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127,264 tools. Last updated 2026-05-05 13:07

"A tool for storing chat conversations and generating knowledge graphs" matching MCP tools:

  • Schedule a downgrade to Free at the end of the current billing period. The org keeps its current plan (Pro or Scale) and paid limits until the period ends. No-op when already on Free. Consent-gated. Two consent surfaces, you pick via `mode`: (1) `chat` (default): FIRST call returns { status: 'confirmation_required', confirm_token, message, expires_in }; surface to your user and re-call within 60s with `confirm_token` set. (2) `web`: FIRST call returns { status: 'approval_required', approval_url, polling_url }; print approval_url in chat, user clicks + approves, then poll polling_url for the result.
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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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  • Returns a token-efficient batch of conversations for bulk analysis. Default output is summaries only (id, summary, trust_score, status, created_at) plus the perspective outline; opt in to full XML transcripts via include_transcripts=true. Default format is TOON (compact); JSON available. Behavior: - Read-only. - Errors when the perspective is not found or you do not have access. - Filters: period (7d/30d/90d/all, default 30d), status, trust_score range. Page size up to 50, default 10. Pass nextCursor back as cursor for the next batch. - Response includes total_matching, returned_count, has_more, nextCursor for sizing. - Citation format when transcripts are included: "conversation_id:message_index". When to use this tool: - Thematic analysis, sentiment distribution, or pattern detection across many conversations. - Building a research summary from many summaries cheaply, then drilling into specific transcripts. - Bulk export with filters. When NOT to use this tool: - Need one conversation in full detail (voice snippets, trust dimensions) — use perspective_get_conversation. - Just need a browsable list with metadata — use perspective_list_conversations. - Aggregate counts only — use perspective_get_stats (call first to size the dataset before batching).
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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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  • 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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  • 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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Matching MCP Servers

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    An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.
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    A server that enables users to chat with each other by repurposing the Model Context Protocol (MCP), designed for AI tool calls, into a human-to-human communication system.
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Matching MCP Connectors

  • Search, order, and manage eSIM data packages for 190+ countries.

  • The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.

