Imagine AI Benchmark
LinkedIn content benchmark for 900+ B2B companies and their teams, with an open scoring method.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"benchmark": {
"url": "https://benchmark.useimagine.ai/api/mcp"
}
}
}Remote-Endpunkte
https://benchmark.useimagine.ai/api/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (5)
🟢list_companies(query, industry, trackingOnly, limit)
List companies in the benchmark catalog (customers, competitors, industry peers). Filter by name substring, industry, or tracking. Returns ids you pass to every other tool.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Case-insensitive name substring, e.g. \"rho\"."
},
"industry": {
"type": "string",
"description": "Exact industry label, e.g. \"Sales & Marketing Tech\", \"AI Infrastructure\", \"Fintech\". Omit to see all industries."
},
"trackingOnly": {
"type": "boolean",
"description": "Only companies in the scheduled scrapes (tracking_enabled). Default false."
},
"limit": {
"type": "integer",
"description": "Max rows, default 200, max 500.",
"minimum": 1,
"maximum": 500
}
},
"required": [],
"additionalProperties": false
}🟢company_fit_summary(companyId, company, workspaceId, dateFrom, dateTo)
A company's page fit plus the distribution of its people's geometric means (median, baseline band), top people and who is under the 18-post floor. Company score = median of its 5 most active fitted people (by original posts in the last 90 days) plus the company page; needs 2+ fitted accounts. people.counted / countedPersonIds say who is in. Use for 'how is X doing on LinkedIn'.
Eingabe-Schema
{
"type": "object",
"properties": {
"companyId": {
"type": "string",
"description": "Company UUID. Workspace keys: omit = your own company."
},
"company": {
"type": "string",
"description": "Company name or dashboard slug, if no id."
},
"workspaceId": {
"type": "string",
"description": "Internal callers only: default to this workspace's own company."
},
"dateFrom": {
"type": "string",
"description": "Inclusive start date YYYY-MM-DD (post_date)."
},
"dateTo": {
"type": "string",
"description": "Inclusive end date YYYY-MM-DD (post_date)."
}
},
"required": [],
"additionalProperties": false
}🟢industry_landscape(industry, companyIds, workspaceId, dateFrom, dateTo, ...)
Every account in an industry (or a custom company set): per-company rollup (page fit + people median over the 5 most active fitted people (peopleCounted), preliminary = "Few posts" under 40 team posts), population vs the research baseline, and category lift with the author effect removed (which post types beat each author's own typical post). Public callers get bands and pooled lifts, the same as Discover. Use for "what works in fintech" or "where does X sit in its market".
Eingabe-Schema
{
"type": "object",
"properties": {
"industry": {
"type": "string",
"description": "Industry label, e.g. \"Sales & Marketing Tech\". Omit with companyIds for a custom set; omit both = the live dashboard set for workspace keys, the whole catalog for operators."
},
"companyIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Custom company set (e.g. a customer + its competitors). Workspace keys: omit (with no industry) = own company + current competitors from the dashboard."
},
"workspaceId": {
"type": "string",
"description": "Internal callers only: default the set from this workspace."
},
"dateFrom": {
"type": "string",
"description": "Inclusive start date YYYY-MM-DD (post_date)."
},
"dateTo": {
"type": "string",
"description": "Inclusive end date YYYY-MM-DD (post_date)."
},
"limit": {
"type": "integer",
"description": "Max accounts listed, default 40, max 300.",
"minimum": 1,
"maximum": 300
}
},
"required": [],
"additionalProperties": false
}🟢methodology
Imagine AI research rules (log-normal engagement, geometric means, research baseline 49/77/130). Call once before interpreting any numbers.
Eingabe-Schema
{
"type": "object",
"properties": {},
"required": [],
"additionalProperties": false
}🟢category_breakdown(companyIds, personIds, workspaceId, source, dateFrom, ...)
Post counts and engagement per content category (product update, hiring, personal story, industry insight, ...) for companies or people.
Eingabe-Schema
{
"type": "object",
"properties": {
"companyIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Company UUIDs. Workspace keys: omit = the live dashboard set (own company + current competitors, see the workspace tool)."
},
"personIds": {
"type": "array",
"items": {
"type": "string"
},
"description": "Person UUIDs (narrows to these people)."
},
"workspaceId": {
"type": "string",
"description": "Internal callers only: default the set from this workspace."
},
"source": {
"type": "string",
"enum": [
"company",
"people",
"all"
],
"description": "'company' = the company page only, 'people' = its tracked people only, 'all' = both. Default varies per tool."
},
"dateFrom": {
"type": "string",
"description": "Inclusive start date YYYY-MM-DD (post_date)."
},
"dateTo": {
"type": "string",
"description": "Inclusive end date YYYY-MM-DD (post_date)."
},
"includeReposts": {
"type": "boolean",
"description": "Include reposts. Default false."
}
},
"required": [],
"additionalProperties": false
}Community
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