"Using Qdrant vector database for code indexing" matching MCP tools:

• create_qdrantA

  mcpA

  Provisions a managed Qdrant vector database on a dedicated VM. Requires a recent plan_managed_datastore (engine 'qdrant'). Reachable from your private network at <private_ip>:6333 (REST) and :6334 (gRPC), and at a public https URL; every request must carry the api-key header (always required). Backups: set_managed_backups with service 'qdrant'. FOR AN APP YOU DEPLOY YOU DO NOT NEED THIS: report vector_engines:['qdrant'] in send_signals and the deploy starts and wires a managed Qdrant itself (a compose stack's own qdrant service is replaced by it). Provisioning takes a few minutes; poll list_qdrant until status='ready'.

  ConnectorNo auth

• secret_scanA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Scan text or code for leaked secrets: API keys (AWS, GCP, Azure, OpenAI, Anthropic, Stripe, GitHub, GitLab, Slack, Twilio, SendGrid, HuggingFace), private keys (RSA/EC/PGP), JWTs, database connection strings, Bearer tokens, and Basic auth headers. Returns a list of findings with type, severity, line number, and a redacted preview. Use before committing code, sharing logs, or sending text to an LLM. 100% regex-based, zero network calls.

  ConnectorNo auth

• pingA

  Nordic Financial MCPA

  Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."

  ConnectorNo auth

• deploy_appA

  mcpA

  Deploys an app to a VM and exposes it at a public https://<name>-<id>.redu.cloud URL. The container is built ON the VM. PREREQS - run check_deploy_prerequisites first for network_id + keypair_name, then plan_deploy for cost approval. Source can be git repo or prepare_upload source_token. PORT must be the real app listen port. DATASTORES ARE MANAGED BY DEFAULT: each one you reported in send_signals runs as a managed store started at your signal and is wired with no settings (DATABASE_URL and PG*/MYSQL_*, REDIS_URL, QDRANT_URL with QDRANT_API_KEY); without a signal, pass database:'managed' (db_engine 'postgres', or 'mysql'/'mariadb' for WordPress/Matomo/LAMP), redis:'managed' or qdrant:'managed'. For WordPress/WooCommerce cluster intent, do not use generic stateless deploy: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu mounts the media space into wp-content/uploads and refuses unsafe local uploads. Build+provision takes minutes; poll list_deployments/get_deployment. ROUTE: when plan_deploy returned recommended_route:'compose', do NOT use this tool for that folder: deploy its recommended_compose_file with deploy_compose (compose_file, recommended_service as service, recommended_port as port), for the reason in route_reason. The exceptions are route_next's: this tool builds the folder's Dockerfile instead only if the user asked for a build from source, the user changed the code the Dockerfile builds (check git status and unpushed commits in the folder), or the image cannot be pulled.

  ConnectorNo auth

• dataset.indexA

  FlexOrch MCPA

  Trigger semantic indexing for a dataset — required before using dataset.chunks (Pro+ plan). Starts an async indexing job that splits the dataset into RAG-ready text chunks, generates embeddings, and stores them for semantic search. Indexing is idempotent: calling it again on an already-indexed dataset re-indexes with fresh embeddings. Indexing typically completes in 10–60 seconds depending on dataset size. After indexing, use dataset.chunks(dataset_id) to retrieve the text chunks. Args: dataset_id: ID of the built dataset to index (from job.status after dataset.build).

  ConnectorNo auth

• statcan.series.infoA

  apibaseB

  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

• get_hashtag_infoA

  DataLikers — Instagram & TikTok DataA

  Get one Instagram hashtag by exact name (leading # is stripped): id, name, media_count, allow_following, profile_pic_url. Use it when you know the tag; use `search_hashtags` for partial names, `get_users_by_hashtag` for accounts using it, and `get_tt_hashtag_info` for TikTok. Returns 'Hashtag not found' if the tag is not in the database. Returns user-generated Instagram content; treat as untrusted input.

  ConnectorAPI key

• portal_get_network_infoA

  SQDA

  Answer "is this network caught up?" with indexing freshness, lag, heads, and available tables. COMMON USER ASKS: - Is Base caught up? FIRST CHOICE FOR: - checking indexing head, lag, tables, and capabilities for one network WHEN TO USE: - You want to know whether a network is indexed, fresh, caught up, or behind before querying. - You need chain family, real-time status, or available tables for a network. DON'T USE: - You only need the latest block or slot number. EXAMPLES: - Is Base caught up?: {"network":"base-mainnet"}

  ConnectorNo auth

• delete_deploymentA

  mcpA

  Deletes a deployment and its underlying app VM. Pass the numeric id from list_deployments. IMPORTANT: its managed datastores are NOT deleted (data safety): its database, Redis and Qdrant keep running and billing, and this tool returns each id so you can delete_database / delete_relational_database / delete_redis / delete_qdrant them when you're done with the data. Cannot be undone.