  • 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 MidOS knowledge base for relevant information. Use this as your FIRST tool to discover what knowledge is available. Returns ranked results with titles, snippets, and quality scores. Args: query: Search query (keywords or topic) limit: Max results (1-20, default 5) domain: Filter by domain (engineering, security, architecture, devops, ai_ml) Returns: JSON array of matching atoms with title, snippet, score, and source
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  • Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.
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  • Save a file (PDF, PPTX, DOCX, etc.) to a client's record in the broker's CRM. Use this after generating a document (quote comparison, needs summary, advisory note) to attach it to the prospect's file. The client must already exist as a lead (use save_lead first). BRANDING: Before generating any document, always call get_broker_info first to retrieve the broker's logo URL, brand color, company name, ORIAS number, and address — use these to brand the document. The file content must be base64-encoded.
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  • AI-powered RAG chat, document analysis, and shareable summaries. Create chats, send messages, read AI responses, and generate shareable summaries. Works on both workspaces and shares. Side effects: chat-create and message-send consume AI credits (1 credit per 100 tokens). Destructive action: chat-delete permanently removes a chat. Actions & required params (all actions require profile_type + profile_id): - chat-create: type, query_text (workspace req'd, share optional) (+ optional: privacy, files_scope, folders_scope, files_attach, personality) - chat-list: (+ optional: include_deleted, limit, offset) - chat-details: chat_id - chat-update: chat_id, name - chat-delete: chat_id - chat-publish: chat_id - message-send: chat_id, query_text (+ optional: personality, files_scope, folders_scope, files_attach) - message-list: chat_id (+ optional: limit, offset) - message-details: chat_id, message_id - message-read: chat_id, message_id - share-generate: node_ids (workspace) | files (share) - transactions: (workspace only) - autotitle: (share only, + optional: context)
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  • Returns the deployment artifacts for a perspective: the share_url and direct_url for outreach plus ready-to-paste embed snippets (fullpage, widget, popup, slider, float, card) and an SDK reference (script URL, events, URL/brand/theme params, JS API methods, callbacks). Behavior: - Read-only. - Errors when the perspective is not found or you do not have access. - URLs are stable per perspective. Conversations started from these embeds count toward the workspace's quota (preview conversations do not — see perspective_get_preview_link). - Use the snippet returned for that specific perspective rather than hand-rolling URLs. - share_url / direct_url accept these URL params: name, email, returnUrl, plus arbitrary tracking keys (source, campaign, etc.). When to use this tool: - Deploying a perspective to a real site, email, or app surface. - Generating SDK integration code (Next.js layout, raw HTML, popup trigger button, etc.). - Looking up event names or URL parameters the embed accepts. When NOT to use this tool: - Internal smoke testing — use perspective_get_preview_link (preview conversations don't count toward quota). - Inspecting outline / setup — use perspective_get. Typical flow: 1. perspective_create → design 2. perspective_get_preview_link → test 3. perspective_update → refine 4. perspective_get_embed_options → deploy 5. automation_create → (form / lead-capture contexts) wire conversation data to a CRM or backend For coding assistants: after returning embed options, help the user wire the snippet into their app: - Popup / Slider / Float: add the script before `</body>` in HTML, or in `_app.tsx` / `layout.tsx` for React/Next.js. - Widget: place the div where the widget should appear. - Fullpage: use in a dedicated page or iframe container. - Card: use as a preview link in landing pages or emails. For form / lead-capture contexts: after deployment, ask whether they want to set up an automation (automation_create) to forward each completed conversation to their CRM, database, or notification channel. Examples: - Optional URL params on the share link: `email` (pre-fills participant email), `returnUrl` (redirect after the conversation completes), and arbitrary `key=value` pairs for tracking (e.g. `source=email`, `campaign=q4-launch`, `user_id=...`). Embed snippets accept additional appearance params (brand colors, theme) — see the `sdk.parameters` section in the response. - Each perspective has unique URLs — always use the URL returned for that specific perspective.
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  • Returns Argentine national public holidays for any given year. Use this tool when calculating delivery dates, scheduling appointments, computing working days, or any task requiring knowledge of non-working days in Argentina. Returns all national holidays with dates in YYYY-MM-DD format and names in both Spanish and English. Note: Argentina also has bridge holidays (feriados puente) declared annually by the government which are not included here.
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  • Is it safe to deploy these changes? Cross-references your changed modules against active constraints, recent incidents, knowledge freshness, and active alerts. Returns a composite verdict (ready/caution/block) with per-module breakdown and actionable recommendations. Use BEFORE deploying to catch constraint violations, recent regressions in the same area, stale knowledge that needs verification, and active alerts that might interact with your changes.
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  • Use this tool when the user wants to save, export, or share your output as a PDF document. Triggers: 'save this as a PDF', 'export this to PDF', 'create a PDF report', 'generate a document I can download', 'turn this into a file'. Supports # headings, ## subheadings, - bullet lists, and plain paragraphs. Returns a base64-encoded PDF. Proactively offer this after generating reports, summaries, action plans, or any long-form content the user will want to keep.
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  • WHEN: generating a visual diagram of D365 table relationships or security chains. Triggers: 'generate diagram', 'diagramme', 'visualize', 'schéma', 'ER diagram', 'entity-relationship', 'relation diagram', 'security diagram', 'show connections'. Generate visual Mermaid diagrams from D365 F&O knowledge base data. Diagrams render directly in Copilot Chat, Cursor, Claude, and markdown viewers. Types: 'er' (entity-relationship diagram for a table and its relations), 'security' (security chain: Role->Duty->Privilege->EntryPoints -- use when you need a VISUAL Mermaid diagram; for the structured text chain with tables of duties/privileges/entry-points use `trace_security_chain` instead). Note: 'flow' (execution flowchart) is disabled -- static call trees are misleading in D365 due to CoC and event handlers.
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  • Get LLM instructions at the specified level. Call with level 'brain' early in conversations to learn user preferences. Required: level ('brain'|'personal_root'|'container'|'team'). Optional: id (integer, required for 'container' and 'team' levels). 'container' level returns the full inheritance chain (personal root -> ancestors -> container).
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  • Get report status and metadata. Returns status (pending/generating/completed/failed), title, type, and summary. When status='completed', download the PDF with atlas_download_report(report_id). report_id from atlas_start_report response or atlas_list_reports. Free.
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  • Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.
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