"A server for interacting with a large language model chat" matching MCP tools:

• nwd_get_clashesA

  Navisworks MCPA

  Detect geometric/logical clashes between two element sets in an already-translated Navisworks model. Uses APS Model Derivative property extraction + same-level proximity heuristics, optionally augmented by VDC rules stored in D1 (table vdc_rules). When to use: when coordinating federated MEP + structural + architectural models for clash review before issuing an RFI; e.g. "find duct vs. beam clashes on Level 3 before the Wed coordination meeting" or "sanity-check the latest MEP revision against structure before releasing for fabrication." Pair with nwd_export_report to produce a deliverable. When NOT to use: do not call on a model whose translation is still "inprogress" — call nwd_export_report first and confirm translation_status == "success"; not a substitute for Navisworks Manage Clash Detective for final sign-off (this is a coordination-stage screen, not a regulatory clash report). APS scopes required: viewables:read data:read (read-only — does not create anything in APS). Rate limits: APS default ~50 req/min per endpoint; Model Derivative metadata/properties endpoints are the bottleneck. Properties response may return 202 "isProcessing" on first call — the worker retries once after 3s. For very large models (>50k elements) the worker caps analysis at 50x50 element cross-compare and 100 reported clashes; re-run with tighter category_a/category_b filters for exhaustive coverage. Errors: 401 APS token expired (transient, retry); 403 missing viewables:read/data:read scope (report, do not retry); 404 URN not found or not translated (prompt user to re-run nwd_upload); 409 not applicable; 422 model translated but property index unavailable — typically means source NW version unsupported or translation partially failed (supported: Navisworks 2015+); 429 rate limit (backoff); 5xx APS upstream (retry once). If properties.data.collection is empty the tool returns clash_count: 0 with a note rather than erroring — the agent should treat that as "model not ready" and retry later. Side effects: none in APS. Reads vdc_rules from D1 when both categories are supplied. Logs usage to D1 usage_log. Idempotent — same inputs on a stable model yield the same clash list.

  ConnectorNo auth

• convert_pdfA

  pdfintactA

  Convert a PDF into structured content (tables, charts, formulas, headings, body text) using a two-stage pipeline (layout detection, then a vision-language model) rather than a single VLM call on the raw PDF -- calling a VLM on a raw PDF directly is a known-unreliable pattern for numeric tables. Measured accuracy (500-page real-world benchmark of government/corporate reports, ~51,000 table values checked): tables 95.2% digit-exact, body text 88.8%. This tool reads PDFs a VLM cannot read directly, including scanned pages and PDFs with corrupted/garbled text layers (common in older Japanese academic PDFs). For scanned Japanese documents the numbers hold up (99.4% on the same benchmark). For scanned Arabic, body text does NOT: characters are dropped mid-sentence and quantities can turn into different quantities, so body blocks from scanned Arabic are always flagged confidence:"estimated" -- tables in the same documents stayed exact in our measurement. Strong on Japanese-language documents specifically; the accuracy figures above were measured on Japanese material and are not a claim about every language. Chart values are extracted but are best-effort estimates (about 52% exact match, excluding axis tick labels) and are always flagged confidence:"estimated" in the result -- do not treat estimated chart numbers as authoritative. This is a PAID, ASYNCHRONOUS, per-page-billed operation: credits are reserved from the caller's PDFIntact balance before processing starts, and the response's _meta.credits_remaining shows the balance right after reservation. Processing takes real wall-clock time (roughly 7 seconds/page; a 500-page PDF takes about 42 minutes including a multi-minute cold start), so this tool returns a job_handle immediately without waiting -- call get_result with that job_handle to poll for completion instead of calling convert_pdf again. Always pass idempotency_key; reuse the exact same value if you retry the same request, otherwise retries can double-charge and double-process. Provide the PDF either as a public https URL (source.type="url", up to ~200MB) or inline base64 (source.type="base64", up to ~20MB) -- prefer the URL form for large files. Requires sign-in (OAuth): this session is not authenticated, so calling this tool will fail until the PDFIntact account is connected and authorized.

