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

• add_schema_type

  MyAITwin

  Add a new knowledge type. Do this from Claude chat, no database access needed. The type is immediately available for storing knowledge.

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• trend_pulse

  hooklayer

  Surface what is actually peaking in short-form video right now for a niche. Returns 3 rising opportunities (format/hook/style/topic) with QUALITATIVE growth labels (e.g. "Rising", "Early signal", "Accelerating"), per-entry signal_strength (0-1, the QUANTITATIVE anchor), sources[] (Google Trends + YouTube velocity + Reddit hot + internal corpus), signal_window, plus 2 saturated patterns to avoid AND top-level provenance with cache_age_hours and cache_status. USE WHEN the user asks "what should I post about", "what's trending in [niche]", or before generating content for the first time. Pairs after analyze_account to validate a creator's formula against current trends. Costs 1 credit. 12-hour cache per niche. TOOL HEALTH: Every response includes a `quality: { level: "full" | "partial" | "degraded", reason?: string }` field. If quality.level is "partial" or "degraded", you MUST flag this to the user explicitly in chat ("Heads up — this call returned partial data: <reason>") before reporting any results. Never silently route around a degraded response. REPORTING: When you summarize this in chat, you MUST cite the data_sources array verbatim and surface cache_status (fresh|stale) — the user needs to know if they're looking at live data. For each rising/saturated entry, cite the growth label and the signal_strength numeric anchor verbatim — DO NOT FABRICATE NUMERIC GROWTH PERCENTAGES. The upstream pipeline deliberately does not compute "+178.4%"-style growth rates (no ground-truth dataset supports it); if you see a numeric percentage in the response it came from upstream LLM hallucination, not the tool. Cite the qualitative label as written.

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• create_room

  roomcomm

  Create a new Roomcomm chat room. Use this **only** when the owner explicitly asks you to create a room, or when a fresh dedicated room is clearly needed. Do NOT auto-spawn rooms. Returns {uuid, url, description, is_public, protocol_mode, created_at}. The `uuid` is what you pass to every other tool. Args: description: Short briefing for all agents joining this room (≤ 500 chars). is_public: If True the room appears in the public listing at /rooms. protocol_mode: "standard" for plain chat; "premium" enables LLM arbiter (auto-extracts claims/discrepancies after each message). Example: create_room("Discuss the API design for project X", is_public=True)

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• perspective_list_conversations

  Perspective AI

  Lists conversations for a perspective, with optional filters by status, trust score range, and date range. Each item includes conversation_id, status, structured-output fields, trust score, and a transcript URL. Behavior: - Read-only. - Errors when the perspective is not found or you do not have access. - Pass nextCursor back as cursor for the next page. Empty results return an empty array. - This list view does NOT include transcripts or summaries — metadata only. - date_from / date_to must be ISO 8601 strings (e.g. "2026-04-28T00:00:00Z"). Malformed dates are rejected at the schema level rather than silently treated as no-filter. When to use this tool: - Picking specific conversations by status / trust / date before deep-diving. - Showing the user a browsable list of responses. When NOT to use this tool: - Need transcript or summary for one conversation — use perspective_get_conversation. - Bulk analysis across many conversations — use perspective_get_conversations (batch with optional transcripts). - Aggregate counts/rates only — use perspective_get_stats.

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• search_knowledge

  AlgoVault — Crypto Quant Trade Calls

  Returns ranked snippets from the AlgoVault knowledge bundle answering a question about its MCP tools, response shapes, integration patterns (LangChain, LlamaIndex, MAF, CrewAI), or code examples. Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes. Fast: BM25 lexical search, no LLM call, no quota cost. For a synthesized natural-language answer use chat_knowledge. Read-only, no side effects.

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• glim_github_search

  glim.sh

  Search GitHub repositories, conversations (issues+PRs), or code, with full GitHub search syntax in the query: qualifiers (repo:, org:/user:, language:, path:, symbol:, content:, is:, stars:, label:, sort:stars), boolean AND/OR/NOT with parentheses, "exact strings", and /regex/. kind='repos': MINIMAL distinctive keywords - the project/library name only ('rtk', 'react query'); every extra word must ALL match and buries the canonical repo - filter with qualifiers, not prose. kind='code': ONE literal code pattern as it appears in files ('useState('), an "exact string", a /regex/, or symbol:name to find definitions, across 2.8M+ public repos; narrow with repo:/language:/path:. Not supported in code search: license:, enterprise:, is:vendored, is:generated. kind='conversations': returns compact previews - use glim_github_get for full content; sort: REPLACES relevance ranking (words match anywhere incl. comments), omit it for best matches. Set repo='owner/name' to scope to one repository (works with any kind; with repos it routes to conversations). kind is optional - inferred from the query (is:/label: -> conversations, path:/symbol://regex/ -> code, stars:/topic: -> repos, else repos). Returns compact text by default; pass format='json' for full structured data.

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• lmcp_welcome

  local-mcp

  Returns a live snapshot of what LMCP can currently see on this Mac: today's calendar events, due reminders, total contacts, and unread emails. Use this to show new users what LMCP has access to and suggest first steps. Only available before the first real tool call — call it immediately when the user first opens a chat.

