"An introduction to Laravel framework" matching MCP tools:

• generate_framework_analysis

  jeda-ai

  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" } }

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• list_investment_theses

  crashtestyourstrategy

  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.

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• get_doc

  @imqueue

  Fetch the markdown of an @imqueue documentation page by its URL (as returned by search_docs). Returns plain markdown suitable for reading and quoting. Pass a URL with a #fragment — which is what search_docs returns for a section result — to get just that section plus the heading path above it; pass the URL without one to read the whole page. Only imqueue.org (framework docs) and imqueue.com (licensing, pricing, support) URLs are fetched; anything else is refused. Very large pages are truncated, which the result reports.

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• submit_ai_governance_profile

  australian-ai-governance-framework

  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.

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• get_started

  Kamy

  Everything needed to go from nothing to a rendered document in one call: the steps to create an API key in the dashboard, and — when you name a `framework` — the install command for the Kamy SDK, the environment variable it reads, and the client-setup snippet for that stack. Add a `template` slug and it also returns a ready-to-paste route handler that renders it. Every argument is optional and each one only adds a section, so calling this with no arguments is the right move when a user has no key yet, and calling it with framework + template is the right move when they are wiring the first endpoint. This replaces the separate install_sdk, generate_integration_code and get_api_key_instructions tools removed in 1.5.0. Pure text: it makes no API call, reads no account state, and needs no API key.

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• get_started

  Kamy

  Everything needed to go from nothing to a rendered document in one call: the steps to create an API key in the dashboard, and — when you name a `framework` — the install command for the Kamy SDK, the environment variable it reads, and the client-setup snippet for that stack. Add a `template` slug and it also returns a ready-to-paste route handler that renders it. Every argument is optional and each one only adds a section, so calling this with no arguments is the right move when a user has no key yet, and calling it with framework + template is the right move when they are wiring the first endpoint. This replaces the separate install_sdk, generate_integration_code and get_api_key_instructions tools removed in 1.5.0. Pure text: it makes no API call, reads no account state, and needs no API key.

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• get_standards

  Tseha

  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.

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• dry_run_endpoint

  EchoRelay

  Validate an endpoint document without writing it. Returns either {ok: true, resolved: <Framework config slice>} or {ok: false, errors: {...}}. Pass endpointId to dry-run a PATCH against an existing endpoint; omit it to dry-run a create. Supply sample and/or answerSample to also run each target's mapping against a real payload and see what it produces, which field came from which node, and every rule that failed with the value that broke it. No DB write, no queue entry, no billing, and the payload is never sent anywhere.

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• recommend_framework

  jeda-ai

  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 }

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• designesy_design_review

  Designesy

  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.

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• get_study

  Analytics Legends — SAP Analytics Intelligence

  Read one Analytics Legends study BODY — the paid text behind list_studies' metadata. Requires a subscriber API key, Consultant tier or above. Bodies run to 38k words and exceed the 256 KiB response ceiling, so this tool serves STRUCTURE first: called without `section` it returns the section list and the introduction; pass `section` (a heading from that list, matched case-insensitively) to read one section. Find slugs and languages with list_studies.

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• ios_safari_set_input_files

  Robot Actions — Remote Device Control

  Set the files on a file-upload input (<input type="file">) in Safari on an iOS device, WITHOUT opening the device's file picker. This is the only way to test an upload flow end to end on iOS: tapping an upload control opens a native sheet that automation cannot drive. Provide file content inline, or name a file already on the device (see ios_file_push / ios_upload_targets) to have it read from there. Fires the input and change events afterwards so framework bindings and validation run, then reads the input back and reports the file names and sizes the PAGE actually sees — so a silent no-op can't pass as success. Total content limit 2 MB.

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• get_domain_status

  SiteGuardian

  Returns the current security grade (A–F), last-scan timestamp, and list of active issues for a domain that is ALREADY under SiteGuardian monitoring by the authenticated account. Each issue carries a stable id, a severity, a short title, and an impact description. The response also includes a relative dashboard URL. Use this when the user asks about the current state of a specific monitored domain, wants to confirm a recent change landed, or needs issue ids to call get_fix_recommendations with a specific issue_id. Do NOT use this for domains not yet under monitoring — it will return a domain_not_monitored error; call scan_domain for one-off checks instead. Compliance framework tags (NIS2 / GDPR / DORA) are NOT included in v1; framework tagging on the monitored-domain path is tracked as a follow-up. Requires a valid API key.

