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LinkedIn and TikTok ads

linkedin_tiktok_ads

An advertiser's ads on LinkedIn (from LinkedIn's Ad Library: headline, text, type, start date) or TikTok (its ads and boosted posts with views and likes). platform: linkedin or tiktok. advertiser: the company's name, or its website (needed for TikTok). Saved in the user's Puley history.

Input Schema

NameRequiredDescriptionDefault
limitNo
countryNoALL
platformYes
advertiserYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior3/5

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

The annotations declare readOnlyHint=false with destructiveHint=false and openWorldHint=true; the description usefully explains the write side by noting results are 'Saved in the user's Puley history', which reconciles the non-read-only hint. It also sketches what each platform returns. It does not disclose coverage limits, rate behavior, or what happens when an advertiser isn't found, which matters for an open-world lookup.

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?

Three tight sentences, front-loaded with what the tool returns before the parameter hints. The parenthetical source attribution and field list are dense but earn their place; minor run-on quality from stacked parentheses.

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?

With no output schema and only four flat parameters, the description covers the core of what an agent needs: what comes back per platform and how to specify platform/advertiser. The remaining gap is the undocumented limit and country parameters, which could change result sets silently.

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 description coverage is 0% across four parameters, so the description must carry the load; it explains 'platform' (linkedin or tiktok) and 'advertiser' (company name, or website required for TikTok), which is genuinely additive. It says nothing about 'limit' or 'country' (default 'ALL'), leaving half the parameters undocumented anywhere.

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 names the resource (advertiser ads on LinkedIn's Ad Library or TikTok's ad/boosted-post inventory) and the fields returned (headline, text, type, start date; views and likes), so an agent can distinguish it from generic siblings like find_ads. It would be a 5 if it explicitly contrasted itself with those siblings.

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?

It states the accepted values for platform and how to identify the advertiser, but gives no guidance on when to pick this tool versus find_ads, ugc_ad, or product_ad_pack. There are no exclusions or prerequisites mentioned.

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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