Influship

search_creators

Read-onlyIdempotent

Find a creator by name/handle, while preserving legacy semantic creator search.

Use this as the default creator lookup tool when the user gives a creator-ish string but not a canonical creator UUID: a handle, partial handle, display name, creator name, or profile-ish text. This is cheap, fast, and backed by the creator lookup index.

If the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram"), prefer get_profile first because it returns the full platform profile. If you need to resolve a rough creator name or partial handle first, use this tool with query_type: "creator_lookup".

For backward compatibility, this tool still accepts the old semantic-search fields (platforms, follower/engagement filters, creator_kinds) and routes legacy calls to the semantic endpoint unless the query clearly contains a handle/profile URL. For new topical/niche discovery calls such as "fitness creators in NYC" or "vegan recipe creators with high engagement", prefer semantic_search_creators because its name is explicit and less likely to be confused with exact creator lookup.

Examples:

User: "Find @cris" -> use this tool with query "cris" and query_type "creator_lookup".
User: "Who is that fitness coach called Jane?" -> use this tool with query "Jane" and query_type "creator_lookup".
User: "Pull @niickjackson on Instagram" -> use get_profile with platform "instagram" and username "niickjackson".
User: "Find news creators with 1M+ followers" -> use semantic_search_creators, not this tool.

Returns either autocomplete-style creator lookup results or legacy semantic results, depending on routing. Use returned creator IDs with get_creator, find_lookalike_creators, or match_creators; use returned platform usernames with get_profile or get_posts.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "search_creators"
  }
}

Input Schema

Name	Required	Description	Default
`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.
`scope`	No	Which linked platforms to include in each lookup result.	all_platforms
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`platform`	No	Optional platform to narrow username matching.
`verified`	No	When set, only return verified or unverified creators.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`platforms`	No	Platforms to search. Omit for all.
`query_type`	No	Use creator_lookup for specific names/handles and semantic_discovery for topical/niche discovery. Auto routes exact handles and profile URLs to lookup, and keeps legacy semantic-search behavior otherwise.	auto
`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.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.
`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

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedNov 5, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "query"
-]New value: +[
+  "query",
+  "context",
+  "llm_model"
+]

First observedMay 28, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, closed-world behavior. The description adds real value beyond them: it discloses cost/speed characteristics, that output shape varies by routing (autocomplete vs. legacy semantic results), and which downstream tools consume which returned identifiers. It does not mention rate limits or result ordering, keeping it short of a 5.

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 one-line purpose, then routing rules, then a compact example block – a good structure for a tool with 15 parameters. There is mild redundancy in restating the legacy/backward-compatibility behavior and explaining twice why semantic_search_creators is preferable for discovery, but no sentence is wasted outright.

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?

Given 15 parameters, 3 required, existing output schema, and a non-obvious dual routing mode, the description covers what an agent needs: routing decision, return-type variability, and follow-up tools to use with returned IDs/output. It doesn't fully reconcile the handle-handling ambiguity with the schema, which is the one completeness gap.

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 baseline is 3, but the description genuinely adds semantics the schema lacks: it explains routing behavior for query_type, clarifies that platforms/follower/engagement/creator_kinds fields are legacy pass-throughs to the semantic endpoint, and ties query inputs to concrete examples. However, it conflicts with the schema's own `query` description, which says exact handles must NOT be passed here while the tool description instructs the agent to do exactly that.

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?

Opens with a concrete verb+resource ("Find a creator by name/handle") and explicitly frames itself against siblings by name (get_profile, semantic_search_creators). The dual-mode framing ("while preserving legacy semantic creator search") makes the tool's identity slightly ambiguous, since it partly overlaps with semantic_search_creators, which keeps it from a 5.

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?

Explicitly states when to use it (creator-ish string but no canonical UUID), when to prefer get_profile (exact handle on a specific platform), and when to prefer semantic_search_creators (topical/niche discovery), backed by four worked examples. Both inclusion and exclusion conditions, plus named alternatives, are covered.

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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