  ConnectorNo auth

• upload_assetA

  socialclawB

  Add an image or video to the user's SocialClaw media library so it can be attached to a post. Pass exactly one of: file (a file the user attached in this chat), sourceUrl (a direct public http(s) link to the image or video file, not a web page), or contentBase64 (small files only). Files up to 1 GB are supported through file or sourceUrl. The result is the asset: when status is ready, use its publicUrl as media_link in validate_schedule/apply_schedule. Large videos may come back with status uploading; then call get_asset with the asset id until status is ready (or failed) before scheduling.

  ConnectorNo auth

• request_project_quoteA

  agent-gatewayA

  Creates and immediately sends an external project inquiry to a private Relux Works Telegram chat for human review. Do not call it to test the connector or demonstrate MCP. Build the summary from known conversation context, ask only for missing details, obtain a real user-provided or user-confirmed reply route to the decision maker or an accountable relay agent, and show the complete draft including that route. Before calling, provide https://relux.works/en/privacy-policy/ and obtain explicit user consent. Never invent or infer contact details. The current MCP chat is not a reply route. A human replies within one business day with a recommended package and a fixed-price quote.

  Connector

  Destructive

  No auth

• open_sutta_viewerA

  Tripitaka MCP (hosted)A

  Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the bilara-data English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to *read* a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 **Translating for the user (important):** when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate **from the Pāli as the source, using the English as a semantic guide** — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents. 3. Call this tool with `translations=[{segment_id, text}, ...]` covering ONLY the segments being displayed (never a whole long sutta), `translation_language` (BCP-47, e.g. "th", "es"), and `translation_disclaimer` — one short line IN THE USER'S LANGUAGE saying the translation is AI-generated in this conversation and should be checked against the Pāli/English above. Translations are conversation-ephemeral: nothing is stored server-side; the canon stays Pāli + English only. Translations whose segment_id is not in the displayed window are dropped (reported in `translations_dropped`). Without `around`, shows the sutta from the top (capped for long suttas).

  ConnectorNo auth

• tm_import_rvtA

  Twinmotion MCPA

  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.

  ConnectorNo auth

• tm_render_imageA

  Twinmotion MCPA

  Render a still preview image of the model at a specified resolution by pulling the APS Model Derivative thumbnail (capped at 800x800 by the APS endpoint). Also resolves the camera_preset against model metadata to identify which 3D view it maps to, and applies any stored environment config from tm_set_environment for reference. When to use: when you need a quick visual sanity-check of an imported model (e.g. 'show me what Tower A looks like'), to preview a specific named view before committing to a full UE/Twinmotion render, or to embed a low-res preview in a chat/report. Pair with tm_list_scenes first to discover valid view names/GUIDs. When NOT to use: not for production-quality renders (APS thumbnails are low-res and raster-only; for cinematic output use Unreal Engine Movie Render Queue after FBX/USD export), not for arbitrary custom camera angles (only named views from the source file are resolvable — there is no runtime camera placement API here), not for 2D sheet exports (use tm_list_scenes to find 2D roles and fetch directly). APS scopes required: viewables:read data:read. Hits Model Derivative thumbnail + metadata endpoints only. Rate limits: APS default ~50 req/min per app per endpoint. Thumbnail endpoint is usually fast (<2s) once the model has translated; if called while status='inprogress' it returns no thumbnail. Do not loop-poll this tool — poll the manifest via tm_set_environment or tm_list_scenes instead. Errors: 401/403 = token/scope; 404 = URN not found or thumbnail not yet generated (model still translating — retry after manifest reports success); 409 = n/a; 422 = n/a; 429 = back off 30s; 5xx = APS upstream. Side effects: NONE (read-only on APS). Reads KV env_config_<urn>. Writes a row to usage_log. Idempotent.

