MCP MeasureMCP Measure

Index beobachtet

Weav Customer Service

Explore Weav customer service pricing, features, comparisons, signup, and demos. No auth required.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
90%
Vollständigkeit des Schemas
100%
Qualität der Benennung
100%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (2)

  • LOWTool 'get_product_overview' description lacks action verbin get_product_overview
  • LOWTool 'get_comparison' description lacks action verbin get_comparison

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,509Tokens (Tool-Definitionen)
~2.3 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.18% von 128k Kontext)

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": {
    "weav-customer-service": {
      "url": "https://weav.com/mcp"
    }
  }
}

Remote-Endpunkte

https://weav.com/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (6)

🟢 Nur lesen🟡 Schreiben🔴 Löschen⚪ Unbekannt
🟢get_pricing(context, llm_model, conversation_id)

Return Weav public pricing: Lite, Plus, Pro, Max monthly prices, annual discount, add-ons, and signup/sales links. Use this instead of scraping weav.com/pricing.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_product_overview(context, llm_model, conversation_id)

Return a structured overview of Weav: AI agents, unified inbox, channels, training, actions, and escalation. Links to product and docs.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_signup(context, llm_model, conversation_id)

Return the self-serve signup URL (app.weav.com/register) and what the free Lite start includes.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_demo(context, llm_model, conversation_id)

Return the sales/demo booking URL (weav.com/sales). Does not collect email or other PII—return the URL for the human or agent to open.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_comparison(context, llm_model, conversation_id)

Return a short structured contrast of Weav vs Ada, Intercom, and Zendesk, plus the comparison page URL.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_more_tools(context, llm_model, conversation_id)

Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "A description of your goal and what kind of tool would help accomplish it."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ]
}

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Aktuelle Beobachtungen

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