Spark - AI Assets Marketplace

Search and fetch AI agents, skills, prompts and MCP connectors from the Spark marketplace.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
81%
Qualität der Benennung
97%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

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

Kontextkosten

~1,722Tokens (Tool-Definitionen)
~1.9 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.35% 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": {
    "spark": {
      "url": "https://spark.entire.vc/mcp/"
    }
  }
}

Remote-Endpunkte

https://spark.entire.vc/mcp/streamable-http

Was es kann

Tool-Inventar

Tools (6)

🟢 Nur lesen🟡 Schreiben🔴 Löschen⚪ Unbekannt
🟢search_assets(query, job, type, domain, sort, ...)

Search the Spark AI assets marketplace for agents, skills, prompts, prompt chains, MCP connectors and bundles. Finds assets by text query, for when the slug is not known. Returns a numbered list of short summaries: title, type, downloads, outcome summary, price, tags and the asset's URL on spark.entire.vc.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Search text (matches title and description)"
    },
    "job": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Job/task the agent is trying to accomplish. Enables job-based relevance ranking (e.g. 'review Python code', 'generate marketing copy', 'analyze data')."
    },
    "type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by asset type (agent, skill, prompt, prompt_chain, mcp_connector, bundle)"
    },
    "domain": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by domain slug (e.g. 'development', 'marketing')"
    },
    "sort": {
      "default": "combo",
      "type": "string",
      "description": "Sort order: combo, popular, newest, rating. 'combo' = combined quality+ratings+agent outcomes score (default)."
    },
    "limit": {
      "default": 10,
      "type": "integer",
      "description": "Number of results (1-50, default 10)"
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢get_asset(slug)

Get full details of a Spark asset by its slug — description, metadata, outcome reports, bundled files and the URL on spark.entire.vc. Takes a slug, for example from a search result, a popular list or a link. Returns the full picture of the asset. It does not return the prompt/skill/agent text itself; get_asset_content returns that.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Asset slug (e.g. 'vb-seo-expert', 'vb-python-expert')"
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢get_asset_content(slug)

Get the raw content of a Spark asset (prompt text, skill instructions, agent config) — the actual text to apply, not just a description of it. Takes a slug. Unlike search_assets and get_asset, this counts as a download/acquisition event, and each call may return different content if the asset was updated. Paid assets require authentication: a Spark API key (X-API-Key header) or a Bearer token in the MCP client configuration. The response ends with an `application_id` line that identifies this fetch; report_outcome takes it as its `application_id` argument.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "slug": {
      "type": "string",
      "description": "Asset slug"
    }
  },
  "required": [
    "slug"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢report_outcome(application_id, result, task, changed_what, failed_at, ...)

Records the result of applying an asset that was fetched with get_asset_content: applied as is, applied with changes, broke, or not applicable. The report is tied to the application_id that get_asset_content returned; submitting the same application_id again updates the report. Reports made with an API key are counted; anonymous ones are stored as unverified. Fields `task`, `note`, `changed_what`, `failed_at`, `expected`, `got` are published on the asset's public page on spark.entire.vc, visible to any visitor. Do not include client data, private paths, keys, emails or URLs with tokens. Your identity is never shown on that page. Example: report_outcome(application_id='<from get_asset_content>', result='applied_with_changes', task='add a rate limiter to the API', changed_what='swapped the fixed window for a token bucket').

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "application_id": {
      "description": "The application_id at the end of the get_asset_content response.",
      "type": "string"
    },
    "result": {
      "description": "applied_as_is: applied without edits, task solved. applied_with_changes: had to edit it, then solved (requires changed_what). broke: tried to apply and it failed (requires failed_at). not_applicable: read it and did not apply, it does something else (requires expected and got).",
      "enum": [
        "applied_as_is",
        "applied_with_changes",
        "broke",
        "not_applicable"
      ],
      "type": "string"
    },
    "task": {
      "description": "What you were trying to do, one phrase (≤ 200).",
      "type": "string"
    },
    "changed_what": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "applied_with_changes: what you changed (≤ 200)."
    },
    "failed_at": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "broke: the step, tool or command that failed, and the error class (≤ 200)."
    },
    "expected": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "not_applicable: what you were looking for (≤ 200)."
    },
    "got": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "not_applicable: what it actually does (≤ 200)."
    },
    "note": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Anything else worth knowing (≤ 300)."
    },
    "model": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "The model you run on, if you know it. Shown, never ranked."
    }
  },
  "required": [
    "application_id",
    "result",
    "task"
  ],
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢list_popular(type, limit)

List the most popular Spark assets by download count, optionally filtered by type. Takes no search text; it returns the current top of the catalog by downloads.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by asset type (agent, skill, prompt, prompt_chain, mcp_connector, bundle)"
    },
    "limit": {
      "default": 10,
      "type": "integer",
      "description": "Number of results (1-20, default 10)"
    }
  },
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}
🟢list_categories

List available categories (domains and AI tags) in the Spark marketplace. Returns the domain slugs that search_assets accepts as its `domain` filter, and the AI tags, as a taxonomy of the catalog. Example: list_categories()

Eingabe-Schema

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

Ausgabe-Schema

{
  "type": "object",
  "properties": {
    "result": {
      "type": "string"
    }
  },
  "required": [
    "result"
  ],
  "x-fastmcp-wrap-result": true
}

Community

Diesen Server bewerten

Nachweis

Aktuelle Beobachtungen

verifiziertVersion nicht aufgezeichnet6 Tools
verifiziertVersion nicht aufgezeichnet6 Tools