Influship

semantic_search_creators

Read-only

Semantic discovery search for influencers/content creators using natural-language queries.

Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria (e.g., "fitness creators in NYC", "vegan recipe creators with high engagement", "tech reviewers who cover phones"). The query is matched against creator profiles, extracted facts, and visual style via hybrid vector search.

Do not use this for exact handles, usernames, or known creator names. If the user gives a specific platform and handle (for example "@niickjackson on Instagram"), use get_profile first. For rough name/handle lookup, use search_creators. For multiple known handles, use lookup_profiles. Semantic search can return lookalike or topical matches and is allowed to miss an exact username.

Examples:

User: "Find news creators with 1M+ followers" -> use this tool.
User: "Find creators in LA who make cinematic travel videos" -> use this tool.
User: "Pull @niickjackson on Instagram" -> use get_profile, not this tool.
User: "Is @niickjackson a fit for Pixel?" -> use get_profile first, optionally get_posts, then match_creators.

Returns a ranked list of creators (id, platform, username, follower count, engagement rate, top categories, evidence facts). Use the flat follower, engagement-rate, and verified fields to constrain results when the user gives concrete numeric constraints.

Use find_lookalike_creators instead when you want creators SIMILAR to known ones. Use match_creators when you want to SCORE specific creators against a brief.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum results to return.
`query`	Yes	Natural-language semantic discovery query by topic, niche, audience, geography, or content style. Do not pass exact handles or usernames here; use get_profile, lookup_profiles, or autocomplete_creators instead.
`verified`	No	When set, only return verified or unverified creators.
`platforms`	No	Platforms to search. Omit for all.
`creator_kinds`	No	Optional creator kind filter. Omit for no creator-kind filter.
`max_followers`	No	Maximum follower count.
`min_followers`	No	Minimum follower count.
`max_engagement_rate`	No	Maximum engagement rate as a percentage from 0 to 100.
`min_engagement_rate`	No	Minimum engagement rate as a percentage from 0 to 100.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`data`	No
`count`	No
`results`	No
`has_more`	No
`not_found`	No
`next_cursor`	No
`suggested_followups`	No

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds meaningful behavioral context beyond annotations: it explains the hybrid vector search mechanism, states that results may be lookalike/topical matches, and acknowledges that exact usernames may be missed. It also advises using flat numeric fields to constrain results, which clarifies expected behavior.

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 long but well-structured and every sentence earns its place. It opens with a clear purpose, gives concrete usage rules, includes illustrative examples with user queries, and differentiates from siblings. The examples are compact and highly informative, making the length appropriate for the tool's complexity.

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 complexity, its 9 parameters, and a rich sibling landscape, the description is complete. It covers the query semantics, return value summary, filtering approach, and exclusions, and it complements the existing output schema and annotations. No critical usage context is missing.

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%, so the baseline is 3. The description adds value beyond the schema by explaining the nature of the query parameter (natural-language, topic-based) and by explicitly instructing to use the flat follower, engagement-rate, and verified fields for numeric constraints. This enriches parameter understanding, though the schema already documents each parameter well.

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 states a specific verb and resource: 'Semantic discovery search for influencers/content creators using natural-language queries.' It clearly distinguishes itself from sibling tools by explicitly naming alternatives like get_profile, search_creators, lookup_profiles, find_lookalike_creators, and match_creators.

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?

The description provides explicit when-to-use guidance: 'Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria.' It also gives explicit when-not-to-use instructions and names alternative tools for exact handle lookup, similar-creator search, and scoring creators against a brief.

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.

semantic_search_creators

Input Schema

Output Schema

TDQS

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TDQS

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