"How to use Bootstrap 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

• 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_changes_sinceA

  remnusA

  Everything created, updated or deleted since a cursor (from the workspace digest/map or a previous call) or an ISO time — pages, databases, rows and deletions, oldest first. Omit both to bootstrap. Always keep nextCursor for the next call: this is how to catch up instead of re-reading the tree. An entry stamped in the cursor's own second may appear once more — dedupe by id.

  ConnectorOAuth

• 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

• designesy_design_reviewA

  DesignesyA

  Get the Designesy Design Review framework — an 8-dimension rubric (Purpose, Clarity, Context, Inclusion, System coherence, Durability, Delight, Responsibility) plus the agent prompt, output format, and verification checklist for a qualitative design critique. Use this when you want a structured rubric to critique a design holistically, rather than a numeric compliance score. When NOT to use: for a deterministic numeric score, use designesy_score; this tool gives you a rubric, not a number. Read-only — returns the rubric + prompt. The calling agent performs the actual critique (this tool does not evaluate the design for you). Returns JSON: { rubric, dimensions[8], agent_prompt, output_format, verification_checklist }. Pass artifact/purpose/context/rules to get a pre-filled critique prompt; omit all four to get the blank framework.

  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

• how-toA

  Fast.ioA

  Built-in product help — ask a natural-language "how do I…" question about Fastio and get a grounded, product-aware answer (or a short clarifying question) back in one call. EXPLAIN-ONLY / ADVISORY: it returns GUIDANCE TEXT and performs NO platform action (it will not create shares, move files, or change anything) — read the guidance, then act with the other tools. Answers are grounded in Fastio's own how-to knowledge AND phrased in terms of these MCP tools — they name the concrete `<tool> action="…"` calls to make — so prefer this over guessing endpoints or burning exploratory calls. For Q&A over YOUR uploaded files (RAG) use the `ai` tool instead — `how-to` answers questions about Fastio ITSELF. FREE and requires only an authenticated user (no org, no plan gate, no billing). Call action='describe' for the full action/param reference.

  ConnectorNo auth

• get_interpretationA

  corpusA

  Return the Wheel of Heaven interpretive framework's reading of a topic — explicitly the project's own Raëlian-canon-centred position, NOT mainstream consensus. Accepts a framework topic (overview, hypothesis, terminology, timeline, sources, method) for the curated narrative documents, or any other term to get the framework reading from the closest wiki entry. Use fact-layer tools (get_passage, compare_traditions) for source-grounded data without this framing.

  ConnectorNo auth

• prior_searchA

  Prior — Knowledge Exchange for AI AgentsA

  Search for solutions other agents already discovered. STOP and search before acting on any error — even mid-task. Do not chain fixes without searching each one. When: Any error, compile failure, stack trace, or unexpected output. Before config, integration, or setup tasks. When a fix attempt just failed — search before trying the next one. Unfamiliar library or framework combination. Do not skip because the fix seems obvious — obvious fixes are the most common and most likely to already be solved. How: Paste the exact error message, not your goal. Include framework or language name. Read failedApproaches first to skip dead ends. Feedback: Include previousSearchFeedback to rate a result from your last search — this refunds your search credit and costs nothing extra.

  ConnectorOAuth

• pathrule_setupA

  PathruleA

  Fetch the active Pathrule bootstrap brief and execute it. Call this ONCE when the user asks to set up / bootstrap / initialize Pathrule for a project (e.g. 'Set up Pathrule for this project', 'Bootstrap Pathrule'). The response `body` is a prompt you must follow immediately — it tells you how to scan the project, propose memories/rules/skills, and write the approved items via pathrule_write_memory / _rule / _skill. Do NOT call this mid-task, for already-populated workspaces, or when the user just wants context — use pathrule_get_context for routine context lookups. If no workspace exists yet, call pathrule_list_organizations + pathrule_create_workspace first.

  ConnectorNo auth

• pysr_uncertaintyA

  OccamA

  Bootstrap confidence intervals for the numeric constants of a frozen expression, plus optional prediction bands on an x-grid. Typical flow: call pysr_run, pick an expression from the response (best_expression or a pareto_front entry), pass it back here with the same dataset to get CIs on its fit constants. Returns frequentist bootstrap confidence intervals, not Bayesian credible intervals — posterior inference over expression structures is an open research problem. This tool freezes the expression chosen by the caller and bootstraps only its numeric constants; uncertainty about *which* expression is correct is not quantified. Bootstrap semantics: - If y_sigma is supplied, uses parametric bootstrap (y_b = y + Normal(0, y_sigma)). CI reflects user-stated measurement noise. - Otherwise uses residual bootstrap: fit once, resample residuals. CI reflects estimated-from-residuals noise. Only Float constants in the expression become free parameters. Integers stay structural (the 2 in x**2 is a function-class choice, not a fit constant). Expressions with no Float constants (e.g. "x + y") will be rejected with a validation error. Expression grammar: the `expression` string is parsed by sympy. Accepted operators are the same set pysr_run emits: unary `sin`, `cos`, `tan`, `exp`, `log`, `log2`, `log10`, `sqrt`, `abs`, `sinh`, `cosh`, `tanh`; binary `+`, `-`, `*`, `/`, `^` (or `**`). Whitespace and parenthesization are free. Every free symbol in the expression must correspond to an entry in `feature_names` — an unrecognised symbol is silently treated as a fresh sympy Symbol and the fit will fail downstream rather than reject early. Parse failures (syntax errors, malformed operators) surface as tool errors. If `feature_names` is supplied, its length must equal the number of columns in `X`; a mismatch is rejected with a validation error. Pricing: always free, regardless of dataset size. This tool has no `payment` parameter and is never subject to the x402/Stripe gate. Large bootstrap jobs still count against the shared rate limit below, so budget `n_resamples` accordingly. Rate limit: 10 requests/hour per IP, 200/hour global, max queue depth 20 (shared with sindy_run and pysr_run).

  ConnectorNo auth

• list_suitesA

  saferagenticai-mcpA

  List all 16 suites in the SaferAgenticAI framework (9 drivers + 7 inhibitors) with subgoal counts and titles. Call this first to orient.

  ConnectorNo auth

• sap_quick_contextA

  SAP MCP ServerC

  Free single-call bootstrap aggregator. Returns a compact markdown-like summary with server version, total tools count, tools by category, pricing tiers summary, premium plugins/capabilities count, bundled skills list, and nextAction guidance. Pass agentKnownVersion with the version you currently know to get skillsUpdateRequired + skillsContents (full SKILL.md inline) when the server version differs — this lets you auto-update your local skills in 1 call without a separate sap_skills_bundle. Use this instead of calling sap_agent_start, sap_pricing_catalog, sap_premium_plugin_catalog, sap_skills_list, and sap_get_tool_category_summary separately to reduce bootstrap from 5+ tool calls to 1. SAP MCP execution guidance: Intent: SAP MCP tool workflow. Pricing: free; call directly without x402. Routing: free hosted call; call directly and keep it small/exact when possible. Signer boundary: hosted reads/builders never receive keypair bytes; value-moving results must be finalized locally when signing is required.

  ConnectorNo auth