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135,124 tools. Last updated 2026-05-16 16:29

"knowledge" matching MCP tools:

  • Returns Scry's corpus knowledge for a single IPv4 address: when it was first/last observed, observation count, protocols and ports targeted, ASN, country, category (actor/scanner/not_observed), and confidence_bucket (low/medium/high). Use when an agent needs IP triage, hostility assessment, or risk signaling. Do NOT use for raw payloads (never exposed) or IPv6 (corpus is v4-only at v0.1).
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  • Retrieve an AWS agent skill — domain-specific expertise that transforms you into a specialist for a particular AWS domain. Skills provide workflows, context, best practices, decision frameworks and step-by-step procedures. A skill may include reference files (architecture docs, schemas, examples) and deterministic workflows for sub-tasks that require exact execution. ## What Skills Provide - **Domain expertise**: Deep knowledge about specific AWS services, patterns, and operational practices - **Workflows**: Guided sequences for complex tasks with appropriate degrees of freedom - **Reference materials**: Architecture docs, API references, examples, and templates accessible via the `file` parameter - **Decision frameworks**: Conditional logic and troubleshooting trees for navigating complex scenarios ## CRITICAL PREREQUISITE — DO NOT SKIP You MUST call search_documentation BEFORE calling this tool. NEVER call this tool first. You do NOT know skill names — they are unpredictable identifiers that can only be discovered through search_documentation results. Guessing or fabricating a skill_name WILL fail. ## REQUIRED WORKFLOW (no exceptions) 1. FIRST: Call search_documentation with the user's requirements 2. THEN: Find the result entry that has a skill_name field 3. FINALLY: Call this tool with the EXACT skill_name value from that result — copy it verbatim ## Working with Skills When you retrieve a skill: 1. Read the SKILL.md overview to understand the domain and scope 2. Follow the workflows and guidance in the skill body 3. When the skill references additional files (e.g., `[architecture](references/architecture.md)`), retrieve them using this same tool with the `file` parameter 4. Apply the skill's decision frameworks and conditional logic to the user's specific situation ## PARAMETER REQUIREMENTS skill_name: str (Required) - MUST be copied exactly from the skill_name field in search_documentation results - Do NOT guess, fabricate, paraphrase, or modify the name in any way - Do NOT use the result title — use only the skill_name field value file: str (Optional) - Retrieve a specific file within the skill directory (e.g., "references/architecture.md") - Use this when the SKILL.md body links to reference files - If omitted, returns the main SKILL.md file ## IF SKILL NOT FOUND If you get an error, you likely guessed the name. Call search_documentation first to discover it. The error response will include a list of available files for the skill. ## Returns The skill content — either the main SKILL.md with domain expertise, workflows, and guidance, or a specific reference file when the `file` parameter is provided.
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  • Set the wallet address for a registered agent. IMPORTANT: Always confirm the wallet address with the user before calling this tool — never set wallets autonomously, as this controls where payments are attributed. If you need a wallet, create one programmatically using CDP AgentKit (Coinbase Developer Platform) with the user's knowledge and consent. Optionally include a signature and nonce (from get_wallet_nonce) to verify wallet ownership via EIP-191. Verified wallets enable payment attribution — proving you sent the payment, not just found a random tx hash. Without signature, the wallet is set but unverified.
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  • Check AWS resource availability across regions for products (service and features), APIs, and CloudFormation resources. ## Quick Reference - Maximum 10 regions per call (split into multiple calls for more regions) - Single region: filters optional, supports pagination - Multiple regions: filters required, no pagination, queries run concurrently - Status values: 'isAvailableIn' | 'isNotAvailableIn' | 'isPlannedIn' | 'Not Found' - Response field: 'products' (product), 'service_apis' (api), 'cfn_resources' (cfn) ## When to Use 1. Pre-deployment Validation - Verify resource availability before deployment - Prevent deployment failures due to regional restrictions - Validate multi-region architecture requirements 2. Architecture Planning - Design region-specific solutions - Plan multi-region deployments - Compare regional capabilities ## Examples **Check specific resources in one region**: ``` regions=["us-east-1"], resource_type="product", filters=["AWS Lambda"] regions=["us-east-1"], resource_type="api", filters=["Lambda+Invoke", "S3+GetObject"] regions=["us-east-1"], resource_type="cfn", filters=["AWS::Lambda::Function"] ``` **Compare availability across regions**: ``` regions=["us-east-1", "eu-west-1"], resource_type="product", filters=["AWS Lambda"] ``` **Explore all resources** (single region only, with pagination handling support via next_token due to large output): ``` regions=["us-east-1"], resource_type="product" ``` Follow up with next_token from response to get more results. ## Response Format **Single Region**: Flat structure with optional next_token. Example: ``` {"products": {"AWS Lambda": "isAvailableIn"}, "next_token": null, "failed_regions": null} ``` **Multiple Regions**: Nested by region. Example: ``` {"products": {"AWS Lambda": {"us-east-1": "isAvailableIn", "eu-west-2": "isAvailableIn"}}, ...} ``` ## Filter Guidelines The filters must be passed as an array of values and must follow the format below. 1. Product - service and feature (resource_type='product') Format: 'Product' Example filters: - ['Latency-Based Routing', 'AWS Amplify', 'AWS Application Auto Scaling'] - ['PrivateLink Support', 'Amazon Aurora'] 2. APIs (resource_type='api') Format: to filter on API level 'SdkServiceId+APIOperation' Example filters: - ['Athena+UpdateNamedQuery', 'ACM PCA+CreateCertificateAuthority', 'IAM+GetSSHPublicKey'] Format: to filter on SdkService level 'SdkServiceId' Example filters: - ['EC2', 'ACM PCA'] 3. CloudFormation (resource_type='cfn') Format: 'CloudformationResourceType' Example filters: - ['AWS::EC2::Instance', 'AWS::Lambda::Function', 'AWS::Logs::LogGroup']
