Search Meta's ad-interest targeting catalog (returns interest ids + audience sizes). Use when designing a Meta ad draft and you need valid {id, name} targeting pairs for create_meta_ad_draft — e.g. search "pharmacy" or "compounding".
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Interest keyword, e.g. "pharmacy", "healthcare compliance" | |
| companyId | Yes | FreedomOS company id to act within (you must be a member). Required for company-scoped tools. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns data (interest ids, audience sizes) but does not explicitly state it is read-only or free of side effects. It also lacks details on pagination, rate limits, or authorization beyond the required companyId parameter. The description is adequate but could be more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences. The first sentence front-loads the core action and output, while the second provides essential usage guidance. No redundant or extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the two required parameters, no output schema, and no annotations, the description adequately explains the tool's purpose, usage context, and output structure (interest ids and audience sizes). However, it does not detail the exact format of the response, which could help an agent parse the results. Still, it is nearly complete for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear descriptions for both parameters. The description adds context by stating the catalog is for Meta ads and gives example queries, but this does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search'), the resource ('Meta's ad-interest targeting catalog'), and what it returns ('interest ids + audience sizes'). It effectively distinguishes itself from sibling tools like create_meta_ad_draft by specifying its role in providing valid targeting pairs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool ('when designing a Meta ad draft') and ties the output to a specific sibling tool ('create_meta_ad_draft'). It also provides concrete examples ('search 'pharmacy' or 'compounding''). While it does not explicitly state when not to use, the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.
Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.
297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.
The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.