  ConnectorNo auth

• nwd_uploadA

  Navisworks MCPA

  Upload a Navisworks file (.nwd/.nwf/.nwc) to Autodesk Platform Services (APS) Object Storage and start an SVF2 translation job so the model becomes queryable by the other nwd_* tools. When to use: at the start of a coordination workflow — e.g. the GC hands off a federated NWD combining MEP + structural + architectural models and the agent needs to stage it for clash review before issuing an RFI, or when a subcontractor publishes a new NWC model revision that must be ingested for weekly BIM coordination. Always the first call in a session for any new model. When NOT to use: do not call for already-translated models (re-use the returned model_id/URN); do not use for raw Revit .rvt, IFC, or DWG — those go through a different MCP. APS scopes required: data:read data:write data:create bucket:read bucket:create viewables:read. The worker acquires a 2-legged client-credentials token; the caller does not supply one. Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation job submission ~60 req/min. NWD bundles can be large (hundreds of MB); the upload PUT and translation can take minutes — translation is asynchronous, poll via nwd_export_report (manifest) with exponential backoff (e.g. 5s, 10s, 30s, 60s) before calling clash/properties tools. Errors the agent should handle: 401 invalid/expired APS token (surface as auth failure — do not retry with same creds); 403 missing scope (report scope gap, do not retry); 404 source file_url unreachable (ask user for a fresh public URL); 409 bucket already exists (handled internally, safe to ignore); 413/422 unsupported Navisworks version — APS Model Derivative supports NWD/NWC from Navisworks 2015 and later (state the unsupported version to the user); 429 rate limited (exponential backoff, retry); 5xx APS upstream (retry once, then surface). Side effects: creates a fresh transient OSS bucket (scanbim-nwd-<timestamp>, 24h TTL) and uploads the file as an object, then POSTs a Model Derivative translation job. NOT idempotent — each call creates a new bucket/URN even for the same file_url. Logs usage to the D1 usage_log table.

  ConnectorNo auth

• scalix_db_text_to_sqlA

  Scalix CloudA

  Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

  ConnectorNo auth

• create_agentA

  WebRun Browser AutomationA

  Create a scheduled agent — a recurring or one-time automation that runs on a timer in a real Chrome browser. Two modes. WORKFLOW MODE: pass workflowId plus a schedule — the platform copies everything else (prompt, starting URL, output contract, model, proxy, policy, files) from the workflow and bakes your variables into the prompt; results and memory stay centralised on the workflow. STANDALONE MODE: omit workflowId and provide name, prompt (a Goal / Ground rules / Stages / Output browsing runbook — see the prompt field description), and a schedule; the connection's environment is used automatically. Ask the user only what it should do and when it should run, in plain language — pick sensible defaults for everything else and state them; never ask about environments or technical settings. Results are delivered to your connected chat and appear in session history.

  ConnectorNo auth

• get_price_estimateA

  BridgeNode MCPA

  Estimate the USDC cost of a chat completion request before paying — free, no payment, no authentication required. Read-only: no state changes and no external calls; the estimate is computed locally from server pricing config, so repeated calls with identical inputs return identical results (idempotent). Use this tool to check the exact price for a given model/mode, messages, and max_tokens before calling the paid chat_completions tool. Provide either mode (auto/eco/premium routing) or model (explicit id, mutually exclusive with mode); one of the two is required — if both are sent, model wins. mode values: auto = cheapest model fitting the context, eco = cheapest available, premium = best model.

  ConnectorNo auth

• search_cars_by_descriptionA

  Voxcars (Portugal)A

  Search Portugal listings by describing the car in plain language, matched against listing text by meaning rather than keywords. Use this for wants that are not filter fields — 'a family SUV good in the snow', 'a low-mileage hybrid for the city', 'a used 4x4 to restore'. Prefer search_cars for entirely structural queries (make/model/budget); use this when the request is descriptive. Combine both by passing structural filters here too — they act as hard constraints while the description only ranks. Results are ordered by relevance, so `total` is the number matching the structural filters, not the number that fit the description.

