Search for new tools, APIs, MCP servers, and frameworks by keyword (e.g., 'vector databases', 'Claude integrations'). Returns matching developments with descriptions and sources.
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712,278 tools. Updated 2026-10-28 22:00
"Searching for technologies and tools related to embeddings and vector databases" matching MCP tools:
- Fast code search across any public git repo. Returns file paths, line numbers, and code snippets with context. Supports regex, boolean queries, fuzzy matching, and structural filters (declarations, usages, strings, comments). Dependency/build directories excluded by default. Searching for leaked secrets? Call code_analyze instead and read security_summary — it runs a real secret and credential scanner over the whole repository, where searching for key prefixes like "sk-", "ghp_" or "AIza" only matches the spellings you happened to think of.ConnectorNo auth
- Build (or top up) the vector index a graph's semantic_search reads. `semantic_search` matches against stored embeddings, so a graph that has never been embedded answers every query with ZERO results — indistinguishable from "nothing matches". Run this once per graph, and again after a large ingestion, to make newly added objects findable by meaning. Idempotent: entities already embedded for the current model are skipped unless `force`. `kinds` defaults to ['object', 'class']. Requires write scope. RETURNS QUICKLY, and usually unfinished. Embedding is CPU-bound, so one call spends a fixed wall-clock budget (~25s) and then reports what is left in `objects_remaining` / `classes_remaining`. It is safe to call again immediately to push it along; you do not have to loop it to zero, because a background worker drains the same backlog on the server.ConnectorAPI key
- Index a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embeddings (enables search_videos) - qa_only: transcription + captions (enables ask_video) - full: transcription + captions + embeddings (enables all tools) Scene detection is enabled by default and produces scene boundaries for get_scenes. Pass scene_detection=False to skip it. Prerequisites: if using video_id, the video must be in 'uploaded' status. Use get_video to check status before calling this tool.ConnectorNo auth
- Find templates in the Control Plane Template Catalog: databases, caches, queues, search, storage, gateways, AI, analytics, auth, security, automation, observability, developer tools, and self-hosted products. Pass `query` naming the capability or product needed ("vector database for RAG", "uptime monitoring with status page", "self-hosted Google Analytics"); when the user describes a goal or problem instead, decide the capability it needs and search for that. Results are ranked, each with a confidence, what must exist before install, and the template to pick instead for other needs. Omit `query` to list the catalog or one `category`. An app the user asks you to build, such as a blog, wiki, or shop, is built (plan_app) unless they name a template. Then pass the pick as `name` for its prerequisites, README notes, and the EXAMPLE values.yaml to copy for install_template. Postgres, MySQL, MariaDB, MongoDB, and Redis install with `add_database` unless they need a setting it does not take, such as backups.ConnectorOAuth
- The Vector Panda quickstart: install the veep Python SDK, upload a file, and run similarity queries end to end.ConnectorNo auth
Matching MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.AGPL 3.0
- AlicenseCqualityDmaintenanceEnables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.18MIT
Matching MCP Connectors
Rick and Morty MCP — wraps the Rick and Morty API (free, no auth)
Human-verified daily catalog of free and cheap AI model API credits and token promos.
- Generates normalized high-dimensional semantic vector embeddings settled via Solana SPL USDC ('x402' protocol headers) for decentralized RAG pipelines. Use when computing text embeddings paid via Solana USDC x402 signatures rather than Bitcoin Lightning; for extracting discrete JSON entities from unstructured text, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('encoding_format' defaults to 'float_array'), supports '?demo=true' or x402 (10 sats), and returns HTTP 400 if invalid.ConnectorNo auth
- Generates normalized high-dimensional semantic vector embeddings settled via Solana SPL USDC ('x402' protocol headers) for decentralized RAG pipelines. Use when computing text embeddings paid via Solana USDC x402 signatures rather than Bitcoin Lightning; for extracting discrete JSON entities from unstructured text, use extract_structured_json_entities instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('encoding_format' defaults to 'float_array'), supports '?demo=true' or x402 (10 sats), and returns HTTP 400 if invalid.ConnectorNo auth
- Detect website technology stack: CMS, frameworks, CDN, analytics tools, web servers, languages (via HTTP headers + HTML analysis). Use for passive reconnaissance; for full audit use audit_domain. Free: 30/hr, Pro: 500/hr. Returns {technologies: [{name, category, confidence%, version}]}.ConnectorNo auth
- List the shows most related to a podcast, best first — "shows like this show". Each result carries the related show's slug, a calibrated score in (0,1], and a coarse band (strong: same beat and audience; moderate: overlapping subject or audience; weak: a loose connection) to branch on. Add `include: ["basis"]` to see WHY each pair is related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors — use it to explain a recommendation or to keep only pairs related for the reason you care about (shared guests for booking, content for media planning). Related sets are precomputed per show from its transcripts, topic profile, guest roster, network and advertisers, restricted to the show's language. Only shows above a relatedness floor are listed, machine-generated and farmed feeds are never listed, and a publisher's duplicate feeds of one show appear once. An empty FIRST page is not an error: its `coverage` says whether the set is not computed yet, nothing cleared the floor, or the request's filters and the default policy removed everything; an empty page reached through a cursor is simply the end of the list. Not a topic browser: for shows that COVER a topic use `particle_podcast_resolve` with `topic_slug`. Not a guest lookup: for where a person has appeared use `particle_podcast_get_guest`. Not advertiser co-occurrence: use `particle_podcast_get_sponsors`. Every related show's slug feeds `particle_podcast_resolve`, `particle_podcast_list_episodes` and the other podcast tools; person slugs in the basis feed `particle_podcast_get_guest`, topic slugs feed `particle_podcast_resolve`'s `topic_slug`. For the five most related shows inline on a resolve, pass `include: ["related"]` to `particle_podcast_resolve` instead of calling this tool.ConnectorOAuth
