match_creators
Score how well specific creators fit a campaign brief or search intent.
Use this when the user already has candidate creators in mind and wants to evaluate fit (e.g., "rate these 5 creators for a vegan cookbook launch", "which of these is the best match for my crypto audience?"). For each creator the API returns a match score (0-1), a good/neutral/avoid decision, and structured reasons.
Pass candidates in creator_ids (canonical UUIDs) and/or profiles
(platform + username). intent_query is the brief the LLM reasons against;
intent_context is optional extra context (target audience, brand values,
prior collabs).
Use semantic_search_creators when you don't have candidates yet and need topical
or niche discovery. Use search_creators first when you only need to resolve rough
creator names/handles into candidates.
Use find_lookalike_creators when you want creators similar to known good fits.
Examples:
User: "Is @niickjackson a fit for Pixel?" -> use this tool after resolving the exact Instagram profile with
get_profile; callget_postsfirst if recent content context is needed.User: "Rate these five creators for a vegan cookbook launch" -> use this tool.
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": {
"intent_query": "<intent_query>",
"context": "<context>",
"llm_model": "<llm_model>"
},
"name": "match_creators"
}
}Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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." | |
| profiles | No | Candidate creators identified by platform and username. | |
| 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. | |
| creator_ids | No | Candidate creators identified by canonical Influship creator UUID. | |
| intent_query | Yes | Campaign brief or matching intent. | |
| intent_context | No | Optional extra campaign context, audience, brand values, or prior collabs. | |
| 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. |
Output 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 |