  ConnectorNo auth

• chat_completeA

  HubrisA

  Run a chat completion on any model in the Hubris catalogue and get the answer back. Full parity with POST /v1/chat/completions — same request shape, same response shape, including tool calls, structured output and reasoning. Streaming is not available here: MCP tool calls are synchronous, and `stream` is ignored if you pass it. This is how you reach a model you cannot reach otherwise — pick an id with models_search, then call it here. Useful for delegating a subtask to a cheaper or a stronger model than the one you are running on. Chat models that draw (Gemini Image, GPT-5 Image) take modalities ["image","text"]; their pictures come back as image content next to the JSON. Voice answers of audio models need streaming and are available through the HTTP API only. The call spends real money from the account balance: it is billed per token at the catalogue price, in rubles. balance_get shows what is left.

  ConnectorAPI key

• generate_blog_outlineA

  Razi ToolsA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• generate_blog_outlineA

  Razi Text GenerationA

  Produce a markdown heading structure for a blog post — title, introduction, numbered sections with subsections, conclusion and an FAQ block. Returns JSON { outline } holding the markdown. It writes the skeleton only, not the article: use generate_text for body prose and humanize_text to rework text that already exists. Paid model call, capped at roughly 1,000 tokens, so a large section count yields thinner sections. 10 calls per minute per IP; identical requests may return a cached outline.

  ConnectorNo auth

• get_build_detailsA

  Glushkov Modelling MCPA

  Read-only. Returns detailed information for one sailing-ship model, including vessel background, technical notes, and build-log parts. Use list_builds to browse or filter the model catalog, and use list_model_slugs when a model slug must be identified from a name or metadata. Use this tool when details for one known model are needed; do not use it for catalog browsing. Without partNumber or partSlug, returns model metadata and a summary list of build-log parts. Set partNumber or partSlug to retrieve one build-log part with complete, untruncated content. If both are provided, partSlug takes precedence. Returns a not-found result if the model or requested build-log part does not exist.

  ConnectorOAuth

• search_articlesA

  Polyblog Multilingual BlogA

  Use this when someone asks to see their blog posts, drafts or published articles, optionally for one blog, language or slug. Returns id, title, language, slug and published state, most recently edited first, 25 per page by default with a cursor. It filters by those fields and does not search the text. Not for reading a post.

  ConnectorOAuth

• search_articlesA

  PolyblogA

  Use this when someone asks to see their blog posts, drafts or published articles, optionally for one blog, language or slug. Returns id, title, language, slug and published state, most recently edited first, 25 per page by default with a cursor. It filters by those fields and does not search the text. Not for reading a post.

  ConnectorOAuth

• get_entity_profileA

  MentionForgeA

  Return a Wikipedia and Wikidata identity card for a company, product, or brand. query is a name, not a URL (URLs are stripped); language picks the Wikipedia host (default en, e.g. es → es.wikipedia.org) and does not translate the query or run listening; omit language unless you need a non-English wiki page; language never changes billing because this tool is free. Use this free grounding pulse when you only need who or what an entity is; never use it for social listening — for cited mentions use research_mentions; for liveness use get_health instead; for price use get_pricing instead. Never charges; hits Wikipedia/Wikidata only; this origin rate-limits discovery pulses; no payment headers.

  ConnectorNo auth

• begin_nodeset_uploadA

  OPC UA NodeSet GateA

  Start one temporary in-memory upload for a large NodeSet2 XML document. Call this once for the old baseline and once for the new candidate when the complete XML documents are too large for audit_nodeset_compatibility. It returns upload_id and max_total_chars. Append that document's chunks in original order with append_nodeset_chunk. Uploads expire after 15 minutes and the service allows at most four active uploads; this tool stores only temporary process memory and does not modify an external system.

  ConnectorNo auth

• list_job_filesA

  AI3D StudioA

  List the files a finished job already has, one per format: the main result plus any alternate formats the model returned (for 3D models, for example glb with fbx or usdz), each with a public download URL. Pass format to find one format. This does not convert: if the format asked for is not there, available_formats says what is, and a model can be exported to other formats from the web app model viewer.

  ConnectorNo auth