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741,505 tools. Updated 2026-11-04 04:38

"A search for Revit software and resources" matching MCP tools:

  • Fetch the full content for one id returned by `search`: a rental listing (rental:<uuid>), a plan set (plan:<uuid>) or a published page (page:<route>). Returns {id, title, text, url, metadata}. Always cite the url. Hosts that can call specific tools should prefer get_rental or the resources.
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  • Compare what this account pays for in Google Ads search with what its Search Console site ranks for, over the same dates: paid terms ranking top three, queries with no Google Ads search term found, and paid terms with no organic ranking, with cost, clicks and position. Identical wording only. Read-only; needs Search Console. Tools revision: 8ebdf5be.
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  • Search SaidItHere for real-world software dilemmas, framework gotchas, tool fatigue, and battle-tested community consensus workarounds from Reddit, Hacker News, GitHub, and Stack Overflow. Usage Guidelines: - Primary discovery tool: use FIRST when investigating bugs, library gotchas, unexpected price hikes, or developer complaints by keywords. - Sibling routing: follow up with 'get_problem(slug)' using the discovered dilemma slug to inspect full evidence quotes, sources, and step-by-step workarounds. - Sibling routing: if 0 matching dilemmas are returned, use 'suggest_dilemma' to queue the topic for crawler consensus synthesis. - Do NOT use for generic language syntax lookups or textbook documentation queries. Behavior & Side Effects: - Pure read-only, safe, and idempotent query without side effects. Does not modify user, session, or database state.
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  • Import a Revit/BIM model into the Twinmotion visualization pipeline: downloads the source file from a public URL, uploads it to an APS OSS transient bucket, and kicks off an SVF2 + thumbnail translation job. Returns the base64 URN (project_id) used by every other tm_* tool. When to use: when a user wants to prepare a Revit (.rvt), IFC (.ifc), or other BIM/CAD model for real-time visualization in Unreal Engine / Twinmotion — typically the first step before rendering stills, defining scenes, or exporting FBX/glTF/OBJ geometry for a UE import. Also use when you need thumbnails or view metadata from a source file that has not yet been translated by APS. When NOT to use: not for MEP clash review (use navisworks-mcp), not for quantity takeoff or cost estimation (use qto-mcp), not for Twinmotion presets editing — Twinmotion itself has no public REST API, so scene/material authoring must happen manually in the UE editor after FBX/USD export. APS scopes required: data:read data:write data:create bucket:read bucket:create viewables:read. Uses Model Derivative API (translation) + OSS (upload). Twinmotion has no public REST API; all automation is APS Model Derivative + manual Unreal Engine export. Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; large .rvt/.nwd/.ifc files are often multi-GB and translation can take 5–60 min — poll the manifest with exponential backoff (start 5s, cap 60s) rather than retrying this tool. Worker request ceiling is ~100MB body; extremely large files may need signed-URL upload instead. Errors: 401 = APS token failed (check APS_CLIENT_ID/APS_CLIENT_SECRET, re-auth); 403 = scope missing (bucket:create/data:write not granted — have user re-consent); 404 = file_url unreachable; 409 = bucket key collision (rare — retry, tool uses timestamp); 413/507 = file too large for worker memory (advise signed-URL upload); 422 = unsupported source format (only Autodesk-accepted types: rvt, ifc, nwd, dwg, dgn, 3dm, stp, etc.); 429 = back off 60s before retrying; 5xx = APS upstream outage, retry with backoff. Side effects: CREATES a new transient OSS bucket (scanbim-viz-<timestamp>, auto-expires in 24h), CREATES an object in OSS, STARTS a translation job consuming APS cloud credits. NOT idempotent — each call creates a new bucket + URN. Writes a row to usage_log D1 table.
