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521,490 tools. Updated 2026-09-06 11:42

"Understanding Perplexity" matching MCP tools:

  • Fetch a public HTTPS URL and return a prose summary with key points. Lean mode — no bundle stored. Use when you need a condensed understanding of a web page. For raw text, use url.extract. For asking a specific question about a page, use url.qa. Returns: { url, summary, key_points: string[], truncated: boolean, word_count } Example prompts: - "Summarize https://en.wikipedia.org/wiki/Artificial_intelligence for me." - "Give me the key points from this blog post: [URL]." - "What is this article about? Summarize [URL]."
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  • Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.
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  • Calculate IPv4 subnet details from CIDR notation. Parses a CIDR block (e.g. 192.168.1.0/24) and returns the network address, broadcast address, subnet mask, wildcard mask, first and last usable host addresses, total and usable host counts, prefix length, and classful IP class (A/B/C/D/E). Essential for homelab network planning, VLAN segmentation, firewall rule design, and understanding address space allocation. Handles special cases for /31 point-to-point links (RFC 3021) and /32 host routes.
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  • Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.
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  • List Categories List all agent categories with counts. Returns every category in the directory along with the number of agents in each. Useful for building category filters or understanding the directory's coverage areas. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json [ { "category": "Category", "count": 1 } ] ```
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  • Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.
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Matching MCP Servers

  • A
    license
    A
    quality
    C
    maintenance
    MCP server for Perplexity AI Pro that enables deep web search, thread management, and export of answers using an existing Perplexity Pro subscription and browser session cookie.
    5
    30
    MIT

Matching MCP Connectors

  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.
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  • Given a concept or colour name, traces its documented appearances across cultures and centuries in chronological order. Returns a dated sequence of archive entries showing when and where the colour appeared, with primary sources. Use for historical research, provenance chains, and understanding why a colour carries the cultural weight it does. Example: 'indigo' traces from ancient Indian trade routes through Roman imports to Tudor sumptuary law to synthetic aniline displacement in 1897.
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  • Check subscription status, plan details, billing cycle, and feature access. Useful for understanding what the business can and cannot do on their current plan.
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  • Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    ConnectorNo auth
  • Fetch a public HTTPS URL and return a prose summary with key points. Lean mode — no bundle stored. Use when you need a condensed understanding of a web page. For raw text, use url.extract. For asking a specific question about a page, use url.qa. Returns: { url, summary, key_points: string[], truncated: boolean, word_count } Example prompts: - "Summarize https://en.wikipedia.org/wiki/Artificial_intelligence for me." - "Give me the key points from this blog post: [URL]." - "What is this article about? Summarize [URL]."
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  • Check the homepage robots.txt rule for eight named OpenAI, Anthropic, Perplexity, and Google AI crawler tokens. The free result stays complete and includes a bounded optional handoff to exact monitoring terms. Results describe robots policy only and do not prove network access, authentic bot identity, crawling, indexing, citation, or ranking.
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  • Step-by-step instructions for connecting an AI client to Pipeworx — Claude Code, claude.ai, Cursor, Windsurf, Gemini CLI, Perplexity, ChatGPT, or any MCP-capable client. Returns the gateway URL, plugin links, and first-question suggestions. Example: pipeworx_getting_started({ client: "cursor" })
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  • Check the current operational status of AI services tracked by Prismix. Returns status indicator (operational/degraded/outage), active incident count, and 30-day uptime. Covers 75+ services: OpenAI, Anthropic, Cursor, Mistral, Perplexity, Google AI, and more. Pass a service name/id to filter, or omit to see all.
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  • Browse all funding categories with opportunity counts. Categories include: Grant, Construction, Goods & Services, Professional Services, Technology, Healthcare, Research, and more. Useful for understanding what types of opportunities are available. Does not count toward your monthly searches.
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  • How AI assistants (ChatGPT, Perplexity-class) cite a brand: platforms, mention counts and the entities it gets associated with. Use when the user asks "does AI recommend us/them?". Costs credits; cached results are free.
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