Agentfetch MCP vs BulkTranscripts YouTube

Agentfetch MCP tools 4

• fetch_urlFetch any URL and return clean, LLM-ready Markdown with token count, metadata, and 6h caching. WHEN TO USE: - You have a specific URL whose content you need. - You want to cap response size to stay inside your context window. - You want repeat fetches to be cheap (cache hits ≈ $0.0001). - The URL might be JS-rendered, a PDF, or behind a paywall — this tool auto-routes to the right fetcher (Trafilatura → Jina → FireCrawl → PDF). WHEN NOT TO USE: - You don't know which URL to fetch — use search_and_fetch instead. - You have many URLs to fetch — use fetch_multiple instead. Args: url: The URL to fetch. max_tokens: Hard cap on response size. Default unlimited. Pass this if you're tight on context budget — cheaper than over-fetching. format: "markdown" (default — recommended), "text", or "json". use_cache: True returns a cached copy if one exists (≤6h old). Pass False only when freshness matters (live news, prices). Returns: { "url": str, "success": bool, "markdown": str, "metadata": {title, author, published_date, domain, word_count, token_count, reading_time_seconds, content_type, language}, "cache": {hit, cached_at, expires_at}, "fetch_info": {fetcher_used, fetch_time_ms, cost_credits}, "error": str | None }
• estimate_tokensEstimate token count of a URL's content WITHOUT fetching the body. WHEN TO USE: - You're considering fetching a URL but unsure if it fits your remaining context window. This call is ~10x cheaper than a full fetch. - You want to triage a list of candidate URLs before deciding which to actually retrieve. IMPORTANT: Many servers omit Content-Length on dynamic / chunked responses. When that happens, this tool returns confident=false and estimated_tokens=null. In that case, call fetch_url with a max_tokens cap instead of trusting the estimate. Args: url: The URL to estimate. Returns: { "url": str, "success": bool, "estimated_tokens": int | null, "byte_size": int | null, "content_type": str, "confident": bool, "note": str }
• fetch_multipleFetch up to 20 URLs concurrently. Each result is the same shape as fetch_url. WHEN TO USE: - You have a list of URLs (search results, links from a doc, sitemap) and want them retrieved in parallel rather than one at a time. Args: urls: 1–20 URLs. Larger batches: split into multiple calls. max_tokens_each: Per-result cap. Apply this to keep total response inside your context budget — total ≈ len(urls) * max_tokens_each. use_cache: True for cache-aware fetching (default). Returns: {"count": int, "results": [<fetch_url shape>, ...]}
• search_and_fetchWeb search + fetch top results in one call. WHEN TO USE: - You have a research question, not specific URLs. E.g. "what's the latest on X", "find docs for Y library", "recent news about Z". - You'd otherwise have to call a search tool, parse results, then call fetch — this collapses that into one round-trip. Args: query: Search query (2–500 chars). num_results: Top N to fetch (1–10, default 3). max_tokens_each: Per-result cap (default 2000). Returns: {"query": str, "count": int, "results": [<fetch_url shape>, ...]}

BulkTranscripts YouTube tools

No tool names declared yet. Check the project README from the listing page.