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  • When to use: Kick off a Model Derivative translation of a previously uploaded Revit URN into IFC (IFC2x3 Coordination View 2.0 by default) so the model can be exchanged with non-Autodesk tools (Solibri, BIMcollab, Tekla, openBIM workflows). When NOT to use: Do not use for SVF/SVF2 web viewing (that happens automatically in revit_upload), and do not call repeatedly while a prior IFC job is still inprogress — poll the manifest instead. APS scopes: data:read data:write viewables:read (Model Derivative job + manifest). Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired — refresh. 403 scope insufficient — add data:write. 404 URN not found — confirm model_id was translated. 409 not applicable. 429 rate limited — back off. 5xx APS upstream — retry with jitter up to 3x. Side effects: Creates a Model Derivative job and, on completion, a new IFC derivative inside the model's manifest. Safe to re-run (APS deduplicates) but each call with x-ads-force may retranslate.
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  • Long-poll: blocks until the next edit lands on this board, then returns. WHEN TO CALL THIS: if your MCP client does NOT surface `notifications/resources/updated` events from `resources/subscribe` back to the model (most chat clients do not — they receive the SSE event but don't inject it into your context), this tool is how you 'wait for the human' inside a single turn. Typical flow: you draw / write what you were asked to, then instead of ending your turn you call `wait_for_update(board_id)`. When the human adds, moves, or erases something, the call returns and you refresh with `get_preview` / `get_board` and continue the collaboration. Great for turn-based interactions (games like tic-tac-toe, brainstorming where you respond to each sticky the user drops, sketch-and-feedback loops, etc.). If your client DOES deliver resource notifications natively, prefer `resources/subscribe` — it's cheaper and has no timeout ceiling. BEHAVIOUR: resolves ~3 s after the edit burst settles (same debounce as the push notifications — this is intentional so drags and long strokes collapse into one wake-up). Returns `{ updated: true, timedOut: false }` on a real edit, or `{ updated: false, timedOut: true }` if nothing happened within `timeout_ms`. On timeout, just call it again to keep waiting; chaining calls is cheap. `timeout_ms` is clamped to [1000, 55000]; default 25000 (leaves headroom under typical 60 s proxy timeouts).
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Matching MCP Servers

Matching MCP Connectors

  • Revit model query + APS Design Automation runner.

  • Read and edit the Revit 2024–2027 model on your PC. Needs the free Revit MCP add-in.

  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Return the safely validated redirect chain. Use only for public HTTP(S) resources; it does not execute JavaScript or bypass access controls. Pass url as an absolute public HTTP(S) URL. Keep fresh=false to allow cache reuse; set fresh=true only when a new upstream fetch is required.
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  • Classify MIME type, charset and bounded content length. Use only for public HTTP(S) resources; it does not execute JavaScript or bypass access controls. Pass url as an absolute public HTTP(S) URL. Keep fresh=false to allow cache reuse; set fresh=true only when a new upstream fetch is required.
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  • 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.
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  • Return safe network timing, payload and cacheability diagnostics. Use only for public HTTP(S) resources; it does not execute JavaScript or bypass access controls. Pass url as an absolute public HTTP(S) URL. Keep fresh=false to allow cache reuse; set fresh=true only when a new upstream fetch is required.
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  • Start linking a cloud account so it can be scanned. `provider` is "aws", the only provider supported today; `account_id` is the 12-digit AWS account number. `software` is the Tomorrow Central software the link is for; omit it for Cloud Cost Sentinel. Each software has its own read-only role, so link once per software. This is a two-party flow and you cannot finish it alone — creating the read-only IAM role requires the human's AWS credentials. Returns a `launch_url`. Hand the human these instructions verbatim: 1. Open the launch_url (a pre-filled CloudFormation quick-create link). 2. Review the read-only role it creates, then click Create stack. 3. Tell you when the stack says CREATE_COMPLETE. Then call `verify_connection` with the returned connection_id. Verification fails with a clear message until the role exists, so polling it every ~15 seconds is safe and expected. No ARN or secret needs to be copied by anyone.
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  • Uninstall a release and remove the resources it created, including volume data. Use it, not delete_resource, for anything install_template created. Provide the release `name`.
