"CopilotKit - AI copilot development framework" matching MCP tools:

• generate_framework_analysisA

  jeda-aiA

  Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

  ConnectorNo auth

• list_investment_thesesA

  crashtestyourstrategyA

  Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.

  ConnectorNo auth

• submit_ai_governance_profileA

  australian-ai-governance-frameworkA

  Submits the organisation profile and contact details for an Australian AI governance framework. The profile determines which legislation the framework identifies, so the answers should reflect the organisation's actual circumstances — turnover in particular, since the Privacy Act's small business threshold sits at $3 million and several categories are caught regardless of turnover. Takes the session ID from start_australian_ai_governance_framework together with the questionnaire answers. Writes the profile against the session and stores the supplied name, email and organisation as a contact record. contact.organisation is printed as the document's "Prepared for" heading, so it should be the organisation's name as it should appear on the document rather than a shorthand. Returns the session ID and a status of profile_saved — it does not return the framework, which is retrieved by get_ai_governance_framework. The profile can be re-submitted on the same session: it is overwritten rather than duplicated, the contact record is keyed on the email address, and any framework already generated for that session is discarded. No authentication, and no charge at this step.

  Connector

  Destructive

  No auth

• get_standardsA

  TsehaA

  Returns the organization's development standards: coding conventions, project structure, and framework-specific rules. Read-only. Call it before writing or reviewing code, so the result follows this organization's rules rather than general defaults. Call it first without a section to get an index of available sections, each with a note on what it covers, then call again with one section id copied from that index; inventing a section id returns a not-found error naming that step. Request only the sections a task needs - the full content of one section can be long. The framework argument is deprecated: use section with the "framework:" prefix instead. It returns prose rules, not data - use get_style_tokens for visual values and get_component for component APIs.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize GovernanceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• recommend_frameworkA

  jeda-aiA

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

  ConnectorNo auth

• list_supported_frameworksA

  Titan Frameworks (LangChain, LlamaIndex, Ollama, XRPL)A

  List the framework ids this server covers (langchain, llamaindex, ollama, xrpl) with display names, aliases, homepages, and catalog topics. Use when you do not know which framework string to pass. Free tools/call (no x402). Not a docs search (search_ai_framework_docs) and not a deprecation dump (list_known_deprecations). Catalog-backed.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize HealthcareB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Real EstateB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize Crypto & DeFiB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize IntelligenceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize FinanceB

  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. $0.02 USDC per call.

  ConnectorNo auth

• get_adoption_stageA

  Stratalize OracleB

  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. $0.02 USDC per call.

  ConnectorNo auth

• ai_citationsA

  SEOmaticA

  This site's OWN first-party AI-citation data, the part no scan can see. observed_citations: citations recorded in REAL logged-in AI answers (ChatGPT, Perplexity, Gemini, Copilot, Claude, AI Overviews) by the user's browser extension, with per-engine counts and best rank - engines personalize and gate their APIs, so a server-side scan sees a de-personalized view while this sees what a real human was shown. brand_facts: the canonical business facts this site publishes for AI engines, plus internal-link health; use them to spot an AI answer contradicting the owner's own declaration. Counts only, never prompt or answer text. scan_results reads the latest server-side SCAN instead: per-engine brand visibility, the prompts where the brand was MISSED and who won them, competitors, and the sources engines cite. Observed vs scanned is the key distinction - observed is what a real logged-in human saw, scanned is a clean de-personalized baseline comparable over time. For AI traffic per page use traffic_analytics. Already scoped to the connected workspace and its site; call directly, no domain or site parameter is needed. Cost: FREE - reads your connected/stored data, no AI credits.

  ConnectorOAuth

• aidefense_locate_conceptA

  website-searchA

  Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. 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.

  ConnectorNo auth

• get_ai_trafficA

  RevenueScope: revenue-first analytics for your EC siteA

  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.

  ConnectorNo auth

• search_frictionsA

  copilot-studio-friction-indexA

  Ranked search over the Copilot Studio Friction Index. Exact error-code/message hits rank first, then title, alias, summary and symptom-checklist matches (solution bodies are NOT searched — an empty result means no record is indexed under these terms, not that the register lacks a fix). Use this when the user describes a Copilot Studio problem, symptom or keyword. Returns compact records with slug, status, severity, last-verified date and the citable powerleap.ch URL.

  ConnectorNo auth

• findagent_list_tech_facetsA

  FindAgentA

  List the controlled Tech Domain and Language/Framework vocabularies (the software-development discipline facets). Pass chosen slugs as create_draft tech_domains[] / languages[]. Read-only; these are enrichment facets (unknown slugs are dropped). Prefer findagent_submission_wizard to walk the user through the tech step-by-step.

  ConnectorOAuth

• get_ai_readiness_frameworkA

  AI Recommendation Readiness Audit | The Black Friday AgencyA

  Returns the AI Recommendation Readiness scoring framework: the scored dimensions and their weights, the four readiness tiers with score ranges, and every valid input option (business sizes, data maturity levels, technical stacks, and goals). Read-only and deterministic; use it to understand how the assessment is scored and to build valid inputs for assess_ai_recommendation_readiness.

  ConnectorNo auth

• list_servicesA

  The AutomatorsA

  Returns the six service pillars and the eleven AI Agent Development capabilities, each with a one-line description and URL. Use it first to find the right service slug before calling get_service. Not for pricing (get_pricing).

  ConnectorNo auth

• get_god_promptA

  GodPrompt MCP ServerA

  Returns the complete GodPrompt.md — a single-file universal system prompt for AI software development (47KB, ~1145 lines). Use this when you want the full payload in one shot. For progressive disclosure (smaller context), use get_core_skill and the reference tools instead.

  ConnectorNo auth