AAJ Marketing Engines
Deterministic marketing math for AI agents: LTV:CAC, pipeline coverage, A/B tests, pricing.
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": {
"aaj-engines": {
"url": "https://aaj-engines-mcp.sarojkjha10.workers.dev/mcp"
}
}
}Remote-Endpunkte
https://aaj-engines-mcp.sarojkjha10.workers.dev/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (5)
🟢list_engines(category)
Lists every AAJ marketing engine: its id, the question it answers, and its category. Engines cover unit economics, pipeline forecast, budget allocation, retention, A/B significance, SEO/GEO scoring, ad spend and pricing.
Eingabe-Schema
{
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional filter. Words are matched against the engine's category, id and skill title (for example \"pricing\", \"retention\" or \"seo audit\"), then against its summary."
}
},
"additionalProperties": false
}🟢describe_engine(engine)
Shows one engine's input schema: required fields, units, alternative shapes, and a worked example. Engines reject bad input rather than guessing.
Eingabe-Schema
{
"type": "object",
"properties": {
"engine": {
"type": "string",
"description": "Engine id."
}
},
"required": [
"engine"
],
"additionalProperties": false
}🟢run_engine(engine, input)
Runs an AAJ engine and returns its verdict. Give `input` as an object of the engine's fields. Omit `input` to run the engine's built-in worked example. Engines are deterministic, offline, and validate their input — a wrong unit is reported as an error, not absorbed into a confident answer.
Eingabe-Schema
{
"type": "object",
"properties": {
"engine": {
"type": "string",
"description": "Engine id."
},
"input": {
"type": [
"object",
"string"
],
"description": "The engine's configuration object. Some engines also accept a named mode as a plain string (for example 'design' or 'readout'). Omit for the demo."
}
},
"required": [
"engine"
],
"additionalProperties": false
}🟢search_skills(query, limit)
Finds AAJ marketing skills by the problem they solve — 'churn is rising', 'we're launching next month', 'not showing up in AI answers'. Returns matching methods and marks which have a runnable engine.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The problem, in plain words."
},
"limit": {
"type": "integer",
"description": "Max results, default 8.",
"minimum": 1,
"maximum": 73
}
},
"required": [
"query"
],
"additionalProperties": false
}🟢get_skill(skill)
Returns the full method for one skill: when to use it, the steps, the quality bar, and how to run its engine if it has one.
Eingabe-Schema
{
"type": "object",
"properties": {
"skill": {
"type": "string",
"description": "Skill slug."
}
},
"required": [
"skill"
],
"additionalProperties": false
}Community
Nachweis