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    Destructive
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  • Fetch a LIVE Amazon search-results page (SERP) right now — the ranked organic + sponsored listings a shopper would see this minute for a query, with position, ASIN, title, price, rating and badges. Use when the user wants CURRENT ranking/visibility: 'who ranks for "dog bed" on Amazon right now', 'is my ASIN on page 1 for this keyword', 'what's sponsored vs organic for this search'. This is a real-time fetch (takes ~10-40 seconds) — for warehouse keyword search use search_products; for buying advice use shopping_search. Amazon marketplaces only; up to 3 pages per call.
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  • Free. Sends a message to the human operators of this service: a bug report, a problem with these tools or the documentation, or a suggestion. It is read by people, not processed by software, and nothing is returned beyond an acknowledgement. Do NOT use it for status updates, progress notes, task narration, error logs, or as a fallback when another tool is unavailable or a payment cannot be made -- it cannot complete, retry or work around any task. Name is optional (omit for anonymous feedback); message is limited to 1000 characters.
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  • Use this when the user has a specific question for 7IT that describe_studio does not answer. Ask 7IT Solutions, a solo senior software studio, a question about whether it fits a project, what a first phase could cover, how engagements run, or what the client owns. Answers come only from 7IT's published profile and contain no prices or commitments; questions that need a quote are passed to our team, which replies directly.
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  • THE DEFAULT TOOL FOR QUESTION-SHAPED QUERIES ("wie/warum/was hilft bei X?", "how do I…?"): retrieves the best-matching fulltext passages from the corpus and returns them with citations (source resource, page, heading, source URL) — answer the user FROM the passages and cite each source. (For discovery intent — "finde/empfiehl Materialien" — use search_content instead, or afterwards to offer browsable links.) Keep `question` topic-only. Ranking is hybrid keyword+vector, so phrase the question as a topical statement that names the subject and the target group ("Friedenserziehung in der Grundschule: Einstieg in das Thema Frieden mit Kindern"), not as the user's literal sentence ("Wie kann ich …?"). Results are capped at two passages per document; a passage with only a snippet and no text has no fulltext yet — say so instead of guessing. Passages come from learning resources under Creative Commons, Public Domain and free software/documentation licenses, and DINI-AG-KIM license types that resolve to one of these and from articles, wikis, publications and the other content kinds. Other matching learning resources appear in `metadataOnlyMatches` (naddr, title, license, creator/publisher, sourcePage) without text, because their license does not permit quoting — point the user to them by title, license and link, never quote or paraphrase them as content. Cite the license and the creator/publisher with every learning-resource passage. licenseFilter narrows learning resources: "open" = CC0, Public Domain, CC BY, CC BY-SA, their DINI-AG-KIM license-type equivalents, and free software/documentation licenses (GPL, LGPL, AGPL, GFDL, MIT, Apache 2.0, BSD); "cc-any" = all of "open" plus every other Creative Commons license, incl. KIM non-commercial/no-derivatives (normally no metadataOnlyMatches); "any" = also unlicensed and all-rights-reserved resources, as metadataOnlyMatches. Scope is required but simple: with no source restriction from the user, pass the content kinds (e.g. kinds:[30142] for educational resources, [30040,30041] for publications, [30023] for articles). Route source restrictions ("nur Content von X") into the scope parameters: a metadata publisher (most organisations — resolve_publisher finds the exact spelling) goes into `search` as a QUOTED field filter, e.g. search:'publisher.name:"LEHRE LADEN"' (unquoted multi-word names match nothing); a Nostr signing account (resolve_author → pubkey) goes into `authors`. A published grimoire spell (kind 777, nevent or event id) can replace inline scope entirely; the response carries the canonical spell for whatever scope was used — publish it (e.g. via grimoire) to make the scope reusable. Spells may use $me/$contacts; they resolve to the calling user (pass `me` if the transport is anonymous). Fails rather than widening scope: an empty scope, unreachable relay (relay_unreachable — tell the user to retry), or unreachable index is a typed error, never a silently unscoped search.
