Influship

find_lookalike_creators

Read-only

Find creators SIMILAR to one or more seed creators.

Use this when the user already knows a creator they like and wants more like them (e.g., "find creators like @therock", "find more creators like these three I just booked"). Seeds are blended via creator-profile + visual-style + fact embeddings to surface similar accounts.

Seeds are passed in seed_creator_ids (canonical UUIDs) and/or seed_profiles (platform + username; resolve handles via autocomplete_creators first if needed). Returns a ranked list of similar creators with scores. limit caps results (default 25, max 100). Use the flat follower, engagement-rate, and verified fields to constrain results. A seed_not_found response means the supplied creator is not available for similarity matching; choose another seed instead of retrying it.

Use semantic_search_creators instead when you have a topic/niche but no seed. Use match_creators when you have specific candidates and want to score their fit against a brief.

Examples:

User: "Find creators like @niickjackson on Instagram" -> use this tool with seed_profiles: [{ platform: "instagram", username: "niickjackson" }].
User: "Find news creators with 1M+ followers" -> use semantic_search_creators, not this tool.

Input Schema

Name	Required	Description
`limit`	No	Maximum results to return.
`cursor`	No	Pagination cursor from a previous response.
`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."
`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.
`max_followers`	No	Maximum follower count.
`min_followers`	No	Minimum follower count.
`seed_profiles`	No	Seed creators identified by platform and username.
`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.
`seed_creator_ids`	No	Seed creators identified by canonical Influship creator UUID.
`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
`ok`	No
`data`	No
`count`	No
`results`	No
`has_more`	No
`not_found`	No
`next_cursor`	No
`suggested_followups`	No

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"
+}

addedInput schema / required

Added value: +[
+  "context",
+  "llm_model"
+]

Changed2 schema fields changedOct 23, 2026
- removedOutput schema / properties / next_cursor / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / next_cursor / type
```
Added value: +[
+  "string",
+  "null"
+]
```
First observedMay 28, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered; the description still adds real value by explaining the blending/ranking mechanism, that results are scored, and that a seed_not_found response means the seed is unavailable and should not be retried. It does not mention pagination behavior via cursor, which is the one behavioral gap.

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 usage, then alternatives, then examples — a logical order with no filler sentences. It is on the longer side and the examples restate guidance already given, but each section earns its place for a tool with 12 parameters and three near-siblings.

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?

An output schema exists and annotations cover safety, so the description's job is routing and seed semantics — both handled thoroughly, including the failure mode (seed_not_found) and the handle-resolution prerequisite. Nothing an agent needs to invoke this correctly is missing beyond an optional note on cursor-based pagination.

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 description coverage is 100%, so the baseline is 3; the description goes beyond it by explaining that seeds can be supplied via seed_creator_ids (canonical UUIDs) and/or seed_profiles (platform + username), that the two are blended, and that the flat follower/engagement-rate/verified fields constrain results. It does not cover the context, llm_model, or conversation_id parameters, but those are densely self-documented in the schema.

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 ("Find creators SIMILAR to one or more seed creators") and immediately clarifies the mechanism: blended creator-profile, visual-style, and fact embeddings yielding a ranked list. This is plainly distinguishable from sibling tools like semantic_search_creators and match_creators without opening a schema.

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?

Gives explicit when-to-use criteria ("user already knows a creator they like and wants more like them"), names two alternatives with the conditions that select them (semantic_search_creators for topic-only queries, match_creators for scoring specific candidates), and points to autocomplete_creators for handle resolution. The two worked examples, including one negative example, remove ambiguity.

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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