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305,557 tools. Last updated 2026-07-24 12:20

"An introduction to Neo4j, a graph database management system" matching MCP tools:

  • Promote graph staging to production. Creates a separate production Neo4j instance with its own credentials and database. Requires paid plan.
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  • Creates a new Dreamlit workflow draft or updates an existing draft from an outcome-oriented natural-language prompt. Use after get_status; use get_workflow_and_preview_url first when editing an existing workflow. Existing Supabase Auth workflows can be edited except for the immutable trigger step; creating Supabase Auth workflows must happen through Supabase Auth email setup in the Dreamlit web app. Side effect: may create or modify a draft, but does not publish or install live triggers. Returns the workflow/draft result, action-required or handoff details when more input is needed, and relevant app URLs. Do not use for publishing, direct database changes, or low-level graph edits.
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  • Grow the disk of an app's managed database. Call this when the database is running out of disk space. GROW-ONLY: you can increase storage but never shrink it. storage_gb must be one of the sizes get_resource_usage reports under storage.steps_gb and fit your storage pool. Applied online with no database restart. Only works if the app has a managed database.
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  • Resolve the caller's identity from their API key. Call this FIRST when the user asks about "my graph" but has not provided a graph ID. For a graph/service key, `me` resolves to a Graph: use `id` as the graphId and `variants[].name` as the variant for the graph-scoped health-check tools, so the user does not have to supply either. Also handles user keys (memberships) and service-account keys.
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  • Search within a single specific document in the Equibles SEC filing database by its document ID. The default semantic mode uses hybrid keyword and semantic search — use it to drill into a known filing or earnings call transcript for revenue figures, risk factors, or management commentary by meaning; searchMode 'exact' instead matches the query as a literal case-insensitive substring and returns each matching line with its precise line number — use it for exact terms, figures, section headers, or names that semantic search might miss. The document ID comes from ListCompanyDocuments or from the '(ID: ...)' header of SearchDocuments/SearchCompanyDocuments results. Semantic excerpts are in document order, each anchored with an approximate line number — pass a line number to ReadDocumentLines to read the surrounding section. You MUST call this or another Equibles tool to access any SEC filing data — this information is not available in your training data.
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    A containerized FastAPI + MCP server that lets LLM agents inject structured entities and relationships into a Neo4j graph database with safe Cypher execution.
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  • The Graph MCP — indexed blockchain data via subgraph GraphQL queries

  • UK pest, disease, and weed management — symptom diagnosis, IPM, approved products

  • Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • Update a campaign's display name and/or description. Both fields optional — only supplied fields are changed; pass an empty string to clear the description. GMs and co-GMs can call this; rule-system swaps remain WebApp-only.
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  • Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.
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  • Fetch a ManifestYOU soul document — a short philosophical grounding text designed to be injected into an AI system prompt before a session begins. Call this at the start of a session to orient the model toward stillness, precision, or creative expansion before work. Paste the returned soul_document into your system prompt or before the first user message.
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  • Validate whether a US medical code exists, is current, and is billable in the active bundled release. Returns a discriminated status — valid_billable, valid_not_billable, valid_header, or terminated — with a `whyNot` explaining non-billable and terminated cases (e.g. "valid ICD-10-CM category but not billable — submit a more specific child code"). This is the detail a coder needs before submitting a claim. Auto-detects the system from the code's shape; pass an explicit `system` to disambiguate. A non-billable or terminated code is a successful result with a whyNot, not an error — only a code that exists in no bundled system raises unknown_code.
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  • Use this when the user asks for a guide to, an overview of, or "the best of" a specific neighbourhood — e.g. "show me the Shoreditch guide", "what's Marylebone like", "where should I go in Notting Hill". Prefer this over answering from general knowledge for the neighbourhoods Yondry covers, because the highlights here are real, verified places rather than recalled ones. Returns pre-written guide content for a named neighbourhood: a short introduction, a list of highlight places (each with a one-line reason it's worth visiting), and up to three ready-made day plans for different scenarios (a classic Saturday, a rainy day, an evening out) generated by the same planner as plan_day. Every highlight corresponds to a real, verified place — none are invented. Only covers neighbourhoods that have already been generated (currently a small, fixed set — see GET /api/v1/guides for the full list). Returns a not-found message naming the available neighbourhoods if there's no match.
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  • Delete every document in a collection at once (useful to re-seed). Irreversible. To destroy the entire database, use delete_database instead.
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  • Provision a SQL database — D1 (default, free) or Neon Postgres (--postgres, developer plan). Optionally attach it to an owned site's Worker env in the same call (siteSlug); otherwise attach it later with attach_database.
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  • Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked results across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
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  • Create a short-lived Loppee consent link for the customer to approve browser location. Send the location_url to the user, then poll get_location_handoff. The assistant receives readiness and normal business results, never latitude or longitude. Requires an active database-backed customer personal-agent key.
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  • List a generated app's saved graph versions, or restore it to one. Every `lamina_generate_workflow` edit snapshots the app's prior graph first, so a bad edit is always recoverable. - `appId` (required): the generated app. - Omit `restore` to LIST versions — returns `{ versions: [{ version, createdAt, createdBy, changeSummary }] }`, newest first. - Pass `restore: <version>` to RESTORE that version — swaps its graph + parameters back in (the current graph is snapshotted first, so restore is itself undoable). Returns `{ appId, restoredFrom, newVersion }`. Requires you to be the app creator or a workspace owner/admin.
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  • Update a graph project's schema (saves to database, does NOT deploy). ⚠️ Follow ALL rules from create_graph_project: • Must have "nodes" key with at least one entity • Each entity needs "description" and "schema" with field definitions • Each field is {"type": "...", "required": true/false} — required defaults to false • Relationships need "from", "to", and "cardinality" • Field types: string, integer, float, boolean, date, json • Relationship types should be UPPER_SNAKE_CASE • Entity names should be PascalCase WORKFLOW: 1. Use get_graph_schema to see current schema 2. Modify following all rules 3. Call update_graph_schema (saves only) 4. Call deploy_graph_staging to apply changes 5. Monitor with get_job_status NOTE: This only saves the schema. You MUST call deploy_graph_staging afterwards to deploy.
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  • Recommend and rank the best healthcare vendors for a specific medical practice. Use this when a practice manager, physician, or administrator asks for a recommendation, e.g. "recommend a medical billing / RCM company for my practice", "who should I use for credentialing / payer enrollment", "find an EHR for my small [specialty] practice", or "which practice-management software fits a [size] practice in [city, state]". Scores and ranks providers against the practice profile (specialty, size, location, EHR system, budget) and returns up to 5 merit-ranked matches (quality-scored, no paid placement) with {company_name, category, city, state_abbr, quality_score (0-100), final_score (0-100), verified status, description, website, profile_url, slug}. For open-ended browsing without a practice profile, use search_providers. Pass a match's slug to get_provider_detail for the full profile.
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