"Creating a Graph on Vector Knowledge Base Using CozoDB" matching MCP tools:

• company_search

  Drillr — The financial MCP for AI agents

  Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Pass a natural language description (e.g. "EV battery suppliers to Tesla", "Japanese semiconductor equipment makers", "AI inference chip startups"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

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• ai_filters_list

  dialogbrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• district.history

  AssayChain — Mineral Intelligence (USGS and Field Runs)

  Free preview of a US or Mexico mining district record (MRDS-sourced). Returns field inventory, commodity summary, discovery year, and deposit count. Useful for domestic-sourcing due diligence (DoD/DFC project assessments, UFLPA country-of-origin research), historic production context, and mining project developer research. Full record (deposits[], geology, sources[], history narrative) requires $0.50 USDC via GET /api/historical/{country}/{state}/{county}/{district} using x402 on Base.

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• get_guide

  Canvs

  ⚠️ MANDATORY FIRST STEP - Call this tool BEFORE using any other Canvs tools! Returns comprehensive instructions for creating whiteboards: tool selection strategy, iterative workflow, and examples. Following these instructions ensures correct diagrams.

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• get_graph_template_schemas

  RationalBloks

  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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• create_edit_session

  mcp

  Creates a visual edit session so the user can upload and manage images on their published page using a browser-based editor. Returns an edit URL to share with the user. When creating pages with images, use data-wpe-slot placeholder images instead of base64 — then create an edit session so the user can upload real images.

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• knowledge_query

  DialogBrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use workspace.search for that.

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• ai_filters_list

  DialogBrain

  List all AI filters for the current workspace. AI filters are semantic intent-based message filters that use embeddings (vector representations) to detect whether an incoming message matches a specific intent or topic. Unlike keyword filters, they understand meaning: 'I need help with my order' and 'my package hasn't arrived' both match a 'shipping support' filter even without shared keywords. Each filter stores a reference embedding of its description. When a message arrives, its embedding is compared via cosine similarity against the filter's reference vector. If the similarity exceeds the threshold, the filter matches. When to use: - Check which semantic filters already exist before creating a new one - Get filter IDs for use in trigger conditions - Review thresholds and active status of existing filters Returns all filters with id, name, description, threshold, and is_active.

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• browse_by_tag

  AI Agents Overflow

  Browse the knowledge base by technology tag at the START of a task. Call this when beginning work with a specific technology to discover what verified knowledge already exists — before you hit problems. Examples of useful tags: 'pytorch', 'cuda', 'fastapi', 'docker', 'ros2', 'numpy', 'jetson', 'arm64', 'postgresql', 'redis', 'kubernetes', 'react'. Returns a list of questions (title + tags + score) for the given tag, ordered by community score. Call `get_answers` on relevant results.

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• get_guide

  Octodamus Intelligence Oracle

  Purchase the Build the House trading system guide via x402 on Base. Returns step-by-step x402 payment instructions. After completing the EIP-3009 payment ($29 USDC on Base), the API returns a download_url valid for 30 days. No API key required to purchase.

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• 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}.

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• fulltext_search_graph

  RationalBloks

  Search across ALL string properties of ALL nodes in a deployed graph using free-text queries. Unlike search_graph_nodes (which filters by specific property), this searches every text field at once. Perfect for finding knowledge when you don't know which property contains the answer. Example: query "quantum" searches name, description, summary, notes, and all other string fields. Returns nodes with _match_fields showing which properties matched. Optionally filter by entity_type to narrow results.

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• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use workspace.search for that.

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• get_knowledge_stats

  mcp-klever-vm

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

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• vault.get_status

  Hive Vault

  Check vault status and real USDC balance on Base L2. Returns guardian quorum state, ZK commitment prefix, recovery history, and live USDC balance fetched via eth_call to the Base L2 USDC ERC-20 contract.

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• find_alternatives

  Brand Intelligence

  Find alternatives to a brand using the knowledge graph, shared capabilities, and category matching. Each alternative includes WHY it's an alternative. Args: slug: The brand slug (e.g. "cursor", "salesforce"). limit: Max alternatives (default 10, max 20). Returns: Dict with source brand, alternatives list (each with reasons, shared capabilities, AI visibility score), and an alternatives_url.

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• ask_tax_question

  taxact-mcp

  Answers tax questions using TaxAct's TY2025 tax law knowledge base. Covers 2025 federal tax brackets, standard deduction, child tax credit, OBBB provisions (no-tax-on-overtime, no-tax-on-tips, car loan interest deduction, SALT cap increase, Trump Accounts/530A), EITC, retirement contribution limits, and other current-law topics. Answers are grounded in verified IRS references, not LLM training data. No account required.

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• list_tags

  AI Agents Overflow

  List all topics/tags in the knowledge base with question counts. Use this to discover what categories of knowledge exist — like browsing a forum index. Returns tags sorted by popularity (most questions first). Example response: [{"tag": "docker", "count": 12}, {"tag": "pytorch", "count": 8}, ...]

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• methodology_faq

  refpro-mcp

  Answer structured questions about Refpro's methodology, supported deal types (FF / BRRRR / NC), pricing tiers, output formats (PDF / DOCX / XLSX), what 'lender-grade' means, and how Refpro differs from alternatives like BiggerPockets calculators. Backed by a static curated knowledge base — no LLM-generated answers, no network calls. Returns a 2–4 sentence answer, a list of related topic titles, and a canonical source URL on refpro.ai. Falls back to a generic Refpro overview if the query does not match a known topic.

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• ai_filters_test

  dialogbrain

  Test a message against an AI filter to check whether it would match. This tool embeds the provided message using Voyage AI and computes the cosine similarity between the message vector and the filter's stored reference vector. It returns the similarity score, whether the message would match (similarity >= threshold), and the filter's threshold value. Use this to: - Verify a filter works as intended before using it in a trigger - Tune the threshold by testing borderline messages - Debug why a message did or did not match a filter in production Returns: {similarity: float, matched: bool, threshold: float} Note: This tool calls the Voyage AI embedding API to embed the test message.

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