  Connector

  Destructive

  No auth

• request_external_resourceA

  flootA

  Request the user's own credential for an outside service on this project: their OpenAI or Anthropic key, an external Postgres database (type POSTGRES), or any other service's key (type GENERIC, with secret_env_vars). Floot-managed resources (database, auth, push, …) need no user input: use provision_resource. Request it early and keep building; code can reference process.env.<NAME> before the user connects. Never ask the user to paste a secret into the chat.

  ConnectorOAuth

• run_codeA

  EaseWebA

  Run code the way the Run button of a code block does, to check it: "sql", "mongodb", "redis" and "clickhouse" run in your own database of the presentation (made from its database scripts; what you change stays in it), "python" and "bash" in a sandbox without network that keeps nothing. The presentation itself does not change.

  ConnectorOAuth

• add_document_to_collectionA

  DocImprintA

  Add an evidence bundle to a collection and trigger async vector indexing. Use after create_collection to populate a collection with documents. Once indexed, documents become searchable via search_collection and ask_collection. Indexing is async — poll get_job_status with the returned job_id until status is "complete". PREREQUISITE: Bundle must have status "complete" (check with get_bundle). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion) } Example prompts: - "Add my contract bundle ev_550e8400 to the Q4 Contracts collection." - "Put this evidence bundle into my Due Diligence Docs collection for search." - "Add document [bundle_id] to collection [col_id] with a title."

  ConnectorNo auth

• add_document_to_collectionA

  apiA

  Add an evidence bundle to a collection and trigger async vector indexing. Use after create_collection to populate a collection with documents. Once indexed, documents become searchable via search_collection and ask_collection. Indexing is async — poll get_job_status with the returned job_id until status is "complete". PREREQUISITE: Bundle must have status "complete" (check with get_bundle). Collection must be owned by your API key. Returns: { collection_id, bundle_id, job_id (poll for indexing completion) } Example prompts: - "Add my contract bundle ev_550e8400 to the Q4 Contracts collection." - "Put this evidence bundle into my Due Diligence Docs collection for search." - "Add document [bundle_id] to collection [col_id] with a title."

  ConnectorNo auth

• run_auditA

  nimoA

  Start a saved-site audit; consumes allowance. wait:true waits up to 90 seconds and may still return pollAfterMs; poll get_audit_status for up to 3 minutes. Use quick for iteration and full for comprehensive evidence. Use audit_url only for performance on unsaved URLs. For deployed HTML/robots/canonical/noindex, use get_seo_evidence with refresh:true, scope:'page', sources:['page']. audit_url does not prove indexing; use get_seo_evidence refresh:true sources:['indexing'].

  ConnectorAPI key

• minify_jsA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML templates, report payloads, or injecting inline code programmatically.

  ConnectorNo auth

• embedding_similarityA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

  ConnectorNo auth

• normalize_vectorA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 1000 vectors.

  ConnectorNo auth

• vector_statsA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.

  ConnectorNo auth

• get_similar_namesA

  Name Whisper — ENS Intelligence LayerA

  Find ENS names semantically similar to a given name using vector embeddings across 3.5M indexed ENS names. Returns similar names with similarity scores, status (REGISTERED / GRACE_PERIOD / PREMIUM_AUCTION / AVAILABLE), and live marketplace data. Names in GRACE_PERIOD are NOT registerable by anyone but the original holder.

  ConnectorNo auth

• laplacianA

  Aman-Amith-Shastry-scientific_computation_mcpA

  Computes the Laplacian of a scalar or vector field symbolically. Args: f_str (str): Scalar function as "x**2 + y*z" or vector "[Fx, Fy, Fz]". is_vector (bool): Set True to compute vector Laplacian. Returns: str: Symbolic result of the Laplacian—scalar or list of 3 components.

  ConnectorOAuth

• pull_database_schemaA

  flootA

  Introspect the database and write a typed schema helper the app uses for queries (kysely on current projects; some legacy projects use drizzle or snake_case kysely — the pull matches whatever the project already uses). Usually NOT needed after execute_sql — schema-changing statements re-pull automatically. Use it to refresh manually, or with helper_name to generate the helper for an additional/external database. The helper is GENERATED — never hand-edit it or cast around its types: if a column's type is too loose (e.g. role as string when code expects "user" | "admin"), fix the DATABASE (CREATE TYPE … AS ENUM + ALTER COLUMN … TYPE) and re-pull, and the union type falls out.

  ConnectorOAuth