Agent Radar

Search, inspect, recommend, and explain rated AI tools through Agent Radar.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
73%
Vollständigkeit des Schemas
98%
Qualität der Benennung
90%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (3)

  • LOWTool 'get_tool_card' description lacks action verbin get_tool_card
  • LOWTool 'recommend_tools' description lacks action verbin recommend_tools
  • LOWTool 'explain_rating' description lacks action verbin explain_rating

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

Kontextkosten

~1,133Tokens (Tool-Definitionen)
~3.1 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.89% 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": {
    "agent-radar": {
      "url": "https://agent-radar.zation1.workers.dev/api/mcp"
    }
  }
}

Remote-Endpunkte

https://agent-radar.zation1.workers.dev/api/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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🟢search_tools(query, top_k, filters)

Search Agent Radar Tool Cards by query text and optional type, tag, or risk filters.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "default": "",
      "description": "Free-text tool name, capability, or task to search for. Use an empty string to browse the highest-rated matching catalog entries.",
      "type": "string"
    },
    "top_k": {
      "default": 5,
      "description": "Maximum number of results to return, from 1 to 50. Defaults to 5.",
      "type": "integer",
      "minimum": 1,
      "maximum": 50
    },
    "filters": {
      "description": "Optional exact-match filters applied before search ranking.",
      "type": "object",
      "properties": {
        "type": {
          "description": "Exact Agent Radar tool type to include, such as mcp, skill, agent, framework, or cli.",
          "type": "string",
          "enum": [
            "mcp",
            "skill",
            "agent",
            "framework",
            "cli",
            "prompt",
            "rules",
            "dataset",
            "service"
          ]
        },
        "tags": {
          "description": "Exact catalog tags that every returned tool must contain.",
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "risk_level": {
          "description": "Exact assessed risk level to include: low, medium, high, critical, or unknown.",
          "type": "string",
          "enum": [
            "low",
            "medium",
            "high",
            "critical",
            "unknown"
          ]
        }
      },
      "additionalProperties": false
    }
  },
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": {}
}
🟢get_tool_card(tool_id)

Return one Tool Card and its Rating Result by stable tool_id.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "tool_id": {
      "type": "string",
      "minLength": 1,
      "description": "Stable Agent Radar tool identifier, usually obtained from search_tools results."
    }
  },
  "required": [
    "tool_id"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": {}
}
🟢recommend_tools(task, language_or_stack, environment, preferred_tool_types, allowed_permissions, ...)

Recommend known tools for a task using request-scoped LLM credentials when required.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "task": {
      "type": "string",
      "minLength": 1,
      "description": "Natural-language development task for which Agent Radar should recommend suitable AI tools."
    },
    "language_or_stack": {
      "description": "Programming languages, frameworks, runtimes, or other stack constraints relevant to the task.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "environment": {
      "description": "Execution contexts such as local development, CI, browser, cloud, IDE, or production.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "preferred_tool_types": {
      "description": "Agent Radar tool types to prefer when selecting candidates.",
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "mcp",
          "skill",
          "agent",
          "framework",
          "cli",
          "prompt",
          "rules",
          "dataset",
          "service"
        ]
      }
    },
    "allowed_permissions": {
      "description": "Permission scope names the caller is willing to allow; candidates outside these boundaries are treated conservatively.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "risk_tolerance": {
      "description": "Maximum preferred risk tolerance for the recommendation: low, medium, or high.",
      "type": "string",
      "enum": [
        "low",
        "medium",
        "high"
      ]
    },
    "existing_tools": {
      "description": "Tools already available to the project or agent, used as compatibility and duplication context.",
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "budget": {
      "description": "Natural-language cost constraint, such as free, free_or_low_cost, or a project-specific budget.",
      "type": "string"
    },
    "output_format": {
      "description": "Preferred presentation format for recommendation content: json or markdown. MCP still returns structured content.",
      "type": "string",
      "enum": [
        "json",
        "markdown"
      ]
    },
    "top_k": {
      "description": "Maximum number of recommended candidates to return, from 1 to 50. Defaults to 5 when omitted.",
      "type": "integer",
      "minimum": 1,
      "maximum": 50
    },
    "model": {
      "description": "LLM provider model identifier recognized by Agent Radar. Omit it to use the server-configured or registry default model.",
      "type": "string",
      "minLength": 1
    }
  },
  "required": [
    "task"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": {}
}
🟢explain_rating(tool_id)

Return rating dimensions, explanations, penalties, and boosts for one tool_id.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "tool_id": {
      "type": "string",
      "minLength": 1,
      "description": "Stable Agent Radar tool identifier, usually obtained from search_tools results."
    }
  },
  "required": [
    "tool_id"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {},
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "additionalProperties": {}
}

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