"Information about Scalable Vector Graphics (SVG)" matching MCP tools:

• mio_image_png_to_svg

  MiOffice — AI-Powered Workspace Studio

  PNG to SVG — Convert raster images to scalable vector graphics. Runs in the browser. Covered by signup welcome credits and by the Day Pass (24-hour unlimited on this workspace group). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

  Connector

• emem_state

  emem — Earth memory protocol

  Get one dense numeric fingerprint that summarises everything known about a place — ready to feed into similarity search, a classifier, or clustering. Two views: `encoder` returns a single AI-model embedding (128-D Tessera, 1024-D Clay, 1024-D Prithvi); `cube` returns the full 1792-D vector concatenated across every band, with a per-band coverage manifest. When to use: Call this when the user wants a machine-usable summary of a place rather than individual band readings — e.g. 'give me a feature vector for this location', 'how do I represent this place for ML', or before running similarity / linear-probe / clustering downstream. Also use it to get one rebindable handle (`memory_token` / `state_cid`) that cites the whole place. Default `view=encoder` is the cheap single-recall path; pass `view=cube` for the full attested view (its `coverage[]` lets you tell signed-zero from not-yet-materialised). Then hand the vector to `emem_find_similar` (k-NN), `emem_compare` (two-place cosine), or `emem_verify_receipt` (audit the signature).

  Connector

• get_icon

  Supericons

  Retrieve one exact SVG icon when the icon ID and library are already known. Use search_icons first if the user only described a concept. Returns SVG code and public semantic guidance for the exact icon.

  Connector

• data.capabilities

  Recursive Support Oracle

  Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.

  Connector

• get_icon

  Supericons

  Retrieve one exact SVG icon when the icon ID and library are already known. Use search_icons first if the user only described a concept. Returns SVG code and public semantic guidance for the exact icon.

  Connector

• emem_coverage_map

  emem — Earth memory protocol

  Live SVG render of the responder's corpus density, returned as a proper MCP EmbeddedResource content block (image/svg+xml) — multimodal MCP agents can render it natively. When to use: Call when the user asks 'where do you have data?', 'show me the coverage', or wants a visual brief of the responder's corpus footprint. Returns a 1440×720 Plate-Carrée SVG (1° × 1° bins, log-scale colour, continent envelopes for orientation) plus a structuredContent summary (cell_count, total_facts, responder pubkey, REST URL). Multi-content-block reply: an EmbeddedResource (mimeType `image/svg+xml`, with text + uri) followed by a one-line text summary so text-only clients still see the cell / fact counts. For the bare image bytes, fetch `/v1/coverage_map.svg` over plain REST.

  Connector

• get_broker

  mcp-server

  Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.

  Connector

• get_business

  Venturu

  Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.

  Connector

• search_icons

  iconify

  Search for icons by keyword across all collections. Returns icon names in prefix:name format (e.g., "mdi:home"). Use get_icons to fetch SVG data for results.

  Connector

• generate_avatar

  diceuear

  Generate a custom avatar SVG. Specify a style (e.g., 'avataaars', 'pixel-art', 'lorelei') and seed (e.g., username). Returns the SVG URL ready to display.

  Connector

• get_icon_svg

  IconVaultKit

  Get the SVG markup for a specific icon. Use the collection and name from a search_icons result (e.g. collection='mdi', name='home').

  Connector

• get_commitment_basic_info

  mcp

  Get basic information about a Compute Engine Commitment, including its name, ID, status, plan, type, resources, and creation, start and end timestamps. Requires project, region, and commitment name as input.

  Connector

• get_cvss_details

  ContrastAPI

  Parse a CVSS v3.x vector string into a per-metric breakdown plus a recomputed base score. Returns the canonicalized vector, version (3.0 or 3.1), base_score, base_severity (NONE/LOW/MEDIUM/HIGH/CRITICAL), and the eight base metrics: attack_vector (NETWORK/ADJACENT_NETWORK/LOCAL/PHYSICAL), attack_complexity (LOW/HIGH), privileges_required (NONE/LOW/HIGH), user_interaction (NONE/REQUIRED), scope (UNCHANGED/CHANGED), and the three impact metrics confidentiality_impact / integrity_impact / availability_impact (NONE/LOW/HIGH each). When temporal/environmental metrics are explicit in the vector, temporal_score and environmental_score are populated separately. Use to translate raw CVSS strings into agent-friendly attributes without re-parsing the vector grammar yourself, and to verify upstream NVD scoring against the recomputed value. v2 vectors (AV:N/AC:L/Au:N/...) are rejected with 400 — read cvss_v2_vector from cve_lookup if you need v2 detail. Free: 30/hr, Pro: 500/hr. Returns {version, vector, base_score, base_severity, metrics: {attack_vector, attack_complexity, privileges_required, user_interaction, scope, confidentiality_impact, integrity_impact, availability_impact}, temporal_score, environmental_score, summary, verdict}.

  Connector

• chunk_text

  Toolora MCP Server

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

  Connector

• search_icons

  Supericons

  Search 20,000+ curated SVG icons across 10 libraries by meaning, label, visual description, tags, and synonyms. Use this when the user describes an icon concept such as "database", "user profile", "chill", "security", or "AI model". Returns matching icons with SVG code and public semantic guidance.

  Connector

• pentest_guide

  pentest-mcp-server

  Return a structured attack methodology playbook for the given attack vector and optional target context, for use in authorized penetration testing, CTF, or security research. Covers reconnaissance, enumeration, exploitation, and post-exploitation phases for the vector, filtered to what is relevant given the provided stack and WAF profile. Each phase includes: what to look for, tools to use, common mistakes, detection indicators that would alert defenders, and recommended mitigations. Next-tool suggestions are pre-filled with payload generator and technique lookup calls. Covers 15 vectors via the vector enum. Authorized testing only.

  Connector

• get_icons

  iconify

  Get SVG code and dimensions for specific icons. Input icon names in prefix:name format (e.g., "mdi:home", "fa:star"). Returns SVG markup, width, and height.

  Connector

• get_trip_details

  sbb-mcp

  Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.

  Connector

• chunk_text

  Toolora

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

  Connector

• ask_alti

  mcp

  Ask Alti, Christian Perez's AI agent, a single question about Christian — his work at Altivum, The Vector Podcast, his book 'Beyond the Assessment', his military service as a Green Beret, or his AWS / Applied AI engineering practice. Returns a concise 2-4 sentence reply grounded in Christian's published writing and autobiography. Does NOT answer general knowledge questions.

  Connector