Research Agent by Nova (CIVAI)
I do everything related to research and reports
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (5)
- LOWTool 'google_search__converse' description lacks action verbin google_search__converse
- LOWTool 'deep_research__conduct-deep-research' doesn't follow camelCase/snake_casein deep_research__conduct-deep-research
- LOWTool 'deep_research__conduct-deep-research' name length outside 3-30 rangein deep_research__conduct-deep-research
- LOWTool 'deep_research__generate-research-report' doesn't follow camelCase/snake_casein deep_research__generate-research-report
- LOWTool 'deep_research__generate-research-report' name length outside 3-30 rangein deep_research__generate-research-report
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": {
"research-agent": {
"url": "https://nova.civai.co/mcp/agents/research-agent"
}
}
}Remote-Endpunkte
https://nova.civai.co/mcp/agents/research-agentstreamable-httpWas es kann
Tool-Inventar
Tools (4)
🟢deep_research__conduct-deep-research(topic, objective, breadth, depth, max_total_queries, ...)
Conduct deep, iterative research on a topic by generating multiple search queries, processing the results, and recursively exploring new research directions.
Eingabe-Schema
{
"type": "object",
"properties": {
"topic": {
"type": "string",
"description": "The main topic or question to research"
},
"objective": {
"type": "string",
"description": "The specific goal or objective of the research"
},
"breadth": {
"type": "integer",
"description": "Number of search queries per research direction (1-5)",
"default": 3,
"minimum": 1,
"maximum": 5
},
"depth": {
"type": "integer",
"description": "Depth of recursive exploration (0-2)",
"default": 1,
"minimum": 0,
"maximum": 2
},
"max_total_queries": {
"type": "integer",
"description": "Maximum number of search queries to process (2-5)",
"default": 2,
"minimum": 2,
"maximum": 5
},
"max_duration_seconds": {
"type": "integer",
"description": "Maximum duration for the research process in seconds (60-300)",
"default": 120,
"minimum": 60,
"maximum": 300
},
"connection_id": {
"type": "string",
"description": "Optional connection ID for the user",
"default": ""
}
},
"required": [
"topic",
"objective"
]
}⚪deep_research__generate-research-report(research_data, objective)
Generate a comprehensive research report from existing research data.
Eingabe-Schema
{
"type": "object",
"properties": {
"research_data": {
"type": "string",
"description": "The research data and findings to include in the report"
},
"objective": {
"type": "string",
"description": "The specific goal or objective of the report"
}
},
"required": [
"research_data",
"objective"
]
}🟢google_search__search(query)
Run live Google search and summarize results.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"type": "string"
}
},
"required": [
"query"
]
}🟢google_search__converse(reply_hint)
Reply conversationally when the request is ambiguous or needs clarification.
Eingabe-Schema
{
"type": "object",
"properties": {
"reply_hint": {
"type": "string",
"description": "Optional hint for the conversational reply."
}
}
}Empfohlene Prompts
deep_research__conduct-deep-researchdeep_research__conduct-deep-researchCommunity
Nachweis