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Influship

Influship MCP

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find_lookalike_creators

Read-only

Find creators similar to one or more seed creators by blending profile, visual style, and fact embeddings. Returns a ranked list with scores.

Instructions

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.

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

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return.
cursorNoPagination cursor from a previous response.
verifiedNoWhen set, only return verified or unverified creators.
max_followersNoMaximum follower count.
min_followersNoMinimum follower count.
seed_profilesNoSeed creators identified by platform and username.
seed_creator_idsNoSeed creators identified by canonical Influship creator UUID.
max_engagement_rateNoMaximum engagement rate as a percentage from 0 to 100.
min_engagement_rateNoMinimum engagement rate as a percentage from 0 to 100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
countNo
resultsNo
has_moreNo
not_foundNo
next_cursorNo
suggested_followupsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already show `readOnlyHint: true` and `destructiveHint: false`, so the description doesn't need to reiterate safety. It adds value by explaining the blending mechanism (creator-profile + visual-style + fact embeddings) and the returning of a ranked list with scores. However, it could briefly mention pagination or response format.

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 well-structured: a clear opening sentence, a usage section, parameter details, and examples. Every sentence adds value, and there is no redundancy or fluff. It is appropriately sized 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 has 9 parameters, an output schema, and a need to distinguish from 13 siblings, the description covers all key aspects: purpose, usage context, seeds, filtering, examples, and limits. It is complete and leaves no major gaps for an AI agent to misunderstand.

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. The description adds context by explaining the role of `seed_profiles` and `seed_creator_ids`, mentioning the default and max for `limit`, and noting that filter fields (follower count, engagement rate, verified) can constrain results. This helps the agent understand which parameters are central.

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 clearly states the tool's purpose: finding creators similar to seed creators. It uses specific verbs like 'Find creators SIMILAR' and distinguishes from siblings by name-dropping alternative tools. Examples reinforce the purpose.

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 guidance on when to use this tool (user has seed creators) versus alternatives (`semantic_search_creators` for topics, `match_creators` for specific candidates). Also includes concrete examples of when to use and when not to, making it easy for the agent to decide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.