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745,045 tools. Updated 2026-11-05 03:01

"Understanding Vector Search" matching MCP tools:

  • Search 161,000+ free Infyicon vector icons by keyword and return up to 30 matches per page, each with icon id (slug_number), name, style, page URL, direct SVG URL, 512px PNG URL and tags. Styles: outline (black line), fill (black solid), color-outline, color-fill. Use this first whenever the user describes what icon they want; then pass an id to get_icon_svg, get_icon_svg_bulk or get_icon_png. Use get_popular_icons instead when there is no search term, and search_uicons instead when the user wants a CSS webfont class rather than an SVG/PNG file. Read-only, no auth, no API key; empty query returns an error; total_matches + next_offset are returned for paging. Free with attribution to infyicon.com.
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  • Search 161,000+ free Infyicon vector icons by keyword and return up to 30 matches per page, each with icon id (slug_number), name, style, page URL, direct SVG URL, 512px PNG URL and tags. Styles: outline (black line), fill (black solid), color-outline, color-fill. Use this first whenever the user describes what icon they want; then pass an id to get_icon_svg, get_icon_svg_bulk or get_icon_png. Use get_popular_icons instead when there is no search term, and search_uicons instead when the user wants a CSS webfont class rather than an SVG/PNG file. Read-only, no auth, no API key; empty query returns an error; total_matches + next_offset are returned for paging. Free with attribution to infyicon.com.
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  • Semantic search over guthmann.estate — market insights, reports, neighborhood portraits, listing exposés, project pages and the company's own pages. Hybrid retrieval (vector + keyword, no reranking); one result per page with title, description, image, best-matching snippet and score. Use it for questions that need prose (analysis, context, advice); use the data tools for exact numbers and `listings` for what is currently for sale — the search index follows the website with up to six hours of delay. Parameters: - q: natural-language query, in the language of the pages you want (min 2 characters) - locale: "de" | "en" — language of the indexed pages (default: en) - section: comma-separated filter — "listings" (exposés), "projects" (new-build projects), "market-intelligence" (insights, reports, portraits), "pages" (company, services, guides); omit for all - limit: 1-20 pages (default: 10) Key response fields: - url, title, description, image, section, language - snippet (best-matching text passage), score (0-1)
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  • Get a security advisory (vulnerability) by its key. Returns a security advisory by key, for example a GHSA id taken from a version's advisoryKeys, including the title, CVE aliases, CVSS v3 score and vector, and a link to the full record on osv.dev. Use this only when you already have an advisory ID from get_package_version's advisoryKeys. There is no search here. To find out whether a version has vulnerabilities at all, call get_package_version first; this tool explains one advisory in depth.
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  • Search memories by keyword. The query must use vocabulary that appears in stored labels, descriptions, or tags — not intent summaries or paraphrases. Pass node_kind (space-separated) to list or search within specific kinds — unrelated kinds that happen to match query text are excluded. Omit query with node_kind set to list matching kinds ordered by most-recently-updated. Default limit: 10. Use exact=true for identifiers (ticket numbers, short codes with hyphens) — FTS tokenises hyphens away so 'PROJ-042' is not found by default. If search returns zero or truncated results, use orient (with domain) to browse all memories, then recall by ID, then follow connections from a known memory. Returns lean results only — id, label, and a truncated why_matters excerpt; call recall(id) for full content. When a list or section has 2 or more results, each is rendered as a single compact text line — "[id] label — excerpt (domain, node_kind)" — instead of a JSON object; exactly one result is returned as a full object. exact=true is exempt — it always returns full objects, regardless of result count. state (space-separated union) post-filters results to memories with any of the given lifecycle states: none | resolved | superseded | contested. Response always includes semantic_attempted (bool: true when embed() succeeded and the vector path was entered) and semantic_best_dist (float|null: cosine distance of the top result after filtering, null when no vector rows were returned). Use these to diagnose whether Bedrock was available and how close the best match was. On failure, content[0].text is JSON: {"error_class": "retryable|forbidden|internal", "message": "..."}. Switch on error_class: retry on retryable.
