compare-audience-influencers
Compare influencer affinities and uniqueness between a selected audience and a baseline audience to identify distinct alignment patterns.
Instructions
Compares the influencers of an audience with a baseline audience. The baseline is determined as follows: If the selection was the full audience and a single country represents more than 50% of the audience, that country is used as the baseline. Otherwise, the Global baseline is applied. If the selection was a specific segment, the full audience is used as the baseline. Each influencer comparison includes: - Affinity (%) - The level of alignment between the influencer and the audience. Baseline Affinity (%) - The influencer’s affinity within the baseline audience. Uniqueness Score - A measure of how distinct the influencer is within the selected audience compared to the baseline.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| audience_influencers_id | Yes | The ID of the audience influencers. | |
| baseline_audience_influencers_id | Yes | The ID of the baseline audience influencers. | |
| cursor | No | Cursor for pagination. | |
| count | No | Number of items per page (default: 200). | |
| bio_keyword | No | Keyword to filter influencers by their biography. | |
| entity_type | No | Filter by entity type (person or brand). | |
| followers_min | No | Minimum number of followers. | |
| followers_max | No | Maximum number of followers. | |
| categories | No | Filter influencers by categories. | |
| countries | No | Filter influencers by country ISO codes. |
Implementation Reference
- src/audienseClient.ts:189-234 (handler)Core handler function that constructs the Audiense API endpoint for comparing audience influencers, fetches the data, and enriches the top 100 influencers with Twitter/X user details.export async function compareAudienceInfluencers( audience_influencers_id: string, baseline_audience_influencers_id: string, cursor?: number, count?: number, bio_keyword?: string, entity_type?: "person" | "brand", followers_min?: number, followers_max?: number, categories?: string[], countries?: string[] ): Promise<{ cursor: { next: number; prev: number }; influencers: any[] } | null> { const queryParams = new URLSearchParams(); if (cursor !== undefined) queryParams.append("cursor", cursor.toString()); if (count !== undefined) queryParams.append("count", count.toString()); if (bio_keyword) queryParams.append("bio_keyword", bio_keyword); if (entity_type) queryParams.append("entity_type", entity_type); if (followers_min !== undefined) queryParams.append("followers_min", followers_min.toString()); if (followers_max !== undefined) queryParams.append("followers_max", followers_max.toString()); if (categories && categories.length > 0) queryParams.append("categories", categories.join(",")); if (countries && countries.length > 0) queryParams.append("countries", countries.join(",")); const endpoint = `/audience_influencers/${audience_influencers_id}/compared_to/${baseline_audience_influencers_id}?${queryParams.toString()}`; const data = await makeAudienseRequest<{ cursor: { next: number; prev: number }; influencers: { id: string; affinity: number; baseline_affinity: number; uniqueness: number }[] }>(endpoint); if (!data || !data.influencers.length) { return data; } // Get the first 100 influencers for enrichment const influencerIds = data.influencers.slice(0, 100).map((influencer) => influencer.id); const twitterData = await fetchTwitterUserDetails(influencerIds); // Merge Twitter details into influencer data const enrichedInfluencers = data.influencers.map((influencer) => ({ ...influencer, twitter: twitterData[influencer.id] || null, // Add Twitter data if available })); return { cursor: data.cursor, influencers: enrichedInfluencers, }; }
- src/index.ts:194-257 (registration)MCP server.tool registration for the 'compare-audience-influencers' tool, including description, input schema (Zod validation), and thin wrapper handler that calls the core compareAudienceInfluencers function and formats the MCP response.server.tool( "compare-audience-influencers", `Compares the influencers of an audience with a baseline audience. The baseline is determined as follows: If the selection was the full audience and a single country represents more than 50% of the audience, that country is used as the baseline. Otherwise, the Global baseline is applied. If the selection was a specific segment, the full audience is used as the baseline. Each influencer comparison includes: - Affinity (%) - The level of alignment between the influencer and the audience. Baseline Affinity (%) - The influencer’s affinity within the baseline audience. Uniqueness Score - A measure of how distinct the influencer is within the selected audience compared to the baseline. `, { audience_influencers_id: z.string().describe("The ID of the audience influencers."), baseline_audience_influencers_id: z.string().describe("The ID of the baseline audience influencers."), cursor: z.number().optional().describe("Cursor for pagination."), count: z.number().optional().describe("Number of items per page (default: 200)."), bio_keyword: z.string().optional().describe("Keyword to filter influencers by their biography."), entity_type: z.enum(["person", "brand"]).optional().describe("Filter by entity type (person or brand)."), followers_min: z.number().optional().describe("Minimum number of followers."), followers_max: z.number().optional().describe("Maximum number of followers."), categories: z.array(z.string()).optional().describe("Filter influencers by categories."), countries: z.array(z.string()).optional().describe("Filter influencers by country ISO codes."), }, async ({ audience_influencers_id, baseline_audience_influencers_id, cursor, count, bio_keyword, entity_type, followers_min, followers_max, categories, countries }) => { const data = await compareAudienceInfluencers( audience_influencers_id, baseline_audience_influencers_id, cursor, count, bio_keyword, entity_type, followers_min, followers_max, categories, countries ); if (!data || !data.influencers.length) { return { content: [ { type: "text", text: `No influencers found for comparison between ${audience_influencers_id} and ${baseline_audience_influencers_id}.`, }, ], }; } const influencersText = data.influencers .map( (influencer) => `ID: ${influencer.id}\nAffinity: ${influencer.affinity}%\nBaseline Affinity: ${influencer.baseline_affinity}%\nUniqueness: ${influencer.uniqueness}%` ) .join("\n\n"); return { content: [ { type: "text", text: JSON.stringify(data, null, 2), }, ], }; } );
- src/index.ts:204-215 (schema)Zod schema for input parameters validation in the MCP tool registration.{ audience_influencers_id: z.string().describe("The ID of the audience influencers."), baseline_audience_influencers_id: z.string().describe("The ID of the baseline audience influencers."), cursor: z.number().optional().describe("Cursor for pagination."), count: z.number().optional().describe("Number of items per page (default: 200)."), bio_keyword: z.string().optional().describe("Keyword to filter influencers by their biography."), entity_type: z.enum(["person", "brand"]).optional().describe("Filter by entity type (person or brand)."), followers_min: z.number().optional().describe("Minimum number of followers."), followers_max: z.number().optional().describe("Maximum number of followers."), categories: z.array(z.string()).optional().describe("Filter influencers by categories."), countries: z.array(z.string()).optional().describe("Filter influencers by country ISO codes."), },