  },
+  {}
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context beyond annotations: scoping to identifier ('anonymous IP, BYO key hash, or account ID') and the listing behavior when key is omitted. No contradictions with 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 four sentences, dense with information, and front-loaded with the core purpose. Every sentence adds value, and no words are wasted.

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

Completeness5/5

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

Given the tool's simplicity (one optional parameter, no output schema, good annotations), the description fully covers purpose, usage, behavior, and relationships. No gaps remain for an AI agent to misinterpret.

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

Parameters4/5

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

Schema coverage is 100% with a clear parameter description. The description adds value by explaining the effect of omitting the key (list all) and the scoping context, which enhances understanding beyond the schema alone.

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 specific verbs ('Retrieve', 'list') and clearly states the resource ('value previously saved via remember', 'all saved keys'). It explicitly distinguishes from sibling tools 'remember' and 'forget' by mentioning them by name.

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 context for when to use the tool ('look up context the agent stored earlier') and explicitly pairs it with 'remember' and 'forget'. It does not explicitly state when not to use it, but the context is sufficient for an AI agent to decide.

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