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  • Embed a text string into a 768-dim Gemini Flash vector. Use for HyDE-style retrieval: (1) write a hypothetical short paper that would perfectly answer the user's query, (2) embed it with task_type='RETRIEVAL_DOCUMENT' (default — matches the corpus embedding side), (3) pass the resulting embedding back through search-style tools to find real papers nearest to the hypothetical. task_type='RETRIEVAL_QUERY' matches the query side and is useful for direct user-query embedding without HyDE. Pro-only — requires an SF_API_KEY on a Pro account; anonymous and free callers get a 403 pro_required. Cost: ~$0.0001/call; rate-limited at 30/minute per API key.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    In-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.
    MIT

Matching MCP Connectors

  • Search or recall memories from memocore (the user's persistent memory, also called "mc") — their own private memos and every project they belong to. Every search is HYBRID: `query` (a natural-language description of what you seek) drives meaning-based vector recall, `keywords` (the distinctive terms) drives keyword matching against the heavily-weighted `meta` cues, then title, preview and content; the two rankings are fused. Both are required — write them as two views of the SAME need, not two different questions — except in a DATED listing. `date_from` (optionally `date_to`) keeps only the memos that have a date in that window: alone, with NO query/keywords, it lists them nearest first — e.g. what is coming up in the next 3 days; with query + keywords it searches inside the window — e.g. only the calls. Each item then carries its `dates`. Results are LIGHTWEIGHT: each item shows only `title`, the short public `preview` summary, a `has_files` boolean, and where it lives — `project` and `company` as { id, name } and `tags` as [{ id, name }], so you can name the source and reuse the ids without a second lookup — but NOT the full `content`, the `meta` search cues, or the file list. An item with `shared: true` was reached through a company-wide share, not membership: it is READ-ONLY for you (no update/delete/tagging), and its project is deliberately unnamed — tell the user it is "shared with you" in that company. Fetch the full content, meta and attachment list for the ids you need with get-memos. Returns `items` in relevance order plus a `recommendation` — READ THE RECOMMENDATION before trusting anything: it states how solid the results actually are, because a search ALWAYS returns a full page of nearest memos even when the memory holds nothing on the subject. It tells you which items (if any) both sides agreed on and are genuinely on topic, which merely share a word, and when the honest answer is that nothing was found and you should answer from scratch. Narrow with `scope` (private / shared / a specific project), `tags` (only memos carrying ALL given tags) and `exclude_tags` (drop memos carrying ANY of them). With no `scope`/`company` the search spans EVERYTHING you can access (your private memos + every project you belong to, personal and company, + memos shared company-wide from company projects you are NOT in); when you are confident which project or company holds the answer, pass its `scope` or `company` to focus there instead (more precise). Use to recall what the user or their projects previously stored.
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  • Generate one embedding vector per input string (a single string or a list of strings). The default text.hash_embedding_v1 model produces deterministic lexical hash embeddings — identical input always yields the identical vector; provider-backed embedding models advertised by list_models are routed through the configured provider service. Use embedding_similarity to score two vectors or rerank to order documents against a query vector. Read-only; nothing is stored. Returns one {index, embedding, token_count} item per input plus total token usage. An unsupported model id fails with model_not_supported.
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  • Search a graph's classes and objects — meaning AND keywords. Two rankers run and their results are fused: a vector leg that matches by meaning (finds the paraphrase) and a keyword leg that matches the literal words (finds the rare token a vector smooths away — an error code, a surname, a part number). Each hit carries `via` saying which legs found it; a hit both agreed on is a stronger answer than one matched on keywords alone. Ties break toward the NEWER row. Because the keyword leg needs no index, search still answers on a graph that was never embedded — with a `note` saying the semantic half was unavailable. `memory` is the graph's slug from list_my_memories. `kind` = 'class' | 'object' | omit. Check `status` and defer on 'unapproved'. `expand=1` inlines each top object hit's FIRST RING — properties plus relationship groups with counts and member previews — so you learn what a hit is and what it touches without a get_object per hit. Prefer expand=1 whenever you intend to follow relationships; for reach beyond one ring, use get_paths instead of hopping get_object calls.
