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thenavidm

ScrapeCreators MCP Server

by thenavidm

Search Suggestions

tiktok_search_suggestions

Fetch TikTok autocomplete suggestions for any search query to reveal related terms and inform keyword research. Requires confirm=true because it may consume paid API credits.

Instructions

Gets the autocomplete suggestions TikTok shows while someone is typing in search. Returns suggestions, a clean array of suggested search terms and the most useful metadata for each suggestion. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query to get suggestions for
regionNoRegion code for suggestions
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.9/5.0
Behavior4/5

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

Goes beyond the annotations by disclosing the paid-credit cost and the confirm=true requirement, plus clarifying that 'read-like POST requests do not publish to social platforms' — useful context given readOnlyHint=false and openWorldHint=true. Not flagged as contradictory since the POST nature is consistent with the non-readonly hint.

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?

Front-loaded with the core purpose, then the return value, then the cost/confirm constraints. Three sentences with minimal waste; the final sentence on POST semantics is slightly tangential but earns its place by pre-empting a safety concern.

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, the description usefully names the returned `suggestions` array and its content. Combined with credit/confirm caveats, this is sufficient for correct invocation, though it omits any note on result limits or region effects.

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 100%, so the schema already documents all four parameters including confirm and account. The description only reiterates confirm=true and adds no syntax or format meaning beyond the schema, matching the baseline 3.

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?

States a specific verb and resource — 'Gets the autocomplete suggestions TikTok shows while someone is typing in search' — which clearly distinguishes it from content-oriented siblings like tiktok_search_by_keyword, tiktok_search_by_hashtag, or tiktok_top_search. An agent can identify the purpose without opening the schema.

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?

Provides a usage prerequisite (consumes paid credits, requires confirm=true) but gives no explicit when-to-use guidance or naming of alternatives such as tiktok_top_search. Usage is implied by the purpose rather than stated comparatively.

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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