"Research assistant for AI and ML papers, code, and methodology" matching MCP tools:

• search_papers

  Paperswithcode

  Search machine-learning / AI research papers (via Hugging Face Papers, the successor to Papers with Code). Returns arXiv id, title, authors, community upvotes, and a linked GitHub repo when available. Use for "papers on <topic>", "recent ML research about X".

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• trending_papers

  Paperswithcode

  Today's trending ML/AI papers (or a given day's), ranked by community upvotes, via Hugging Face Papers. Use for "what are the hot AI papers", "trending ML research", "top papers this week".

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• search_arxiv_ai

  AgentAI

  Search arXiv for AI/ML papers by keyword and category. Returns recent submissions sorted by date.

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• get_ai_papers

  AgentAI

  Get trending or searched AI/ML research papers from HuggingFace Papers. Returns trending papers for a given date or search results by keyword.

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• lookup_arxiv

  GoCreative Agent API

  Search arXiv for recent papers matching a query (title, authors, abstract, PDF link). Use for ML/AI research agents and literature review. Example call: {"query": "diffusion transformer"} Cost: $0.005–$0.05 USDC on Base per call.

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• get_ai_papers

  AI/ML Research Papers

  Get trending or searched AI/ML research papers from HuggingFace Papers. Returns trending papers for a given date or search results by keyword.

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• search_arxiv_ai

  AI/ML Research Papers

  Search arXiv for AI/ML papers by keyword and category. Returns recent submissions sorted by date.

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• ai_code_smell_scan

  Moltline Code Review

  Flag the tells of unreviewed AI-generated code in a source file. FREE. Detects comments that restate the next line, leaked assistant preambles, placeholder TODOs, shipped 'Example usage' blocks, over-broad try/except that swallows errors, and auto-named identifiers. Typical input {"code": "<file contents>"} returns {"reviewed_confidence": 0-100, "hits": [{"smell": "...", "evidence": "<quoted snippet>"}], "reading": "...", "note": "..."}. Use on a full source file suspected of unreviewed machine authorship. Not on a diff (review_diff), and the result is a signal to check, not proof of authorship. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

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• cti_get_guidelines

  website-search

  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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• list_skills

  civilquants

  List the 10 senior-QS skill methodologies CivilQuants exposes (tender review, risk assessment, QS measurement/contract advice, geotechnical + geo-environmental interpretation, earthworks, preliminaries, pavement design, subcontract analysis). Universal discovery — both tiers see the full list. Returns each skill's slug, title, one-line summary and tier; then call get_skill(skill=<slug>) to fetch the methodology body. The skills are paid-tier; a free caller gets a sign-up prompt from get_skill. NOTE: the document-heavy skills (tender review, the interpretation skills) need a code-execution client (Claude Code / Codex / VS Code) plus the chunking pack from get_document_pipeline to run a real tender pack — on a chat connector you can read the methodology but cannot chunk/parse files.

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• find_methodology

  OpenArx

  Find methodology approaches for a specific research task. Returns structured method-level results (not raw chunks): method name, key idea, dataset used, performance metric. Filters by task domain, dataset, metric. Built on LLM-classified contentType=methodology chunks combined with benchmark results JOIN. Use this instead of `search` when you want HOW researchers approach a problem rather than 10 papers about it. Note: surfaces any chunk classified as methodology, including ones where the task is mentioned only as a toy example. Filter by category (e.g. cs.CV for image tasks) to narrow scope. This searches EXISTING papers for methods others have published (literature search) — it is NOT a guide for conducting your own research: for a step-by-step scientific method tailored to your own research question, start with the `methodist` door.

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• get_research_papers

  TensorFeed

  Get recent AI/ML research papers from one of three feeds, chosen with the source argument. arxiv_recent is the firehose: newest arXiv submissions in cs.AI / cs.LG / cs.CL / cs.CV by submission date, refreshed daily at 11:30 UTC. trending is citation-ranked from Semantic Scholar across five fan-out queries, deduped, refreshed daily at 11:00 UTC. hf_daily is Hugging Face editor-curated with community upvotes and discussion counts, refreshed daily at 14:15 UTC. Pick arxiv_recent for what is brand new, trending for what is influential, hf_daily for what practitioners are discussing. License: arXiv and Semantic Scholar permit metadata use; the standard attribution block ships on every response.

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• get_track_record

  voxodds

  Get VoxOdds' audited AI-vs-market forecast track record. Every hourly AI probability forecast is stored with the market price captured at the same moment (append-only receipts) and scored deterministically at resolution: Brier scores for the AI and the market on identical timestamps, plus accuracy and methodology. Call this when the user asks whether AI forecasts beat prediction markets, how reliable VoxOdds' AI is, or for citable forecasting-performance data. Losses are published too — the record is auditable, not curated.

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• getPaperDetails

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Get full details for a specific quantum computing paper by its arXiv ID (e.g., "2401.12345"). Use after searchPapers or getLatestPapers when the user wants to dive deep into a specific paper. Returns: complete abstract, all authors, publication date, AI-generated tags with reasons, hook (one-line summary), methodology, gist, and key findings. Requires a valid paper_id from search results. Returns error if not found.

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• assessment_get_guidelines

  website-search

  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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• get_ai_traffic

  RevenueScope: revenue-first analytics for your EC site

  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 (last_cited_at is JST ISO8601 with a +09:00 offset — the same basis as the dashboard, so dates line up when compared) (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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• get_ml_trends

  SnowSure — Snow & Ski

  Fetch SnowSure-unique ML/AI trend datasets from the public REST API. Use for powder-day leaders, bluebird-day leaders, bluebird predictions, improving/stable/declining score pulse, per-model accuracy weights, daily SnowSure score component history, ML extended outlook (days 8–14), global forecast trust, and powder/bluebird event logs. Start with dataset=catalog. Its leaderboards read CURRENT-season counters and are global — they take no season and no country/state filter. For a past season, or for any ranking scoped to a state, province, country or region ("most snow days in Maine last season", "rank BC resorts by season snowfall"), use get_season_leaderboard instead. Prefer get_insights for narrative intelligence cards; use this for raw rankings and time series.

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• ai_code_smell_scan

  Moltline Code Review & AI-Code Verifier

  Flag the tells of unreviewed AI-generated code in a source file. FREE. Detects comments that restate the next line, leaked assistant preambles, placeholder TODOs, shipped 'Example usage' blocks, over-broad try/except that swallows errors, and auto-named identifiers. Typical input {"code": "<file contents>"} returns {"reviewed_confidence": 0-100, "hits": [{"smell": "...", "evidence": "<quoted snippet>"}], "reading": "...", "note": "..."}. Use on a full source file suspected of unreviewed machine authorship. Not on a diff (review_diff), and the result is a signal to check, not proof of authorship. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• get_provenance

  Hesper Atlas Evidence

  Use when a user asks where a number came from, whether two results used the same code, or what first-party hashes can and cannot prove. Returns the authoritative public methodology version, content-derived published-decision and evaluation-pipeline calculation versions, component SHA-256 hashes, canonicalization rule and attestation limitations. Use it to determine whether two figures came from the same rules/code. It explicitly states that no independent timestamp authority or third-party signer is configured.

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