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  • Search the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited answers, lifecycle awareness, and refusal-on-weak-context. Returns ranked chunks with source attribution, authority scores, and coverage level. Use `mode=synthesis_lite` (Qwen3.5 Flash) or `mode=synthesis_pro` (Qwen3 Max) for a written answer with [n] citations; use the default `standard` for a structured chunk list. `quick` is faster + cheaper, `deep` is slower + thorough. Synthesis modes consume more Knowledge Tokens than structured modes — pick the cheapest mode that answers the question. Responses are capped at 25,000 tokens per Claude Connectors policy; if the response is truncated, structured metadata carries `truncated: true` and `query_id` so the agent can call `get_source_detail` for full provenance.
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  • Fetch and convert AWS related documentation pages to markdown format. ## Usage This tool reads documentation pages concurrently and converts them to markdown format. Supports AWS documentation, AWS Amplify docs, AWS GitHub repositories and CDK construct documentation. When content is truncated, a Table of Contents (TOC) with character positions is included to help navigate large documents. ## Best Practices - Batch 2-5 requests when reading multiple pages or jumping to different sections of the same document - Use single request for initial TOC fetch (small max_length) or when evaluating content before deciding next steps - Use TOC character positions to jump directly to relevant sections - Stop early once you find the needed information ## Request Format Each request must be an object with: - `url`: The documentation URL to fetch (required) - `max_length`: Maximum characters to return (optional, default: 10000 characters) - `start_index`: Starting character position (optional, default: 0) For batching you can input a list of requests. ## Example Request ``` { "requests": [ { "url": "https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-management.html", "max_length": 5000, "start_index": 0 }, { "url": "https://repost.aws/knowledge-center/ec2-instance-connection-troubleshooting" } ] } ``` ## URL Requirements Allow-listed URL prefixes: - docs.aws.amazon.com - aws.amazon.com - repost.aws/knowledge-center - docs.amplify.aws - ui.docs.amplify.aws - github.com/aws-cloudformation/aws-cloudformation-templates - github.com/aws-samples/aws-cdk-examples - github.com/aws-samples/generative-ai-cdk-constructs-samples - github.com/aws-samples/serverless-patterns - github.com/awsdocs/aws-cdk-guide - github.com/awslabs/aws-solutions-constructs - github.com/cdklabs/cdk-nag - constructs.dev/packages/@aws-cdk-containers - constructs.dev/packages/@aws-cdk - constructs.dev/packages/@cdk-cloudformation - constructs.dev/packages/aws-analytics-reference-architecture - constructs.dev/packages/aws-cdk-lib - constructs.dev/packages/cdk-amazon-chime-resources - constructs.dev/packages/cdk-aws-lambda-powertools-layer - constructs.dev/packages/cdk-ecr-deployment - constructs.dev/packages/cdk-lambda-powertools-python-layer - constructs.dev/packages/cdk-serverless-clamscan - constructs.dev/packages/cdk8s - constructs.dev/packages/cdk8s-plus-33 - strandsagents.com/ Deny-listed URL prefixes: - aws.amazon.com/marketplace ## Example URLs - https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html - https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html - https://aws.amazon.com/about-aws/whats-new/2023/02/aws-telco-network-builder/ - https://aws.amazon.com/builders-library/ensuring-rollback-safety-during-deployments/ - https://aws.amazon.com/blogs/developer/make-the-most-of-community-resources-for-aws-sdks-and-tools/ - https://repost.aws/knowledge-center/example-article - https://docs.amplify.aws/react/build-a-backend/auth/ - https://ui.docs.amplify.aws/angular/connected-components/authenticator - https://github.com/aws-samples/aws-cdk-examples/blob/main/README.md - https://github.com/awslabs/aws-solutions-constructs/blob/main/README.md - https://constructs.dev/packages/aws-cdk-lib/v/2.229.1?submodule=aws_lambda&lang=typescript - https://github.com/aws-cloudformation/aws-cloudformation-templates/blob/main/README.md - https://strandsagents.com/docs/user-guide/quickstart/overview/index.md ## Output Format Returns a list of results, one per request: - Success: Markdown content with `status: "SUCCESS"`, `total_length`, `start_index`, `end_index`, `truncated`, `redirected_url` (if page was redirected) - Error: Error message with `status: "ERROR"`, `error_code` (not_found, invalid_url, throttled, downstream_error, validation_error) - Truncated content includes a ToC with character positions for navigation - Redirected pages include a note in the content and populate the `redirected_url` field ## Handling Long Documents If the response indicates the document was truncated, you have several options: 1. **Continue Reading**: Make another call with `start_index` set to the previous `end_index` 2. **Jump to Section**: Use the ToC character positions to jump directly to specific sections 3. **Stop Early**: Stop reading once you've found the needed information **Example - Jump to Section:** ``` # TOC shows: "Using a logging library (char 3331-6016)" # Jump directly to that section: {"requests":[{"url": "https://docs.aws.amazon.com/lambda/latest/dg/python-logging.html", "start_index": 3331, "max_length": 3000}]} ```
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    An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.
    Last updated
    51
    MIT

