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741,331 tools. Updated 2026-11-04 03:22

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

  • 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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  • 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 for AI/ML papers by keyword and category. Returns recent submissions sorted by date.
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  • 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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  • 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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    Enables asking questions about foundational AI/ML research papers, with retrieval-augmented generation and an agentic layer for complex queries, via MCP-compatible clients.
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Matching MCP Connectors

  • Live cs.AI/ML/CL/CV/RO paper graph. FTS5 + resolved ArXiv citation links. Use for: arxiv_search_papers("LoRA fine-tuning", has_code_only=True) → Related verticals worth connecting: AI News (mcp.brunosan.de/mcp) — industry reaction to papers Robotics (robotics.mcp.brunosan.de/mcp) — applied robotics papers (cs.RO) Quantum (quantum.mcp.brunosan.de/mcp) — quant-ph research depth Biotech (biotech.mcp.brunosan.de/mcp) — bio-ML and drug discovery papers
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  • Live cs.AI/ML/CL/CV/RO paper graph. FTS5 + resolved ArXiv citation links. Use for: arxiv_search_papers("LoRA fine-tuning", has_code_only=True) → Related verticals worth connecting: AI News (mcp.brunosan.de/mcp) — industry reaction to papers Robotics (robotics.mcp.brunosan.de/mcp) — applied robotics papers (cs.RO) Quantum (quantum.mcp.brunosan.de/mcp) — quant-ph research depth Biotech (biotech.mcp.brunosan.de/mcp) — bio-ML and drug discovery papers
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  • Look up a lender's underwriting methodology by lenderId. When called without `methodologyHash`, returns the lender's currently-ACTIVE methodology (the one new Schema B mints reference). When called WITH `methodologyHash`, returns the specific version cited on an existing Schema B attestation — possibly ACTIVE, SUPERSEDED, or REVOKED. Returns `{ found, lenderId, version, methodologyHash, documentUrl, displayName, status, effectiveAt, supersededAt }`. Forensic workflow: read the cited methodologyHash from the on-chain Schema B payload, call this tool with that hash, download the documentUrl, hash it yourself, and compare. Any divergence flags methodology tampering. Pair with verify_lender + verify_credit_risk for the full forensic chain. Graceful clean-slate envelope on missing / backend error.
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  • Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing. Free, deterministic, rule-based check on pasted listing copy (title, bullets, description). Returns a compliance health score, an AI-readability score, and a combined AI Recommendation Readiness Score (compliance * 0.55 + readability * 0.45) with actionable suggestions. Use this as a fast baseline BEFORE generating or editing a listing. Do NOT use it for a full compliance report - use compliance_scan for the deep knowledge-base audit. Free, read-only, no API key required, no credits deducted. Args: text: raw listing title + bullets + description (required). marketplace: marketplace code, US/DE/ES/FR/IT/JP/AE/SA/UK (default US). lang: zh or en (default en). email: optional lead email for a confirmation message and lead capture.
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  • The merge gate for ANY AI-written output — code, documentation, reports, emails, configs: returns a binary approve/reject verdict with veto power — e.g. it rejects code calling the nonexistent stripe.Inventory API, an N+1 loop with the latency arithmetic to prove it, or a doc claiming success with no evidence. Deterministic reality gates (mock/placeholder veto, gaming and bypass detection, invented-API checks) run first and cannot be overridden; a frontier-model review (ISO/IEC 25010) then scores quality, accuracy, reliability and tech debt. In a benchmark, a frontier model reviewing alone caught reward-gaming and hallucinated APIs 0/6 times in a natural review workflow; these gates catch them deterministically on every call. Read-only: nothing is executed. Call it on every AI-generated deliverable before accepting it; use validate_plan for plans, analyze_code for an advisory report without a verdict.
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  • Search the MITRE ATLAS catalog of AI/ML attack techniques by keyword, tactic, or maturity. Default response is SLIM (description truncated to 240 chars per row); pass include='full' for the verbose record. Pass exclude_id when chaining from atlas_technique_lookup to skip self in sibling-tactic searches. Use this to discover techniques matching a threat-model question, e.g. 'what techniques target LLM serving infrastructure?'. Drill into atlas_technique_lookup with any returned technique_id for the full description, ATT&CK bridge, and pivot hints. For broader cross-referencing: when a result has attack_reference_id, that bridges to D3FEND mitigations via d3fend_defense_for_attack. Free: 30/hr, Pro: 500/hr. Returns {query (echoed filters), total, results [{technique_id, name, description (truncated by default), tactics, inherited_tactics, maturity, attack_reference_id, subtechnique_of}], next_calls}.
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  • Look up a MITRE ATLAS case study — a documented real-world AI/ML attack incident. Each case study links a sequence of ATLAS techniques (techniques_used) to the incident. Default response is SLIM (description truncated to 240 chars); pass include='full' for the verbose narrative. Use this after atlas_technique_search to find which incidents have exercised a given technique. Drill into the full techniques_used array via bulk_atlas_technique_lookup in a single call (next_calls emits exactly that hint). Returns 404 when the id is not in the synced catalog. Free: 30/hr, Pro: 500/hr. Returns {case_study_id, name, description, techniques_used, next_calls}.
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".
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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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  • 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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  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • [pqc_signature] 使用 ML-DSA 私钥对消息签名(FIPS 204)。 【双模式】sign_mode 支持 RAW(默认)和 EXTERNAL_MU 两种模式。 - RAW 模式:直接签名原始消息(liboqs,最大 256 字节) - EXTERNAL_MU 模式:先计算 mu = SHAKE-256(tr||M', 64),再通过 OpenSSL 3.5+ mu 模式签名(最大 2048 字节),与 AWS KMS ML-DSA EXTERNAL_MU 语义等价 【消息长度策略】 - RAW:最大 256 字节,空消息合法 - EXTERNAL_MU:最大 2048 字节,空消息合法 - > 256 且 <= 2048 字节:使用 EXTERNAL_MU - > 2048 字节:拒绝 【算法】algorithm 支持 ML-DSA-44 / ML-DSA-65(默认)/ ML-DSA-87。 【参数】 - private_key_in_hex:ml_dsa_keygen 返回的私钥 hex - public_key_spki_in_hex:EXTERNAL_MU 模式必填,ml_dsa_keygen 返回的公钥 hex(SPKI DER) - message_in_hex:待签消息 hex - context_in_hex:可选上下文 hex(最大 255 字节) - sign_mode:RAW 或 EXTERNAL_MU(默认 RAW) 【输出】signature_in_hex、signature_in_base64、algorithm、message_bytes、signature_bytes。
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