Sofya

Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
77%
Qualität der Benennung
90%
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

~1,379Tokens (Tool-Definitionen)
~1.0 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.08% 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": {
    "sofya": {
      "url": "https://sofya.co/mcp"
    }
  }
}

Remote-Endpunkte

https://sofya.co/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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🟡search(query, search_depth, max_results, include_answer, include_domains, ...)

Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 10 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 12 credits, much cheaper than a full 50-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query, relaxed_query (set when the query matched nothing and was retried once with its site: operator, else its quotes, removed - the results answer that looser query). Args: query: The search query search_depth: "basic" (default) for extracted page content (2 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 10 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "title": "Query"
    },
    "search_depth": {
      "type": "string",
      "default": "basic",
      "title": "Search Depth"
    },
    "max_results": {
      "type": "integer",
      "default": 10,
      "title": "Max Results"
    },
    "include_answer": {
      "type": "boolean",
      "default": false,
      "title": "Include Answer"
    },
    "include_domains": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Include Domains"
    },
    "exclude_domains": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Exclude Domains"
    },
    "topic": {
      "type": "string",
      "default": "general",
      "title": "Topic"
    },
    "freshness": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Freshness"
    }
  },
  "required": [
    "query"
  ]
}
🟡fetch(urls, include_raw_html)

Fetch one or more URLs and return their content as clean markdown. Use this to read articles, documentation, blog posts, or any page where you need the complete text, not just a snippet from search. Also supports PDF, DOCX, and other document formats. Costs 2 credits per URL. Max 10 URLs per request. Failed URLs are not charged. Set include_raw_html=true to also get the raw HTML source in each result. Useful for inspecting embedded URLs, data attributes, iframes, or script tags that are stripped during markdown conversion. Returns null for non-HTML content (PDF, DOCX, etc.). Same cost. Returns: results (array of {title, url, content, raw_html, published_time, success, error}), credits_used, credits_remaining. Args: urls: List of URLs to fetch (max 10) include_raw_html: Include raw HTML source in each result (default false)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "urls": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 10,
      "title": "Urls"
    },
    "include_raw_html": {
      "type": "boolean",
      "title": "Include Raw Html",
      "default": false
    }
  },
  "required": [
    "urls"
  ]
}
🟢extract(url, prompt)

Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 10 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "url": {
      "type": "string",
      "title": "Url"
    },
    "prompt": {
      "type": "string",
      "title": "Prompt"
    }
  },
  "required": [
    "url",
    "prompt"
  ]
}
🟢research(query, topic, freshness, max_sources)

Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 50 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "title": "Query"
    },
    "topic": {
      "type": "string",
      "default": "general",
      "title": "Topic"
    },
    "freshness": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "title": "Freshness"
    },
    "max_sources": {
      "type": "integer",
      "default": 20,
      "title": "Max Sources"
    }
  },
  "required": [
    "query"
  ]
}

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