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606,818 tools. Updated 2026-09-24 11:36

"Resources and guidance for coding, developing, and training AI models" matching MCP tools:

  • List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Re-trigger background semantic training for one dataset and return immediately with status and a confirmation message — the build runs asynchronously, so poll get_dataset_status until model_tier reaches 'schema'. Use after schema changes, alias updates, or to force a fresh model build. epochs (default 80, range 5-500) controls training length. An unknown dataset_id fails with not_found; a dataset whose training backend is unavailable fails with semantic_training_unavailable.
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  • The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.
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  • Get Lenny Zeltser's expert writing guidelines for security reports and assessments. Provides guidance on tone, structure, clarity, executive summaries, and avoiding common writing mistakes. Includes rating-sheet items (the four lens sheets: structure, look, words, tone) as concrete reference points for grounded feedback. Works for any security document. This server never requests your documents and instructs your AI to keep them local—guidelines flow to your AI for local analysis. Note: For incident response reports specifically, use the ir_* tools which provide deeper section-by-section review criteria.
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  • Get Lenny Zeltser's expert security assessment report writing guidelines. Topics: severity (the risk-adjusted severity model — the spine), findings, remediation, methodology, scope, strengths, brief (one-page brief section guidance), executive_summary, analysis, anti_patterns, frameworks, handoffs, and summary. The general 'tone' topic defers to `get_security_writing_guidelines` for the canonical Five Elements rules. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.
    AGPL 3.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.
    1
    GPL 3.0

Matching MCP Connectors

  • Rick and Morty MCP — wraps the Rick and Morty API (free, no auth)

  • NICE guidance MCP — the National Institute for Health and Care Excellence's

  • Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.
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  • List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.
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  • List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.
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  • List the AI image, video, music, and sound-effect models available on BudgetPixel with base credit prices and capabilities. Featured models come first with a one-line role hint (when to pick each). Video models are priced per SECOND by resolution; music models are flat per track; sound effects are per second with a 3-second minimum. Prices are base rates — the user's plan discounts and free-model perks apply automatically when generating.
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  • Get remediation advice for a single finding as GitHub-flavored markdown. When AI Assist is enabled and within budget this is a suggestion written for this exact finding; otherwise it falls back to the static guidance-library text and says so in 'source'. Unlike the other reads this one can spend AI budget, which is why it is a separate tool. Use this for advice on one finding; for the whole library use list_security_guidance. Requires project context.
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  • Run a query against AI models (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) and return each answer plus the sources it cited. Covers AI-search, citation, SEO, keyword, trend, and market-research data. If a plan needs more than 20 endpoint calls or may use more than $5 USD, estimate the cost in USD and ask the user before continuing. Quote Cite42 costs and balances in USD using cite42Billing or the USD pricing table. Legacy credit fields are for compatibility only. These funds belong to Cite42, not the host or model provider. AI visibility metrics include counts; quote them with percentages, not as a market-wide score. Optional recommendations are a separate paid call, never automatic. After a visibility result, offer recommendations using its requestId; run only when the user requests advice and knows the additional cost. Free: cite42_balance for the remaining balance, cite42_pricing for exact per-tool prices and cost estimates, cite42_usage for recent calls, spend, and limits. Pricing is per selected AI surface. Omitting models queries the three default surfaces (chatgpt, perplexity, gemini); fullSweep: true queries all five, adding claude and google_ai_overview at a higher cost. Use the models field when the user names a surface or asks to reduce spending, and call cite42_pricing for exact current rates.
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  • Get Lenny Zeltser's expert malware analysis report writing guidelines. Topics include capabilities, confidence, pyramid_of_pain, anti_patterns, methodology, fields, handoffs, frameworks, plus tone, words, structure, and executive_summary topics that defer to `get_security_writing_guidelines` for canonical Five Elements guidance. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks. Six frontier models (OpenAI, Anthropic, Gemini, xAI, DeepSeek, Perplexity) research each covered stock independently. Every model returns its own 0-100 score, rating (outperform / neutral / underperform), price target, and a weighted factor breakdown across financial performance, technical analysis, valuation, earnings call and corporate events. Use for: "what's the AI score for NVDA", "AI rating on my watchlist", "compare the AI scores of AAPL, MSFT and NVDA", "why do the models disagree on Tesla". Pass every symbol in one call — a multi-ticker call returns one compact row per ticker, which is what a watchlist or ranking question needs. A single ticker also returns every model's score with its factor breakdown plus the bull and bear key points. This is NOT the Smart Score (1-10, eight quantitative factors). It is a separate system, and the two routinely disagree by design. `ai_score` is the headline score and matches the AI Stock Analysis page; `consensus` holds the cross-model average, the high and low scoring models, and the split of rating labels. `upside_pct` is the model's price target against the current price. `as_of` is when the report was generated — reports regenerate on new earnings or a significant price move, so an older date means nothing material has changed since. Coverage is a subset of the stock universe and excludes ETFs. Symbols with no report at all come back under `not_covered`; symbols that are covered but lack a report from the requested `provider` come back separately under `no_report_from_provider`, each listing the models that did score them — so a missing provider is never reported as "this stock has no AI analysis". Args: tickers: Comma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT'), max 25. provider: Optional single provider to report on. Omit for the headline score that matches the website. detail: 'consensus' (default) or 'full' to add each model's written reasoning. Ignored on multi-ticker calls. Returns JSON: {stocks: [{ticker, company, ai_score, rating, headline_model, price, price_target, upside_pct, as_of, reflects, consensus: {models, avg_score, score_high, score_low, ratings_split, avg_price_target, avg_upside_pct, reports_dated}, providers: [...], key_points: [...]}], not_covered: [...], no_report_from_provider: [{ticker, covered_by}]}. `consensus.reports_dated` appears only when the models did not all run on the same date; `as_of` is always the headline report's own date.
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  • Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status
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  • Use this when the user supplies source code and asks for UML, Mermaid, sequence, class, component, or architecture diagrams. State-changing generation action: consumes AI credits, creates a generation, and optionally writes markdown to a Space after authorization. Use browser guidance for local uploads or full repository diagrams.
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  • Find which sources AI models cite for a query; optionally check whether a specific URL is cited. Covers AI-search, citation, SEO, keyword, trend, and market-research data. If a plan needs more than 20 endpoint calls or may use more than $5 USD, estimate the cost in USD and ask the user before continuing. Quote Cite42 costs and balances in USD using cite42Billing or the USD pricing table. Legacy credit fields are for compatibility only. These funds belong to Cite42, not the host or model provider. AI visibility metrics include counts; quote them with percentages, not as a market-wide score. Optional recommendations are a separate paid call, never automatic. After a visibility result, offer recommendations using its requestId; run only when the user requests advice and knows the additional cost. Free: cite42_balance for the remaining balance, cite42_pricing for exact per-tool prices and cost estimates, cite42_usage for recent calls, spend, and limits. Pricing is per selected AI surface. Omitting models queries the three default surfaces (chatgpt, perplexity, gemini); fullSweep: true queries all five, adding claude and google_ai_overview at a higher cost. Use the models field when the user names a surface or asks to reduce spending, and call cite42_pricing for exact current rates.
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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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  • List the AI models selectable for visibility runs, per platform, with prices. Returns every platform's options (id, label, default flag, tier, credits per probe) plus a roles table mapping default / latest / prior to a concrete model id for every platform. Use the ids or role keywords in the models map or in probes[].model when creating a run. Read-only and free.
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  • Optional helper before plan_create. Returns model_profile options with plain-language guidance and currently available models in each profile. If no models are available, returns error code MODEL_PROFILES_UNAVAILABLE.
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