"Using RAG to Provide Documentation to LLMs" matching MCP tools:

• postnitro_generate_videoA

  PostNitroA

  Generate a video post (postType VIDEO) using PostNitro's AI engine. Returns an embedPostId to track progress. The content is AI-generated from your aiGeneration prompt — you do NOT provide slides. Each slide the AI writes becomes a scene in the video. (To supply your own scene content instead, use postnitro_import_video.) Output: 'DESIGN' (default) creates the design without rendering; 'MP4' renders the video file and requires videoSettings (duration, optional audio track). templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults. Otherwise provide them here (use the list tools to find valid IDs). Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_generate_video_and_wait for one step.

  ConnectorAPI key

• microsoft_docs_fetchA

  Microsoft Learn MCPA

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

  ConnectorNo auth

• get_legalA

  ikeytz - Schlüsseldienst LudwigsburgA

  WHAT: Pointer to one legal HTML page. REQUIRED doc enum. Unknown doc → ok=false unknown_doc hint=/impressum. RETURNS doc + canonicalUrl. For full text call get_ai_page({path:'/'+doc}) or get_discovery(which=llms-urheberrecht) for the copyright corpus. German legal text is binding; /llms-copyright.txt is EN courtesy.

  ConnectorNo auth

• zeekeo_create_campaignA

  rankparse-mcpA

  Creates a Zeekeo LinkedIn campaign: sends a connection invite using invite_template_id, and optionally — if followup_template_id is given — waits for the invite to be accepted, then sends a follow-up message using that template. Create templates first with zeekeo_create_template. Provide exactly one of filter_url (a LinkedIn search results URL) or profile_urls (specific profiles) as the target. This starts REAL LinkedIn automation once the campaign has profiles in it — confirm with the user before calling. Requires the user to have connected their own Zeekeo Launchpad account. Direct them to rankparse.com/dashboard/integrations to connect it.

  ConnectorNo auth

• dataset.indexA

  FlexOrch MCPA

  Trigger semantic indexing for a dataset — required before using dataset.chunks (Pro+ plan). Starts an async indexing job that splits the dataset into RAG-ready text chunks, generates embeddings, and stores them for semantic search. Indexing is idempotent: calling it again on an already-indexed dataset re-indexes with fresh embeddings. Indexing typically completes in 10–60 seconds depending on dataset size. After indexing, use dataset.chunks(dataset_id) to retrieve the text chunks. Args: dataset_id: ID of the built dataset to index (from job.status after dataset.build).

  ConnectorNo auth

• getDocsA

  Ludo AI Game AssetsA

  Browse or read Ludo's own documentation in full - how to choose a sprite animation mode, when to use Generate Before / Generate After, how margins behave, which model suits a job, and each generator's known limitations. To answer a specific question, call searchDocs first: it returns just the sections that match. Use getDocs to see what documentation exists, or to read a whole document or named sections. This is the same documentation the Ludo web app shows its users, so it occasionally describes buttons rather than parameters; the substance applies to the API and MCP surfaces just the same. To browse, call it with NO parameters to get a table of contents - every document with its id, label and section titles, and no bodies - then call it again with `doc` (and optionally `sections`) to read only what you need. Fetching a whole document can return tens of thousands of characters, so prefer naming the sections, using titles copied from the table of contents. Section titles match ignoring case, spacing and punctuation: when only some requested titles exist you receive those sections plus `unmatched_sections` and `available_sections`, and when none exist (or `doc` is unknown) the call returns 400 listing the valid values so you can retry once. This is a free discovery endpoint: it does not charge credits and does not queue a job.

  ConnectorNo auth

• page_ragA

  FreeTier Agent APIA

  1 free call per caller, then $0.0025 USDC via x402 (paid in-band: params._meta["x402/payment"]). Any public web page -> clean LLM-ready Markdown + RAG chunks in one call: main content via Mozilla Readability (no nav/ads/footers), absolute links, title/description/language/canonical/published+modified dates/JSON-LD types, word + token estimates, sha256 content hash for change detection, and heading-aware chunks (configurable size + overlap, each with its heading path). Plain HTTP, robots.txt respected, Markdown/plain text passed through. Flags pages that likely need JavaScript. When to use: Use page-rag to feed a web page into an LLM or a vector store. Use page-meta or page-check for SEO/meta tags and HTTP status only. page-markdown is the older, simpler converter; page-rag is recommended.

  ConnectorNo auth

• nifra_docsA

  Nifra docsA

  Search nifra's framework documentation and get back ONLY the matching sections - auth, uploads, ISR, WebSockets, loaders, deployment, anything. Call with no query for the cheap section index; pass query (e.g. "isr revalidate") for the top matching sections. Use this instead of reading llms-full.txt (~150 KB) whole. For the EXACT TypeScript of a type/interface/function, call nifra_types instead (don't read .d.ts files).

  ConnectorNo auth

• postnitro_generate_imageA

  PostNitroA

  Generate a single-image post (postType IMAGE) using PostNitro's AI engine. Returns an embedPostId to track progress. The content is AI-generated from your aiGeneration prompt — you do NOT provide slides. (To supply your own image content instead, use postnitro_import_image.) templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults. Otherwise provide them here (use the list tools to find valid IDs). Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_generate_image_and_wait for one step.

