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237,513 tools. Last updated 2026-06-26 11:09

"namespace:io.github.groundlogic-ai-source" matching MCP tools:

  • Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.
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  • Conceptual / semantic passage search across the whole library. Use when the modern term won't literally appear in historical texts — e.g. "distributed cognition" maps to passages about active intellect, art of memory, wax tablet metaphors; "social contract" maps to pre-Hobbesian discussions of consent and authority. Ranks passages by cosine similarity on Gemini embeddings (768d), so paraphrases and conceptually adjacent phrasings match even when no keyword overlaps. ORIENTATION HINT: if the user named a specific author or work, prefer get_book (returns the book's AI summary + chapter outline) — semantic search is expensive and best reserved for cross-corpus discovery. Prefer search_translations for literal phrases or distinctive single terms; use search_concept when the concept matters more than the wording. Similarity calibration: 0.70+ is a strong match, 0.55–0.70 is worth reading but verify, below 0.55 is mostly conceptual drift. Set max_per_book to diversify results across many books rather than cluster on one source. Each passage carries a snippet_type — quote only "translation" snippets, never "summary". Cross-cultural tip: for pre-modern or non-Western topics, also try source-tradition vocabulary — e.g. for seminal economy try "jing preservation" or "bindu yoga" or "istimnāʾ"; for masturbation try "mollities" (Latin) or "hastamaithuna" (Sanskrit) or "shouyin" (Chinese). The corpus is indexed via period translations that use tradition-internal terminology, so adjacent/euphemistic terms often surface material that modern English keywords miss.
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  • Get the latest curated crypto news headlines. Returns real-time news items with headline, sentiment, categories, and sources. Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai'). Call get_categories first to see all available category codes. Args: category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai'). Omit to get news across all categories. limit: Number of items to return (1-10, default 5).
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
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  • Find specific PASSAGES inside books — returns page-level snippets with citation URLs. Use this when you want a quote or evidence on a topic across the whole library. ORIENTATION HINT: if the user has named a specific author or work, prefer get_book (returns a summary + chapter outline) over passage hunting — every book in the corpus has an AI-generated summary that is usually the right first read. Use search_translations when sweeping across many books for evidence of a theme. For finding which BOOKS cover a topic, use search_library. Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.
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Matching MCP Servers

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    Exposes structured Microsoft Dynamics 365 Business Central source code (versions 23-29, 47 localizations) to AI agents via MCP, enabling fast lookups, searches, and code analysis.
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    Enables AI agents to discover and access 800TB+ of public geospatial data from Source Cooperative, with tools for listing organizations, products, files, and fuzzy search.
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  • Scan URLs for WCAG 2.1 violations, generate AI fixes, and produce VPAT 2.5 compliance reports.

  • Read and write open-source flashcards and decks through split read/write MCP tools.

  • AI-analysed news for a stock, newest first. Only returns articles processed by our AI pipeline (sentiment, flag score, summary). - days: look-back window in days (default 30, max 30) - limit: max articles returned (default 10, max 10) - status: "ok" = articles returned | "empty" = no news in window - Per article: title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
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  • Confirm an AI call after reviewing push-back questions, optionally providing answers to missing info. Required when ai_call returns state='pending_confirm'. Uses the original payment — no new payment needed. Returns call_id for polling with check_job_status(jobType='ai-call').
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  • Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.
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  • Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.
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  • AI Voice Generator — Convert text to natural-sounding speech using AI — 6 voices in English and Spanish, with engine tiers for cleaner studio-grade output.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • AI-screened stock ideas actively flagged by the Stocklake pipeline. These are stocks the pipeline's AI agents have identified as worth attention — sourced from news analysis, sector screening, and sentiment signals. Parameters: - direction: "LONG" | "SHORT" | "BOTH" (default: all) - min_conviction: minimum conviction score 0-10 (default 7) - min_flag_score: minimum flag score 0-10 (default 8; 9+ = high conviction) - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50) Returns: - count: number of ideas returned - ideas[]: each with symbol, direction, conviction (0-10), confidence (0-10), flag_score (0-10), source, rationale, expires - Note: ideas expire daily — active ideas represent the pipeline's current view. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
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  • Get commentary (Persian + English) for a specific beyt of the Masnavi, attributed to a primary source (e.g. Abdolkarim Soroush's lectures). Returns the beyt text along with structured commentary entries. Each entry has source, author, language, body markdown, and confidence ('ai-draft' / 'reviewed' / 'verbatim'). Use this when a user asks 'what does this beyt mean' or 'what does <scholar> say about M1:1'.
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  • Generate an AI image and place it directly on a user's Avocado AI flow (the Flows Director). Drops a 'Generating...' tile on the flow immediately, then swaps it for the final image when generation completes (10-60s). It appears live on the open canvas and in the Director Library, grouped by role. Costs match generate_image (1-4 credits per image depending on model and quality).
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  • Send a message to CeeVee AI assistant for CV optimization guidance (2 credits). Requires a cv_version_id (use ceevee_upload_cv or ceevee_list_versions to get one). Returns AI response with optional edit suggestions, source citations, and a conversation_id. Omit conversation_id to start a new conversation; include it to continue a thread.
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  • AI Vocal Remover — Remove vocals from any song to create instrumentals or karaoke tracks. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • Use when assessing a SaaS category investment thesis, competitive dynamics, or market momentum before a strategic decision. Returns growth signal, AI citation leaders, and disruption risk for any software category. Example: CRM category — GROWING signal, Salesforce leads at 42% citation share, HubSpot gaining 8% share year-over-year, disruption risk MODERATE from AI-native CRMs — signals consolidation pressure on mid-tier vendors. Source: Stratalize market intelligence composite.
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  • Fetches the top 15 trending HuggingFace models sorted by likes in the last 7 days. Each item includes id (author/name), likes, downloads, pipeline tag, and url. Source: huggingface.co/api/models. Cache TTL 10min. Use when the agent needs to surface what the open-source AI community is paying attention to right now.
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  • Use when assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing. Returns a composite payload with framework, deadline, total_controls, controls[], hint, and query timestamp, optionally filtered by NIST function from compliance_controls reference data. Example: Filter by MAP to review mapped EU AI Act controls and implementation statuses in the returned controls array for governance planning. Source: EU AI Act mappings in compliance_controls reference data.
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