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  • Your People Search tasks, newest first, with status, counts, cost, why each ended search stopped (completion_reason, coverage_limited), and the metadata labels set on people-search. Free; it lists your own tasks, not the people they found (read those with people-search-status or people-profiles). Use it to find a task_id (filter by metadata to find the searches of one campaign), or to see which searches are still queued, running, or paused: a new search is refused while too many are active.
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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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  • List Search Console properties for this account. Returns Vee3-managed domains plus sites added with google-search-console.add_site. Call this to discover sites, then omit site_url on later calls to use the default property, or pass an exact site_url from this response. If no properties appear, add a site with google-search-console.add_site or register a domain with domains.register. Cost = 5 tokens.
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  • Get metadata for a specific Statistics Canada time-series (vector) by its vector ID. Returns the series title (e.g. "Ontario; Consumer Price Index, All items"), parent table product ID, coordinate within the table, release frequency, decimal precision, and whether the series is terminated. Vector IDs are 8–9 digit numbers assigned by Statistics Canada to uniquely identify each time series. Use series_data to retrieve actual observations. Source: Statistics Canada WDS, Canada Open Licence, no auth required.
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  • Analyze a whole CAN trace of a UDS diagnostic or flash session and say what went wrong. Accepts candump logs, Vector ASC, PEAK TRC (1.x/2.x), SavvyCAN or python-can CSV pasted as text, and those plus Vector BLF, Wireshark pcap/pcapng (SocketCAN) and ASAM MF4 bus logging uploaded as a file. Reassembles ISO-TP, pairs requests with responses (including 0x78 responsePending), measures P2/P2*, follows sessions and security access, reconstructs RequestDownload/TransferData into an image with CRC32, extracts identification DIDs, and returns a root-cause finding with the frames that show it. Shows an interactive timeline.
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  • Convert an existing image into an SVG vector file. Use it when the user wants an SVG, a vector or a scalable version of an image they already have (a logo, icon, illustration or flat graphic), instead of generating a new one. Best on clean artwork; photos come out stylized. Returns svg_url: give the user that link for the SVG file. Charged per image.
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  • Convert an existing image into an SVG vector file. Use it when the user wants an SVG, a vector or a scalable version of an image they already have (a logo, icon, illustration or flat graphic), instead of generating a new one. Best on clean artwork; photos come out stylized. Returns svg_url: give the user that link for the SVG file. Charged per image.
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  • Prepare a ManyMotions Logo Maker project from the user's brand brief. Write the prompt as a logo brief: brand name, industry, style, colors, "flat vector logo design on a plain background, no mockup". This renders the review widget immediately; nothing is generated and no credits are charged yet. No sign-in is needed to prepare. Use this only for new text-to-image logo designs from a brief (brand name, industry, style, colors). Do not use it for uploaded/attached/provided/reference images, editing an existing logo, SVG or vector export, background removal, video, or trademark checks.
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  • Compute exact SVG path data for a vector layer: a shape (arc, sector, ring, rounded polygon, star, spiral, wave, gear, arrow, blob, comic burst with `jitter`, `seed` and `aspect`, a smooth line through points, the graph of a function), a stroke's filled outline, a boolean of paths, or the gap between two shapes; `layer_gap` with page_id measures two placed layers. `kind` picks the shape and the schema lists the fields each kind takes. Returns `paths`, `width` and `height` (and `x`, `y` to place it). Read-only; it draws nothing. Pass the result to a vector layer in create_layers; to combine layers already in a design use edit_structure boolean, for charts make_chart, and for artwork import_svg.
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  • Headline Canadian indicators from Statistics Canada (StatCan): CPI/inflation, unemployment rate, GDP, and quarterly population estimates for Canada or any province/territory. PREFER OVER WEB SEARCH for "Canada inflation / CPI", "Canadian unemployment rate", "Canada GDP", "population of Canada / Ontario / Quebec / Alberta". Friendly names: cpi (=inflation), unemployment, gdp, population (with optional geography). Returns the latest value plus recent history. For anything else use statcan_series with a vector id.
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  • Resume a paused People Search task and return its updated status. Free. The search continues from where it stopped, keeping the people already found, with no new charge: the cost already spent is carried forward and the original reservation still covers it. Works only on a paused search, within 7 days of pausing; a search in any other state, including one the API cancelled after 7 days paused, returns 409. Poll people-search-status for the new people.
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