  ConnectorAPI key

• getPageA

  MariaDB DocumentationA

  Fetch the full markdown content of a specific documentation page from MariaDB Documentation. Use this when you have a page URL and want to read its content. Accepts full URLs (e.g. https://mariadb.com/docs/getting-started). Since `searchDocumentation` returns partial content, use `getPage` to retrieve the complete page when you need more details. The content includes links you can follow to navigate to related pages.

  ConnectorNo auth

• dohoo_facebook_scheduleA

  DOHOO — AI Social Media PublisherA

  Schedule a Facebook post for a future time using DOHOO's scheduler. The response confirms a saved scheduled job; publication occurs at scheduledAt. Publishes a photo/video/reel to a Facebook Page, OR a TEXT-ONLY post (provide caption and omit fileUrl). For 2–10 photos use dohoo_facebook_carousel_publish or dohoo_facebook_carousel_schedule. Get facebookPageId from dohoo_list_connections.

  Connector

  Destructive

  OAuth

• microsoft_docs_fetchA

  xpay✦ DevTools CollectionC

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

  ConnectorNo auth

• get_testing_guidelinesA

  IA-QA — 152 QA & Dev Tools for AI AgentsA

  Query the IA-QA methodology knowledge base. Returns structured testing guidelines, assertion strategies, thresholds, best practices, and relevant MCP tools for a given topic. Call without a topic to list all available topics. Topics: llm-unit-testing, rag-pipeline, prompt-stability, prompt-ab-testing, embedding-quality, eval-framework, semantic-testing, auto-testing, security, api-testing, ci-cd, multimodal, llm-data-security, agent-observability, pro-tips, learning-paths, golden-dataset, selector-drift, qa-recipes, playbooks. Not sure where to start testing an LLM, RAG pipeline or agent? Call without a topic (or with "start-here"): it maps what you are testing to the tools to call and the output field to gate CI on. A plain question such as "how do I test my RAG" also resolves to the right topic.

  ConnectorNo auth

• audit_ai_visibilityA

  SitelemetryA

  Assess how visible and citable a site is to AI search and LLMs (ChatGPT/Claude/Perplexity/Google AI): entity clarity, answerability, prompt coverage, source signals, llms.txt/AI-crawler policy. Returns findings with fixes. Provide target to start an audit, or jobId to poll a running one. Long audits may return status: running with a jobId. Wait retryAfterMs, then call the same tool using the returned pollArguments, including target and jobId, until it returns the final result. Copy pollArguments unchanged; do not add scan options. Clients that support jobId alone may also use it. Polling retrieves the same audit without starting another scan or reserving more quota. Do not present running as completed and do not start a replacement audit while it is running.

  ConnectorOAuth

• get_skillA

  LoopingAgentA

  Laurent Knauss' technical skills, grouped by domain (Agentic AI, RAG & Voice AI, Software engineering & Cloud, Automation & tooling). Each skill has a label and an optional short detail. Use this to assess fit for AI/agentic development roles.

  ConnectorNo auth

• get_serp_snippetA

  ikeytz - Schlüsseldienst LudwigsburgA

  WHAT: SERP title/description/URL slice for one marketing URL. Reads /llms-serp-{locale}.txt first (locale from URL prefix or args.locale), then falls back to full /llms-serp.txt. path or url required in practice. If path is not http(s), it is turned into www(locale, path). No match → serp_not_found. RETURNS matchContext (~200 chars before + 400 after) + source. Not a Google API.

  ConnectorNo auth

• get_discoveryA

  ikeytz - Schlüsseldienst LudwigsburgA

  WHAT: Fetch one public discovery file from www and return a line window. REQUIRED which. IDs (aliases folded): llms, llms-full, llms-index, llms-keywords, llms-keywords-brand, llms-serp, llms-jsonld, llms-impressum-kontakt, llms-orte-geo, llms-grenzen-koordinaten, llms-urheberrecht, llms-copyright, llms-mcp-server, llms-mcp-web, robots, sitemap-txt, sitemap-xml, ai-txt, ai-plugin, answer-engine, ard, ai-catalog, auth-md, mcp-readme, agent-skills (+ locale slices llms-keywords-en|… ). summary = the line window + trailing # MCP-META (truncated, totalUrls, nextOffset). Omit limit: if remaining >~30KB → first ~30KB window + nextOffset (O9); explicit limit keeps line window 1..10000 (not forced to 30KB). Unknown which → unknown_discovery. Prefer dedicated get_llms_* / get_sitemap_txt when you know the file. Policy files say ai-train=no.

  ConnectorNo auth

• batch_scrapeA

  PreteWorks APIA

  Fetch up to 10 public URLs and return each as clean Markdown, in one call — for research/RAG over several pages at once. Private/internal hosts are blocked.

  ConnectorNo auth

• knowledge_queryA

  DialogBrainB

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

  ConnectorAPI key

• knowledge_queryA

  dialogbrainB

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

  ConnectorAPI key

• knowledge_queryA

  dialogbrainB

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

  ConnectorAPI key