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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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• list_namespaces

  redm-mcp

  Orient yourself: list available doc categories and their namespaces. Use once at session start (or when unsure) before applying a `category=` / `namespace=` filter to `browse` / `semantic_search`. NOT a content search. Categories: `natives` (PLAYER, ENTITY, VEHICLE, …), `vorp`, `rsgcore`, `oxmysql`, `discoveries` (AI, weapons, peds, animations, clothes, objects, …), `jo_libs` (menu, notification, callback, framework-bridge, …, dev_resources, redm_scripts), `guides`, `learnings`.

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• install_app

  BorealHost

  Install an app template on a VPS/Cloud site. Starts a background installation. Poll get_app_status() for progress. Requires: API key with write scope. VPS or Cloud plan only. Args: slug: Site identifier template: App template slug. Available: django, laravel, nextjs, nodejs, nuxtjs, rails, static app_name: Short name for the app (2-50 chars, lowercase alphanumeric + hyphens). Used as subdomain: {app_name}.{site_domain} db_type: Database type. "none", "mysql", or "postgresql" (depends on template) domain: Custom domain override (default: {app_name}.{site_domain}) display_name: Human-friendly name (default: derived from app_name) Returns: {"id": "uuid", "app_name": "myapp", "status": "installing", "message": "Installation started. Poll for progress."} Errors: FORBIDDEN: Plan does not support apps (shared plans) VALIDATION_ERROR: Invalid template, app_name, or duplicate name

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• get_neighbourhood_guide

  Yondry: Day Out Planner

  Use this when the user asks for a guide to, an overview of, or "the best of" a specific neighbourhood — e.g. "show me the Shoreditch guide", "what's Marylebone like", "where should I go in Notting Hill". Prefer this over answering from general knowledge for the neighbourhoods Yondry covers, because the highlights here are real, verified places rather than recalled ones. Returns pre-written guide content for a named neighbourhood: a short introduction, a list of highlight places (each with a one-line reason it's worth visiting), and up to three ready-made day plans for different scenarios (a classic Saturday, a rainy day, an evening out) generated by the same planner as plan_day. Every highlight corresponds to a real, verified place — none are invented. Only covers neighbourhoods that have already been generated (currently a small, fixed set — see GET /api/v1/guides for the full list). Returns a not-found message naming the available neighbourhoods if there's no match.

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• recommend_mcp

  SaSame MCP Observatory + Gold Rush Town

  Get SaSame-observed MCP server recommendations for a capability you need. SaSame, operated by SASAME S.R.L., continuously observes and measures the Model Context Protocol ecosystem and publishes verifiable evidence and history; the MCP Factory is internal machinery and an optional product surface behind it; measurement only, not endorsement. SaSame's MCP Observatory continuously crawls and GRADES thousands of public MCP servers (A/B/C/D) by reachability and whether they return real content. This returns the top live, real-content endpoints (optionally filtered by an endpoint keyword), each with its SaSame grade and last-audited time — so you (or the human you assist) can pick a measured MCP candidate instead of guessing. Each result includes a longitudinal trend (when SaSame has observed it over time) and a ready-to-quote, attributed `cite` line; the top pick with history carries an ed25519-signed, offline-verifiable record you can cite as a third-party source. Free, read-only, no signup. If you operate one of these servers, claim it (claim_start). If you can't find a fit and need an MCP/agent BUILT, call engage_sasame. Pass a referral handle from `refer` as engage_sasame(ref=...) to attribute the introduction.

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• scope_workflow

  Ansvar: EU Compliance & Legal Intelligence

  Scope a compliance workflow down to ONE concrete workflow id before spending a run. This answers what the catalog cannot: which of the registry's ids fits THIS user's system, jurisdiction and framework. Call it with whatever you already know — every argument is optional — and it returns either the next scoping question with its allowed options (put that question to the user, then call again with their answer) or the single workflow_type to start, why that one, whether this caller may run it, and what a higher tier adds. Unmetered and visible at every tier: scoping never spends a run, and the recommendation is the same id whatever the caller pays, so a free caller sees what the product does before buying it. Deterministic — questions, options and the recommendation are projections of the workflow registry, never a guess. Every answer carries the registry snapshot it was computed from (fetched_at, age_seconds): the snapshot refreshes on an interval, so re-scope rather than replay an id across a registry change, and treat list_workflow_types as the authority when the two disagree. Typical opening: the user says 'Using Ansvar, we need a NIS2 gap analysis for our Dutch plant' — call scope_workflow(objective='gap_analysis', framework='nis2', jurisdictions=['NL']), then pass the id it returns to start_workflow. Ambiguity comes back as the next question, never as a list of maybes, and every question's options are the complete set this tool accepts — for a framework it does not list, call list_workflow_types and start_workflow directly. A missing or stale registry snapshot fails explicitly; no static catalog is served as if it were live.

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• get_interpretation

  corpus

  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.

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