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Glama

Influship Influencer Marketing MCP

match_creators

Read-only

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
countNo
resultsNo
has_moreNo
not_foundNo
next_cursorNo
suggested_followupsNo

TDQS

A4.5/5.0
Behavior4/5

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

The annotations already declare readOnlyHint and destructiveHint, and the description adds useful behavioral context by disclosing the return format: 'a match score (0-1), a good/neutral/avoid decision, and structured reasons.' It also explains how candidates are passed (creator_ids and/or profiles), which goes beyond the schema. It does not mention edge cases like missing candidates, but the annotation coverage lowers the burden.

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 front-loaded with the core purpose, then provides usage guidance, alternatives, and examples in a structured order. Every sentence serves a purpose, and the inline examples are compact yet illustrative. It is appropriately sized for a tool with this complexity and does not waste words.

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?

The description covers when to use the tool, how to invoke it (candidate formats), what it returns, and how it relates to sibling tools. The presence of an output schema further reduces the need to explain return values. It is complete enough for an agent to correctly select and call the tool in a variety of scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides detailed descriptions for all parameters (100% coverage), so the baseline is 3. The description adds marginal value by clarifying that intent_query is 'the brief the LLM reasons against' and that intent_context can include target audience or brand values, but these are minor elaborations of the schema descriptions. No new syntax or format details are added beyond what the schema already documents.

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 opens with a specific verb+resource+context: 'Score how well specific creators fit a campaign brief or search intent.' It clearly distinguishes this tool from siblings by contrasting with semantic_search_creators, search_creators, and find_lookalike_creators, each with a different discovery or resolution 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?

The description explicitly states when to use this tool (when the user already has candidates) and when not to, naming three alternatives: semantic_search_creators for discovery, search_creators for resolving rough names, and find_lookalike_creators for similar creators. It also provides concrete examples that chain it with get_profile and get_posts.

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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TDQS

A3.8/5.0
Disambiguation2/5

Several tools have overlapping boundaries: autocomplete_creators and search_creators are described as near-equivalent fuzzy lookups, get_creator and get_profile both resolve exact platform+username input, and the Instagram/TikTok post helpers overlap with generic get_posts. The descriptions work hard to disambiguate, but an agent would frequently need to choose between two or three equally plausible tools.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern, such as get_youtube_video, search_creators, match_creators, and render_creator_profile. Minor inconsistencies exist: singular/plural variants (get_instagram_post vs get_instagram_posts), list_ vs get_ for video listing, and search_creators carrying legacy semantic behavior under a lookup-sounding name.

Tool Count2/5

Twenty-eight tools places the server in the 'too many' range, and the count is inflated by near-duplicates like autocomplete_creators/search_creators, get_creator/get_profile, and singular/batch transcript variants. Even with three social platforms and rendering helpers, the surface would be more focused around 18–22 tools.

Completeness4/5

The core influencer research workflow is well covered: handle resolution, batch lookup, semantic discovery, lookalikes, posts, transcripts, YouTube search, matching, and comparison rendering. Gaps are minor—there is no creator shortlist persistence or cross-platform comment support—but the main discovery-to-match path has no dead ends.

Resources