"A server for finding information and resources related to UX (User Experience) design" matching MCP tools:

• dreamagent_create_projectA

  DreamAgent CloudA

  WRITE ACTION — creates a new DreamAgent project (website, Telegram bot, Discord bot, or scheduler). Use ONLY when the user clearly asks to create/build a new project; not for questions about what to build. CONFIRM BEFORE CALLING — never create on the first mention. In ONE message, present and get the user's explicit go-ahead for: 1. The credential: run dreamagent_list_global_integrations first. If several telegram/discord credentials are saved, list them (id + title) and ask which to use. If exactly one is saved, state which one you will use. Never pick silently. 2. The plan: name, type, and the final description exactly as you will submit it (this is the Prompt Assistant's recommendation summary). Call this tool only after the user confirms. CREATION IS ASYNCHRONOUS: after calling, check dreamagent_get_project_status repeatedly until the project is 'ready' or 'failed' (bots ~2-5 min, websites longer). CREDENTIALS: raw bot tokens are never accepted in chat or as inputs. For Telegram/Discord bots you MUST pass bot_token_integration_id — the ID of a saved credential from dreamagent_list_global_integrations. If none is saved, direct the user to dreamagent.cloud → Settings → Global Integrations. Other saved keys can be imported via global_integration_ids. Credentials are stored as project secrets and are never exposed back. DESCRIPTION = the refined creation brief. Act as a Creative Director / Product Manager, not an architect. Create a concise but complete description of WHAT should be built: - product vision and target audience - user experience and core functionality - design/tone direction - important features and behavior - final expected result Do not include implementation details such as: - technology stack - architecture - database/API implementation - authentication implementation - deployment - CI/CD - testing commands Infer reasonable defaults when missing details are non-critical. Ask for clarification only when missing information materially affects the requested functionality, scope, credentials, or expected result. Prefer the following structure and constraints for each project type, but do not override explicit user requirements: Website: - Project Vision - Design Style - Pages - Hero Experience - Core Features - UI Components - Mobile Experience - Final Expectation - Prefer up to 4 pages unless the user clearly requires more. Telegram bot: - Bot Purpose - Commands - User Flow - Optional AI Features - Integrations when required - Final Expectations - Prefer a compact command set; always include /start and /help unless the user's explicit requirements conflict. Discord bot: - Bot Purpose - Slash Commands - Events - Permissions - Optional AI Features - Final Expectations - Prefer a compact command set; include /help where appropriate. Scheduler: - Job Purpose - Data Sources - Schedule - Delivery Channels - Message Format - Final Expectations - Use concrete schedules and specify failure/timeout behavior.

  ConnectorNo auth

• dreamagent_create_projectA

  DreamAgentA

  WRITE ACTION — creates a new DreamAgent project (website, Telegram bot, Discord bot, or scheduler). Use ONLY when the user clearly asks to create/build a new project; not for questions about what to build. CONFIRM BEFORE CALLING — never create on the first mention. In ONE message, present and get the user's explicit go-ahead for: 1. The credential: run dreamagent_list_global_integrations first. If several telegram/discord credentials are saved, list them (id + title) and ask which to use. If exactly one is saved, state which one you will use. Never pick silently. 2. The plan: name, type, and the final description exactly as you will submit it (this is the Prompt Assistant's recommendation summary). Call this tool only after the user confirms. CREATION IS ASYNCHRONOUS: after calling, check dreamagent_get_project_status repeatedly until the project is 'ready' or 'failed' (bots ~2-5 min, websites longer). CREDENTIALS: raw bot tokens are never accepted in chat or as inputs. For Telegram/Discord bots you MUST pass bot_token_integration_id — the ID of a saved credential from dreamagent_list_global_integrations. If none is saved, direct the user to dreamagent.cloud → Settings → Global Integrations. Other saved keys can be imported via global_integration_ids. Credentials are stored as project secrets and are never exposed back. DESCRIPTION = the refined creation brief. Act as a Creative Director / Product Manager, not an architect. Create a concise but complete description of WHAT should be built: - product vision and target audience - user experience and core functionality - design/tone direction - important features and behavior - final expected result Do not include implementation details such as: - technology stack - architecture - database/API implementation - authentication implementation - deployment - CI/CD - testing commands Infer reasonable defaults when missing details are non-critical. Ask for clarification only when missing information materially affects the requested functionality, scope, credentials, or expected result. Prefer the following structure and constraints for each project type, but do not override explicit user requirements: Website: - Project Vision - Design Style - Pages - Hero Experience - Core Features - UI Components - Mobile Experience - Final Expectation - Prefer up to 4 pages unless the user clearly requires more. Telegram bot: - Bot Purpose - Commands - User Flow - Optional AI Features - Integrations when required - Final Expectations - Prefer a compact command set; always include /start and /help unless the user's explicit requirements conflict. Discord bot: - Bot Purpose - Slash Commands - Events - Permissions - Optional AI Features - Final Expectations - Prefer a compact command set; include /help where appropriate. Scheduler: - Job Purpose - Data Sources - Schedule - Delivery Channels - Message Format - Final Expectations - Use concrete schedules and specify failure/timeout behavior.