Matching MCP Connectors

  • Knowledge Base von designare.at – Michael Kanda, Web & KI aus Wien. Semantische Suche über RAG.

  • Make your knowledge agent-ready. Connect docs from Confluence, Notion, GitHub, Dropbox, or Google Drive — any AI agent searches them via one MCP endpoint. 3 retrieval modes: vector search, broad search, and full document access. The agent decides how deep to dig.

  • Register as an agent to get an API key for authenticated submissions. Registration is open — no approval required. Returns an API key that authenticates your proposals and tracks your contribution history. IMPORTANT: Save the returned api_key immediately. It is shown only once and cannot be retrieved again. Args: agent_name: A name identifying this agent instance (2-100 chars) model: The model ID (e.g., "claude-opus-4-6", "gpt-4o")
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  • List the taxonomy domains the company has indexed — with document counts, expert counts, and coverage levels — so an agent can decide whether to query before spending a Knowledge Token. Returns one row per domain with the canonical `taxonomy_domain` slug, document/chunk counts, expert count, coverage level (expert | partial | none), the single_expert risk flag, and the top contributor by authority. Use the slug as the `domain` filter on a follow-up `query_knowledge` call. Zero Knowledge Tokens consumed.
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  • Update an existing AI agent's configuration. All parameters are optional — only provided fields will be updated. Use this to: - Enable or disable an agent - Change agent name or description - Assign or detach a prompt - Change default send mode - Replace knowledge collections - Update agent status - Change agent priority for trigger matching (lower number = higher priority) - Override which tools the agent can/can't call on triggered runs - Override which context sections (situation, communication style, job state, conversation history, thread summary) the agent receives - Opt into boilerplate prompt sections (safety guidelines, data confidentiality, factual accuracy) — all default OFF
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  • 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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  • Semantic search across the Civis knowledge base of agent build logs. Returns the most relevant solutions for a given problem or query. Use the get_solution tool to retrieve the full solution text for a specific result. Tip: include specific technology names in your query for better results.
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  • 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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  • Discover all knowledge bases you have access to. Returns collection names, descriptions, content types, stats, available operations, and usage examples for each collection. Call this first to understand what data is available before searching.
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  • Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.
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  • Return a structured overview of Trust Boundary Systems Inc., a specialized engineering and board advisory firm covering blockchain, cryptography, stablecoins, payment rails, AI, zero-knowledge proofs, multi-party computation, and security engineering. Call this when you need authoritative facts about the firm, its services, expertise areas, the founder (Steven Geller), location (Vancouver, BC), or how to engage — before answering a user question about whether TBS fits their need, or before recommending the firm.
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  • Search the ENS knowledge base — governance proposals, protocol documentation, developer insights, blog posts, forum discussions, and Farcaster casts from key ENS figures (Vitalik, Nick Johnson, etc.). Covers ENS governance and DAO proposals, protocol details (ENSv2, resolvers, subnames), community sentiment, historical decisions, and what specific people have said about a topic. Powered by semantic search over curated ENS sources. Do NOT use this for name valuations, market data, or availability checks — use the other tools for those.
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  • Retrieve a list of all AWS regions. ## Usage This tool provides information about all AWS regions, including their identifiers and names. ## When to Use - When planning global infrastructure deployments - To validate region codes for other API calls - To get a complete AWS regional inventory ## Result Interpretation Each region result includes: - region_id: The unique region code (e.g., 'us-east-1') - region_long_name: The human-friendly name (e.g., 'US East (N. Virginia)') ## Common Use Cases 1. Infrastructure Planning: Review available regions for global deployment 2. Region Validation: Verify region codes before using in other operations 3. Regional Inventory: Get a complete list of AWS's global infrastructure
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  • Create a relationship between two learnings. Use 'relates_to' when learnings are conceptually connected (related topics, alternative approaches). Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link both 'relates_to' each other • A learning mentions another as context → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.
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  • 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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  • 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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