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Influship

Influship MCP

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by Influship

match_creators

Read-only

Evaluate specific creators against a campaign brief to get match scores, decisions, and reasons for fit or avoidance.

Instructions

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; call get_posts first if recent content context is needed.

  • User: "Rate these five creators for a vegan cookbook launch" -> use this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
creator_idsNoCandidate creators identified by canonical Influship creator UUID.
profilesNoCandidate creators identified by platform and username.
intent_queryYesCampaign brief or matching intent.
intent_contextNoOptional extra campaign context, audience, brand values, or prior collabs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
resultsNo
countNo
okNo
not_foundNo
has_moreNo
next_cursorNo
suggested_followupsNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about return values (match score, decision, structured reasons) and parameter usage. It does not contradict annotations and provides useful behavioral details beyond the annotations.

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 with a clear purpose, usage guidelines, parameter details, sibling tool comparisons, and examples. Every sentence is necessary and front-loaded with the core purpose. No fluff.

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 (4 parameters, output schema exists, annotations present, siblings distinguished), the description covers what it does, how to call it, what it returns, when to use vs alternatives, and even includes a workflow example. It is fully complete for an AI agent to understand and use the tool correctly.

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 value by explaining how to pass candidates (creator_ids and/or profiles), clarifying intent_query as 'the brief the LLM reasons against', and providing examples. This goes beyond the schema descriptions.

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 starts with a clear verb and resource ('Score how well specific creators fit a campaign brief or search intent'), and explicitly distinguishes itself from siblings like semantic_search_creators, search_creators, and find_lookalike_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 explicitly states when to use this tool (when candidates are already in mind) and when not to use it (e.g., use semantic_search_creators for discovery, search_creators for resolving handles, find_lookalike_creators for similar creators). It also includes examples and a workflow.

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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