  ConnectorNo auth

• read_schematicA

  boardrepoA

  Use when the user asks how a public board is wired, what a component (e.g. U1) connects to, or which pins are on a net (e.g. GND), in that design. Returns the board's latest geometry-free connectivity. No focus returns a bounded overview (components + a net index); ref returns one component and the nets it connects to with the other pins on those nets; net returns the pins on that net. These are in-design connections, not an authoritative manufacturer pinout, and a very large design may be truncated (the response flags this). Prefer a focused ref or net over repeated overviews. Use get_bom for purchasing and read_file for raw source. If nets are still computing, continue with the components shown and try again shortly rather than inferring connectivity.

  ConnectorOAuth

• design.validateA

  AI Design BlueprintA

  Pro/Teams — first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE — what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT — DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run — so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui — non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable — submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders — they are read as literal code). Auth: Bearer <token>, Pro/Teams plan. UK/EU residency; transient OpenAI processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable, schema_mismatch — each carries retryable + next_action); the services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws — each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against — live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).

  ConnectorNo auth

• well_get_entityA

  WellA

  Read ONE entity with its sub-resources nested in a single call. Convenience over well_get_schema + well_query_records: resolves the field paths for you and returns the single record with its related data expanded. depth (relation-nesting BOUNDARY, 1-3, default 1): 1 = the entity + its direct sub-resources (emails, phones, locations, …) 2 = + the sub-resources' related scalars 3 = the full level-3 graph (LARGER payload — use when you need the whole picture) Stops at depth 3. Aggregates are excluded. Each child collection is capped at 50 rows; for a full list or to page a large child collection, use well_query_records on that child root instead.

  ConnectorNo auth

• migrate_to_propelauth_fullstackA

  PropelAuth Integration MCP ServerA

  Returns instructions for migrating from an existing auth provider to PropelAuth in a fullstack Nextjs App Router or Nextjs Pages Router application. If the user is using Next.js as just a frontend (e.g. client-side rendered with or without server routes), use the migrate_to_propelauth_frontend tool. Guidance includes installation and configuration, retrieving user or org information, logging users out, redirecting users to login, and more. Make sure to use the 'Installation' guidance first. It is important to call every guidance to ensure a successful integration. Do not update a component/hook/etc from the auth provider until you receive guidance about that component/hook/etc

  ConnectorNo auth

• design.validateA

  AI Design Blueprint DoctrineA

  Pro/Teams — first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE — what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT — DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run — so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui — non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable — submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders — they are read as literal code). Auth: Bearer <token>, Pro/Teams plan. UK/EU residency; transient OpenAI processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable, schema_mismatch — each carries retryable + next_action); the services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws — each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against — live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).

  ConnectorNo auth

• get_vps_recommendationA

  FracteraA

  Return a single recommended VPS provider for users who do not yet have a server. Call this ONLY when the user explicitly says they have no server. The user buys the VPS at this provider and comes back with IP + password.

  ConnectorNo auth

• submitExperienceApplicationA

  Nomad StaysA

  Submit a completed Experience Application for human review. Rejects with a missingFields list if any required field is still empty, or a 409 if the Application Fee hasn't been paid/waived yet (call purchaseProduct with productId 9 and applicationId first — Experience uses product 9, NOT product 8). There is no partial/optimistic submission. On success the application moves to human review. Requires NOMADSTAYS_MCP_AGENT_TOKEN.

  ConnectorNo auth

• get_tier1_recommendationsA

  Tier1 IT AssessmentA

  Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.

  ConnectorNo auth

• get_corpus_coverageA

  SRI — MCP Server InspectorA

  No arguments. Returns how many MCP servers have been read at source level, and the share of them with each category of finding (credential access, network egress, install-time execution, prompt-injection surface). Use this to judge whether checking a specific server is worth it before you look one up. It reports aggregate counts only - no per-server findings, and no verdict about any individual server.