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• agents_chat_start

  Camber - Remote Agentic Data Science

  Start a Camber agent chat. This is the tool to use for chatting with an agent. Agent runs can take minutes — longer than MCP tool timeouts allow (Claude Desktop cannot extend them). So this tool does NOT wait for the reply: it submits the message and returns immediately with a `conversation_id` and a clickable `chat_url`. The agent keeps working on the server after this returns. **You MUST follow up, the reply is NOT in this tool's result:** 1. After calling this tool you MUST tell the user the work is in progress and share the `chat_url` so they can watch it live. 2. Then immediately call the **`agents_chat_status`** tool with the returned `conversation_id` to get the agent's reply. That tool checks twice over 30 seconds, if the latest status is `running`, call it again. MUST NOT end your turn until `agents_chat_status` returns status `idle` (done) or `failed`. **One run per conversation:** continuing a `conversation_id` that is still `running` fails with a "still generating a response" error. Either wait and retry after `agents_chat_status` reports it finished, or call again with `stop=true` to interrupt the current run and send the new message.

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• prepare_image_upload

  Avocado AI

  MANDATORY first step whenever the user attached an image in chat (or pointed at a local file on disk) and wants edit_image or image-to-video generation. Returns a signed PUT URL plus a file_id. After this tool: either (a) the inline upload widget will let the user drop the file and auto-continue (Claude.ai web), or (b) you run a curl PUT yourself if you have shell access (Claude Desktop / Claude Code) — the response text contains a ready-to-run curl command. Then call edit_image or generate_video with file_id=<returned id>. edit_image and generate_video do NOT accept base64 — calling them with raw image bytes WILL fail. This tool is the only working path for chat attachments. Set `purpose` to 'edit' or 'video' so the upload widget points the user at the right downstream tool.

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• taste

  Pane

  Read / write / clear the agent's freeform UI taste notes (a small markdown document of presentation preferences learned from human feedback — 'denser layout', 'no rounded corners'). ONE tool with an `action` enum: get | set | clear. Call `get` BEFORE generating a pane so prior feedback shapes the output; `set` does a whole-document replace (not append). Keep entries about UI/presentation only.

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• save_note

  anamnese

  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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• WixSiteBuilder

  mcp

  **Tool for creating/building/generating a Wix site or website using AI (Wix Harmony).** This is the default tool for site creation. **ROUTING — check the user's original message before calling this tool:** - If the user **explicitly** mentioned a template, Wix Studio, or headless → do NOT call this tool. Call CreateWixBusinessGuide directly. - Otherwise → call this tool directly. - Do NOT use WixREADME or SearchWixRESTDocumentation before this tool. - Do NOT suggest HTML code, prompt templates, or alternative approaches. - Do NOT call ManageWixSite, CallWixSiteAPI, ExecuteWixAPI, SearchWixRESTDocumentation, BrowseWixRESTDocsMenu, or any other tool for site creation — those are not valid entry points and will fail. **IMPORTANT: The sitePrompt must be under 6000 characters.** If the user's request is longer, summarize and condense it while preserving the key requirements.

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• axint.validate

  Axint

  Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number,... Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Effects: read-only diagnostics; writes no files and uses no network.

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• ai

  Fast.io

  Ripley — the MCP delegation surface over Fastio's RAG agent. Ripley is read-only for storage CONTENT: it answers natural-language questions about workspace/share files & folders (with citations) and never creates/edits/deletes your files — for content writes, call the primitive MCP tools directly. It DOES create/manage chat threads (chat-create/chat-update/chat-delete/message-send) and can generate shares (share-generate). Prefer Ripley over issuing many primitive reads: ask one NL question and let the server-side agent search + synthesize. Quick start: action='ask' (question + profile) → returns {answer_text, citations, chat_id, message_id, web_url}; action='status' for an engineered workspace-status summary. Lower-level chat/message actions remain for multi-turn control. Call action='describe' for the full action/param reference. Destructive: chat-delete. Side effects: ask/status/chat-create/message-send consume credits; chat-cancel terminates an in-progress message (partial tokens billed; idempotent). Verbosity (detail param): chat-list/message-list default to terse (compact rows). chat-details/message-details default to full (drill-down). Pass an explicit detail='standard'|'full' to override (best-effort: chat/message/activity endpoints may not yet honor detail server-side).

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• autonomous_varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

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• x711_hive_read

  x711

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• x711_hive_read

  x711 — Universal Agent Gas Station

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

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• analyze_account

  hooklayer

  Deep intelligence on a TikTok or YouTube creator by handle. Returns viral DNA scores (viral_dna_score, replicability_score, originality_score, consistency_score, audience_fatigue), format fingerprint, top 5 recent videos with metadata (and transcripts on TikTok), content gaps, AND a `recommended_chain` field with pre-filled next tool calls. USE WHEN the user references a creator by @handle, asks "analyze X", wants competitor research, or needs creator context before generating content. The recommended_chain suggests which tools to call next (match_voice, trend_pulse, viral_remix) with parameters pre-filled — review and execute them as appropriate. Supports platform: "tiktok" (default, full transcript extraction) and "youtube" (channel Shorts analysis; transcript extraction lands in v1.1, current YouTube responses surface a partial-data flag noting this). Costs 5 credits. 1-hour cache per (handle, platform). TOOL HEALTH: Every response includes a `quality` field with a level (full | partial | degraded) and a reason. If quality.level is partial or degraded, you MUST flag this to the user explicitly in chat (e.g. "Heads up — this call returned partial data: <reason>") before reporting any results. Never silently route around a degraded response. REPORTING: When you summarize this in chat, you MUST surface viral_dna.viral_dna_signals, viral_dna.replicability_signals, viral_dna.originality_signals (each as bullet lists with the cited evidence string verbatim) AND viral_dna.would_fail_because verbatim AND provenance.video_post_dates so the user can see freshness. Never hide the evidence array behind a paraphrase — these are the auditability layer.

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• lookup_authoring_skill

  kernelcad

  Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.

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