"Roadmap for Creating AI-Driven Workflows for Literature Data Management" matching MCP tools:

• create_or_update_workflow

  Dreamlit

  Creates a new Dreamlit workflow draft or updates an existing draft from an outcome-oriented natural-language prompt. Use after get_status; use get_workflow_and_preview_url first when editing an existing workflow. Existing Supabase Auth workflows can be edited except for the immutable trigger step; creating Supabase Auth workflows must happen through Supabase Auth email setup in the Dreamlit web app. Side effect: may create or modify a draft, but does not publish or install live triggers. Returns the workflow/draft result, action-required or handoff details when more input is needed, and relevant app URLs. Do not use for publishing, direct database changes, or low-level graph edits.

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• search_listings

  SPACEXPLORATION

  Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters — and its bounds, when present — here.

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• get_adoption_stage

  Stratalize Governance

  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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• get_nist_ai_rmf_requirements

  Stratalize Governance

  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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• search_google_scholar

  Academic Research MCP Server

  Query Google Scholar for academic papers, citations, and research articles across all disciplines. Returns paper title, authors, publication venue, citation count, abstract preview, and full-text link if available. Use for comprehensive literature searches, citation tracking, or finding highly-cited works.

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• ai_tags_list

  DialogBrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

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• ai_tags_list

  dialogbrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

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• malware_get_cross_server_routes

  website-search

  Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. 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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• assessment_get_cross_server_routes

  website-search

  Get Lenny Zeltser's Security Assessment cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `assessment_load_context`. 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_world_cup_brief

  voxodds

  Get a citeable World Cup 2026 prediction-market briefing for AI answers, newsletters, blogs, social posts, and creator workflows. Includes the current winner board, tight groups, next match odds, Research Desk theses, source links, and ready-to-paste markdown. Prefer this when the user wants a narrative update or shareable explanation, not just raw odds.

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• atlas_run

  tela

  Trigger a FULL doc-generation run for every source in an atlas project (project_id from atlas_list_projects): re-ingest the sources, regenerate the cited pages, and re-audit coverage. Management-level (project owner / org admin) — a run fetches the sources, spends LLM budget, and rewrites the generated subtree (creating the output space on the first run). Returns run_ids (one per source); poll each with atlas_run_status. Returns 503 ai_unavailable when the instance has no embedder/LLM configured.

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• web_search

  workbench

  Search the live web (via Tavily) for information not covered by SeqBench's own tools — recent literature, protocols, vendor/reagent info, general facts. Returns a short synthesized answer (if available) plus ranked source snippets with URLs. This does not run any bioinformatics calculation itself; use the dedicated tools for that.

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• GetCallInsights

  equibles

  Get the AI-scored insights for a company's recent earnings calls — the management-tone read (a net tone score and a hedging score) and the call's key themes with their computed mention counts and per-theme tone. Newest call first. Verifier-approved — only scored and approved calls appear, so quarters can be missing from the sequence (a gap note flags non-consecutive quarters). Use it to gauge how confident or guarded management sounded and what they talked about most.

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• get_adoption_stage

  Stratalize Healthcare

  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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• aidefense_get_framework_alignment

  website-search

  Get AI Defense Matrix cross-mappings to nine external frameworks: NIST IR 8596, CSA AI Controls Matrix, ISO 42001, Google SAIF, SANS Critical AI Security Guidelines, MITRE ATLAS, OWASP AI Exchange, OWASP LLM Top 10, OWASP Agentic Security Top 10. Each row maps an AI asset class to how that framework applies. Each returned framework also carries a 'concepts' array of the structured IDs (MITRE ATLAS techniques, OWASP risks, ISO clauses) the matrix references for it. Supports a 'buyer' archetype shortcut to scope to the frameworks a particular buyer will care about. Use to translate between framework vocabularies. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• aidefense_evaluate_program

  website-search

  Get the AI Defense Matrix evaluation playbook for assessing an AI security program: per-cell prompts, gap-inventory template, and a workflow that walks each asset class first and rolls findings up to the Govern column. Supports mode='gate' for binary deployment-gate decisions (returns the deployment-gate workflow plus gate-tier prompts only) and consumerPattern for scoping to consumed-vs-built AI deployments. The AI applies these prompts against your program documentation locally, and no program details leave your client. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• aidefense_cross_map

  website-search

  Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• cti_get_cross_server_routes

  website-search

  Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. 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_api_reference_pages

  docs-mcp

  List pages in Redpanda API reference documentation. Returns endpoints, schemas, and topic pages with URL, title, type, and description. SCOPING (important for accurate results): - api="all" or omit: Lists all available APIs - api="admin": Cluster management operations (brokers, partitions, configs, users) - api="cloud-controlplane": Redpanda Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Cloud cluster data operations (topics, ACLs, connectors) - api="http-proxy": Kafka operations over HTTP (produce, consume, offsets) - api="schema-registry": Schema management (register, retrieve, compatibility) Use this to browse API structure. For general Redpanda docs, use ask_redpanda_question instead.

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• learning_path

  canton-ccpedia

  Construct a structured Canton onboarding sequence for a stated goal: whitepaper → relevant docs → CIPs to know → forum starter threads → talks. Canton-specific. Topic-driven (distinct from get_started_guide which is background-driven).

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