  ConnectorNo auth

• get_profileA

  Alex Polonsky ProfileA

  Get Alex Polonsky's curated public professional profile, including selected experience, demonstrated capabilities, journalism background, public projects, and future professional directions. This is selected evidence, not a complete CV. When summarizing examples, preserve their ownership qualifier exactly: led, owned_key_aspects, or contributed. Do not infer stronger ownership. If a list mixes things Alex built with work he contributed to or helped bring to market, frame it as built or contributed to, not built. Keep the action verbs and limits from contribution; do not reduce a contribution to an artifact name. Separate demonstrated experience from future directions. Use get_availability for current opportunity status.

  ConnectorNo auth

• particle_podcast_list_relatedA

  particle-proA

  List the shows most related to a podcast, best first — "shows like this show". Each result carries the related show's slug, a calibrated score in (0,1], and a coarse band (strong: same beat and audience; moderate: overlapping subject or audience; weak: a loose connection) to branch on. Add `include: ["basis"]` to see WHY each pair is related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors — use it to explain a recommendation or to keep only pairs related for the reason you care about (shared guests for booking, content for media planning). Related sets are precomputed per show from its transcripts, topic profile, guest roster, network and advertisers, restricted to the show's language; a show whose set has not been computed yet returns an empty list, not an error. Not a topic browser: for shows that COVER a topic use `particle_podcast_resolve` with `topic_slug`. Not a guest lookup: for where a person has appeared use `particle_podcast_get_guest`. Not advertiser co-occurrence: use `particle_podcast_get_sponsors`. Every related show's slug feeds `particle_podcast_resolve`, `particle_podcast_list_episodes` and the other podcast tools; person slugs in the basis feed `particle_podcast_get_guest`, topic slugs feed `particle_podcast_resolve`'s `topic_slug`. For the five most related shows inline on a resolve, pass `include: ["related"]` to `particle_podcast_resolve` instead of calling this tool.

  ConnectorOAuth

• handoffs.agencyA

  AI Design BlueprintA

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

  ConnectorNo auth

• tailor_resume_toolA

  WorkopiaA

  Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.

  Connector

  Destructive

  No auth

• principles.searchA

  AI Design Blueprint DoctrineA

  Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.

  ConnectorNo auth

• verify_integrationA

  Alter OnboardingA

  Return a copy-pasteable recipe to VERIFY an integration works: `first-call` (code↔design, an audit row, correct attribution) or `per-user-isolation` (a multi-user/broker server runs two users under different credentials and rejects cross-user access). Guidance only — you run the commands.

  ConnectorNo auth

• principles.listA

  AI Design BlueprintA

  List Blueprint doctrine with stable slugs, titles, and clusters. The lens selects which of the three public doctrines: 'architecture' = the 10 agentic principles (default, the architect.validate rubric); 'surface' = the 8 experience-design laws (the design.validate rubric); 'spec' = the 8 spec-quality laws (the spec.validate rubric). Use this when you need the full inventory or want every entry in one cluster (pass cluster slug to filter). Prefer principles.search when the user describes a topic, failure mode, or keyword in natural language. Prefer principles.get when you already know the exact slug and need full detail.

  ConnectorNo auth

• principles.getA

  AI Design BlueprintA

  Get one doctrine entry by stable slug. The lens selects the doctrine: 'architecture' = one of the 10 agentic principles (default); 'surface' = one of the 8 experience-design laws; 'spec' = one of the 8 spec-quality laws. Returns id, title, cluster, definition, rationale, implications, and risk-if-violated (laws also carry their eponym and validator_questions). Use this when you already have the exact slug from principles.list; prefer principles.search when the user describes a topic or failure mode in natural language; prefer principles.list when you need every entry or every entry within a cluster. Returns error_payload on unknown slug for the lens.

  ConnectorNo auth

• principles.searchA

  AI Design BlueprintA

  Search Blueprint principles by free-text query and return the closest matches ranked by relevance. Use this to find principles related to a specific design challenge, failure mode, or keyword (e.g. 'reversibility', 'approval flow', 'delegation boundary'). Returns principle title, cluster, definition, rationale, and implementation heuristics. Prefer this over principles.list when you have a specific topic in mind rather than wanting all principles. NOTE: search currently covers the 10 agentic principles only; for the 8 experience-design laws or the 8 spec-quality laws use principles.list(lens='surface') / principles.list(lens='spec') until search spans all three lenses.

  ConnectorNo auth

• get_aboutA

  Follow On ToursA

  Get information about Follow On Tours — who we are, what we sell (bespoke cricket and golf travel), our experience, our financial protection, and how the service works. Use this when someone asks who Follow On Tours is, whether they cover a sport or destination, or how the service operates.

  ConnectorNo auth