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  • Use this when the user asks how visible their product results are in Google web Search, and which product pages carry them. Search Console performance for one search appearance, defaulting to MERCHANT_LISTINGS: the appearance inventory of the property, a daily clicks / impressions / CTR / position timeline in date order, and the top pages (product URLs) behind it; appearances and pages are sorted by clicks, highest first, and CTR is a 0-100 percentage. Search Console merchant listings counts enriched product results in web Search only, so it does not reconcile to one figure with Merchant Center free listings. Not for the Shopping tab, Images, Lens, YouTube or Maps: use get_organic_shopping_performance. Always served from the live Search Console API: search appearance is never stored in the GSC Wizard warehouse, and it can be filtered on but never grouped with another dimension. `hasAppearance` is false when the property has no rows for the requested appearance, and `emptyBecause` is set whenever it has no impressions, which is normal for non-ecommerce sites. The window may span at most 16 months (Search Console retention); a longer one is rejected with bad_request. Date range is optional: omit startDate/endDate to analyze the last 28 settled days (data lags ~2-3 days).
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  • Search existing reviewed evidence questions and fixed-pack topics when you do not know a route ID. Use this for governance, tool permissions, audit/export and evidence-boundary questions. For a known software advisory ID or an exact ecosystem/package lookup, use find_package_advisories instead. Supply query as keywords (maximum 160 characters), or leave it blank to browse. offset is the zero-based pagination offset (default 0, maximum 10000; increase by 20). Returns up to 20 matches with route_id, matched wording and the scoped question; pass a route_id to preview_evidence_answer for the complete free answer and sources. Matching ignores case, punctuation and common question words; it does not validate arbitrary new claims. No match is not a negative verdict. Free and read-only.
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  • Search Lacuna's ML/AI corpus for papers, research directions, authors, venues, institutions, novel research hypotheses, and research resources (code repositories, datasets, models, and demos linked to papers). For novel ML/AI research ideas, use search_type="hypothesis", then get_hypothesis on promising results. search_type accepts all, cluster, paper, author, institution, venue, hypothesis, or resource. Use paper for literature and get_paper to read a result. Use other sources for biographies, news, and non-research web content. Resources: search_type="resource" searches GitHub repositories, Hugging Face datasets/models, and Zenodo records that Lacuna has linked to at least one paper. To find benchmark or evaluation datasets, use search_type="resource" with resource_kind="dataset" (for example query "image classification benchmark" or a dataset name like "ImageNet"). resource_kind accepts codebase, dataset, model, or demo (one value or a list); provider accepts github, huggingface, or zenodo. Both filters require search_type "resource" or "all" (with either filter set, only resources are returned). Each result has an external `url`, a Lacuna `context_url`, and `linked_paper_count`; call get_resource on its id to list the linked papers (with their relationship to the resource), then get_paper on those papers for reported results and numbers. Resource search does not support date_from/date_to, venue, year sorting, or semantic ranking. ranking_profile accepts: - default / lexical (default): production ranking; relevance-sorted paper searches combine lexical and semantic retrieval when fields is unset. - semantic: conceptual paper retrieval; supported for paper and all. - bm25_title_abstract / bm25: lexical paper matching over those fields. sort accepts relevance (default), year_desc, or year_asc. Semantic ranking cannot use year sorting; constrain recency with date_from/date_to instead. date_from and date_to are inclusive YYYY, YYYY-MM, or YYYY-MM-DD bounds. author_id_or_url constrains a paper search to one author. Pass an author ID or Lacuna author page URL; it requires search_type="paper". fields optionally restricts and weights lexical fields, for example "title^4,abstract". Supported names are title, abstract, summary, concepts, name, top_names, and venue, plus description, topics, provider_key, and paper_titles for resources (a resource's title already includes its description, topics, README text, and linked paper titles). Fields must exist on the selected search_type, weights must be within 0 < weight <= 100, and fields cannot be combined with semantic ranking.
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