- 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.ConnectorNo auth
- Turn text into embedding vectors with any embedding model in the Hubris catalogue — for semantic search, RAG, deduplication and clustering. Same as POST /v1/embeddings, OpenAI shape: data[i].embedding is the vector for input[i]. Pass a list of strings to embed a batch in one call, which is faster and cheaper than one call per string. Vectors are long: 1536 numbers make about 30 KB of JSON, and the whole answer lands in your context. Embed only what you will actually store or compare, use `dimensions` where the model allows it, and send large batches through the HTTP API (POST /v1/embeddings). Spends money from the account balance: billed per input token. usage.cost_rub is what this call cost.ConnectorAPI key
- Find dated findings about code export, GitHub sync, backend connections and databases for Bubble, Lovable, Base44 and Replit. Use this to discover record IDs; get_finding retrieves one exact record. Other topics return no matches, not a product verdict.ConnectorNo auth
- Sets the logical databases a user connects to (databases: a list, or null for all of them, future ones included) and whether it only reads; a field left out keeps its value. Applied within seconds; where its access is removed or lowered, its open connections are closed, so tell the user first if an application uses it. Not on the admin (it always has every database) nor on Valkey. Scope: databases.write.ConnectorDestructiveNo auth
- mlpeek: Create multimodal embeddings: images and text in one vector space. For text only use embed_texts. Returns 768-dim vectors of texts and images (URL or base64) in ONE space (EmbeddingGemma 2), comparable by dot product; `dimensions` 512/256/128, `input_type` text prefix, `image_detail` low/standard/high. At most 16 low, 8 standard or 4 high-detail images (10 MB each, public URLs). Over 4,096 text tokens or a bad image (named by index) is a 422; an unavailable model a 503 (none charged). Typically 0.05 s per short text and 1.5-3 s per image (6.5 s at high detail), plus a few seconds on first use. Price: USD 0.002 per item (1-16 items per call: USD 0.002-0.032). Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.htmlConnectorNo auth
- mlpeek: Create multimodal embeddings: images and text in one vector space. For text only use embed_texts. Returns 768-dim vectors of texts and images (URL or base64) in ONE space (EmbeddingGemma 2), comparable by dot product; `dimensions` 512/256/128, `input_type` text prefix, `image_detail` low/standard/high. At most 16 low, 8 standard or 4 high-detail images (10 MB each, public URLs). Over 4,096 text tokens or a bad image (named by index) is a 422; an unavailable model a 503 (none charged). Typically 0.05 s per short text and 1.5-3 s per image (6.5 s at high detail), plus a few seconds on first use. Price: USD 0.002 per item (1-16 items per call: USD 0.002-0.032). Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.htmlConnectorNo auth
- Generates normalized 3,072-dimensional or 768-dimensional semantic vector embeddings via Google Gemini Embedding 2 for RAG indexing and cosine-similarity search. Use when embedding text passages or queries via L402 Lightning with configurable Matryoshka dimensions; for running live Google Search grounded factual queries, use search_gemini_grounded_web instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('model' defaults to 'gemini-embedding-2'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.ConnectorNo auth
- Generates normalized 3,072-dimensional or 768-dimensional semantic vector embeddings via Google Gemini Embedding 2 for RAG indexing and cosine-similarity search. Use when embedding text passages or queries via L402 Lightning with configurable Matryoshka dimensions; for running live Google Search grounded factual queries, use search_gemini_grounded_web instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'text' ('model' defaults to 'gemini-embedding-2'), supports '?demo=true' or L402 (10 sats), and returns HTTP 400 if invalid.ConnectorNo auth
- mlpeek: Create multimodal embeddings: images and text in one vector space. For text only use embed_texts. Returns 768-dim vectors of texts and images (URL or base64) in ONE space (EmbeddingGemma 2), comparable by dot product; `dimensions` 512/256/128, `input_type` text prefix, `image_detail` low/standard/high. At most 16 low, 8 standard or 4 high-detail images (10 MB each, public URLs). Over 4,096 text tokens or a bad image (named by index) is a 422; an unavailable model a 503 (none charged). Typically 0.05 s per short text and 1.5-3 s per image (6.5 s at high detail), plus a few seconds on first use. Price: USD 0.002 per item (1-16 items per call: USD 0.002-0.032). Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/embeddings-rerank-ner-mcp-server.htmlConnectorNo auth
- Search the directory of developer services (hosting, databases, auth, email, SMS, payments, analytics, monitoring, LLM APIs, vector databases, search, CMS and more): what each provider sells, its free tier, its lowest paid price, caveats and the pricing page it came from. Wider than the cost engine: use it to find options or check a provider the other tools don't price. Entries marked candidate have no prices yet. A provider can carry availability (acquired, shutting_down) and an admin_note: say so when you mention it. Services that shut down are left out unless include_closed is true. Example: {"query":"postgres","category":"database"}.ConnectorOAuth
- Find notes by meaning using vector embeddings — surfaces conceptually related content even when no words are shared with the query (e.g. "machine learning" matches notes about neural networks). Covers notes, bookmarks, emails, and feed articles. Each result includes a similarity score from 0 to 1; only results above ~0.55 similarity are returned, so an empty list means nothing conceptually close exists — fall back to search_notes for exact words. Returns snippets unless full_text=true.ConnectorOAuth