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autocomplete_creators

Read-only

Autocomplete creator names, usernames, or display names from partial input.

Use this for fast lookup when the user types a partial handle or name and you need to resolve it to canonical creator IDs (e.g., "find @cris" or "who's that fitness coach called Jane?"). Cheap and fast — prefer over search_creators for handle-style queries where the user already knows roughly who they want.

Use get_profile instead when the user gives an exact platform+username pair. Use search_creators for the same fuzzy creator lookup behavior with a less typeahead- specific name. Use semantic_search_creators only for discovery by topic, niche, audience, geography, or content style, not for resolving a known creator.

Examples:

  • User: "Who is that fitness coach called Jane?" -> use this tool.

  • User: "Find @cris..." -> use this tool to resolve the partial handle.

  • User: "Pull @niickjackson on Instagram" -> use get_profile, not this tool.

Returns a short list of matching creators with their IDs, platforms, and display names. Use the IDs returned here as input to get_creator, find_lookalike_creators, or match_creators for downstream operations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query (min 2 characters)
limitNoMaximum results to return
scopeNoWhich platforms to include in resultsall_platforms
platformNoFilter by platform

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
countNo
resultsNo
has_moreNo
not_foundNo
next_cursorNo
suggested_followupsNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: 'Cheap and fast', 'Returns a short list of matching creators with their IDs, platforms, and display names', and shows how results feed into downstream tools. No contradictions.

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?

The description is longer than typical but well-structured: purpose, usage guidance, examples, and downstream usage. Every section serves a distinct function and there's no fluff. Slightly verbose but highly informative.

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?

The description covers purpose, usage context, alternatives, examples, return format, and downstream integration. Given the tool's simplicity and the presence of an output schema, this is fully complete.

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 each parameter. The description reinforces the meaning of q by referencing 'partial input' but doesn't add details about limit, scope, or platform 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Autocomplete creator names, usernames, or display names from partial input.' It explicitly differentiates from siblings by stating 'prefer over search_creators', 'Use get_profile instead', and 'Use semantic_search_creators only for discovery...'.

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

Usage Guidelines5/5

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

Explicit when to use: 'Use this for fast lookup when the user types a partial handle or name' and 'Cheap and fast — prefer over search_creators'. Also explicit exclusions: 'Use get_profile instead when the user gives an exact platform+username pair' and 'Use semantic_search_creators only for discovery...'.

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

A3.9/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: `autocomplete_creators` and `search_creators` both claim the exact same example query ('Who is that fitness coach called Jane?') as their primary use case, creating direct routing conflicts. `get_creator` and `get_profile` also overlap heavily for exact platform+username lookups, with descriptions admitting the choice depends on whether 'profile metrics are the main need' — a thin distinction. `search_creators` further muddies things by dual-routing to legacy semantic search, making it a hybrid that competes with both `autocomplete_creators` and `semantic_search_creators`.

Naming Consistency4/5

The naming follows a mostly consistent verb_noun snake_case pattern: `get_*` covers record fetching, with clear singular/batch pairs like `get_instagram_post`/`get_instagram_posts` and transcript variants. Minor deviations exist (`semantic_search_creators` prefixes a modifier, and `autocomplete_`, `find_`, `match_`, `lookup_`, `render_` each introduce different verbs), but the style is uniform and the verb typically reflects the operation type.

Tool Count3/5

At 28 tools the server is heavy, but the scope is genuinely broad — three platform-specific data surfaces (Instagram, TikTok, YouTube), each requiring profile/video/transcript/listing operations, plus creator search, matching, and rendering. The count is inflated by redundancy, though: four `render_*` tools that could collapse into one parameterized tool, and batch variants of the Instagram raw-data endpoints. It is borderline acceptable for the platform-multiplied domain rather than chaotic bloat.

Completeness4/5

The tool surface covers the full read-only creator workflow: fuzzy lookup (autocomplete/search), exact profile fetch (get_profile/lookup_profiles), discovery (semantic_search/find_lookalike), fit scoring (match_creators), content evidence (get_posts), and presentation (render_*). Notable gaps include no Instagram-specific profile endpoint (odd given TikTok/YouTube have dedicated ones), no YouTube comments, and no audience-demographic data, but agents can complete realistic workflows without dead ends.