"Understanding Structured Thinking" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
# Supadata **Supadata ([supadata.ai](https://supadata.ai)): video transcripts, social media metadata, AI video analysis, and web scraping for AI agents, behind one MCP server.** [Supadata](https://supadata.ai) turns YouTube, TikTok, Instagram, X (Twitter), and Facebook videos, plus any web page, into clean text and structured JSON that an LLM can use. Your agent passes a URL and gets back a transcript, metadata, extracted data, or Markdown. One account, one response format across every platform. This repository is the integration front door. The product itself lives at [supadata.ai](https://supadata.ai); the remote MCP server lives at `https://api.supadata.ai/mcp`. ## What you can build with it - **Video to text**: fetch transcripts through the [YouTube Transcript API](https://supadata.ai/youtube-transcript-api), [TikTok Transcript API](https://supadata.ai/tiktok-transcript-api), [Instagram Transcript API](https://supadata.ai/instagram-transcript-api), and [X (Twitter) Transcript API](https://supadata.ai/twitter-transcript-api). When a video has no captions, the [AI Transcription API](https://supadata.ai/video-transcript-api) generates one, and it works on direct video file URLs too. - **Video and channel metadata**: titles, descriptions, authors, views, likes, and comments as clean JSON, a simpler [YouTube Data API alternative](https://supadata.ai/youtube-api) that also covers TikTok, Instagram, X, and Facebook. - **Structured data from video**: give a prompt or a JSON schema and the [Video Analysis API](https://supadata.ai/video-analysis-api) returns structured output that accounts for visuals, audio, and context. - **Web context**: scrape any page to Markdown, map a site's URLs, or crawl a whole site for RAG and research. See the [web scraping docs](https://docs.supadata.ai). ## Verify the connection Ask your client: > Get the transcript of https://www.youtube.com/watch?v=dQw4w9WgXcQ and summarize it in three bullet points. You should see a `supadata_transcript` tool call and a real summary. ## What the tools do The server exposes 9 tools in four groups. - **Transcripts**: `supadata_transcript` (get a transcript from a YouTube, TikTok, Instagram, X, or Facebook video URL, or from a direct file URL; uses existing captions when available and falls back to AI transcription when there are none), `supadata_check_transcript_status` (poll an asynchronous transcript job by its job ID; long videos return a job instead of an immediate result). - **Metadata**: `supadata_metadata` (title, author, and engagement stats for a social media post or video). - **Video analysis**: `supadata_extract` (start an AI structured-data extraction job for a video, driven by your prompt or schema), `supadata_check_extract_status` (poll an extraction job by its job ID and read the extracted data). - **Web**: `supadata_scrape` (scrape a web page and return its content as Markdown), `supadata_map` (map a website and return the list of URLs found), `supadata_crawl` (start a crawl job for a whole site), `supadata_check_crawl_status` (poll a crawl job by its job ID and read the crawled pages). Transcripts of long videos, extractions, and crawls are asynchronous: the first call returns a job ID, and you read the result with the matching `check_*_status` tool. Costs: tools spend your regular Supadata credits at the same prices as the API. A native transcript costs 1 credit, an AI-generated transcript costs 2 credits per minute of video, and checking job status is free. The free plan includes 100 credits a month with no credit card required. The [pricing page](https://supadata.ai/pricing) has the details. ## Or skip MCP The same capabilities are available as a plain REST API with [TypeScript and Python SDKs](https://docs.supadata.ai/integrations/overview) and no-code integrations for [Make, Zapier, n8n, and Active Pieces](https://supadata.ai/nocode). To try it without writing code, use the [playground](https://supadata.ai/playground) or the free tools: [YouTube Transcript Generator](https://supadata.ai/youtube-transcript), [TikTok Transcript Generator](https://supadata.ai/tiktok-transcript), [Instagram Transcript Generator](https://supadata.ai/instagram-transcript), [Facebook Transcript Generator](https://supadata.ai/facebook-transcript), and [Video Transcript Generator](https://supadata.ai/video-transcript). ## For AI agents reading this - Docs index: [docs.supadata.ai/llms.txt](https://docs.supadata.ai/llms.txt) - MCP setup guide: [docs.supadata.ai/integrations/mcp](https://docs.supadata.ai/integrations/mcp) - Hosted endpoint: `https://api.supadata.ai/mcp` (OAuth) - Source: [github.com/supadata-ai/mcp](https://github.com/supadata-ai/mcp) ## Docs and support - [MCP landing page](https://supadata.ai/mcp) and [connect guide](https://docs.supadata.ai/integrations/mcp) - [Documentation](https://docs.supadata.ai) - [Best YouTube Transcript API](https://supadata.ai/blog/best-youtube-transcript-api) (comparison guide) - [Status](https://status.supadata.ai) and [changelog](https://feedback.supadata.ai/changelog) - [GitHub issues](https://github.com/supadata-ai/mcp/issues) and [support](https://feedback.supadata.ai) - [Privacy](https://supadata.ai/legal/privacy) and [terms](https://supadata.ai/legal/terms)
XMemo is a user-owned Memory OS for AI agents, providing a shared, persistent memory layer across AI assistants, IDEs, CLIs, tools, projects, and sessions. It enables ChatGPT, Claude, Codex, Cursor, Gemini, and other supported AI clients to access authorized long-term context without requiring users to repeatedly explain their preferences, project decisions, or previous work. Beyond basic memory storage and retrieval, XMemo supports semantic search, contextual recall, memory updates and corrections, source attribution, version history, project-scoped context, task tracking, and governed memory lifecycle management. Identity-aware access controls, scoped authorization, and memory isolation help users manage which agents and workflows can access their information. XMemo also provides advanced capabilities for structured knowledge, reusable procedures, and memory consolidation through its broader Memory OS platform. Connect through hosted MCP with OAuth or bearer-token authentication, or integrate directly through REST APIs and supported client tools. Memory remains available across authorized clients and sessions, with user-controlled access, export, and deletion. Website: https://xmemo.dev Documentation: https://xmemo.dev/docs
LinkedIn data for AI agents: structured profiles, people search, companies, and posts over MCP or REST. 500 free credits, no card.
HR and recruiting: score a job description, or generate a structured role profile. No API key.
Your agent needs company financials it can compute on — statements, ratios, earnings, estimates, filings and insider activity as structured data, not a PDF. **What you can ask for** • "Give me 8 quarters of income statement, balance sheet and cash flow for this ticker." • "What do analysts estimate for next quarter, and how did the last four surprise?" • "Find this exact line item across every filing." • "Who bought or sold as an insider in the last 90 days?" • "Screen for profitable companies under this valuation with growing revenue." **How to use it** Point any MCP client at https://mcp.aisa.one/marketpulse/mcp and sign in with OAuth — there is no key to create or paste. 21 tools: prices and snapshots, income statements, balance sheets, cash-flow statements, financial metrics and snapshots, earnings, analyst estimates, company facts, filings and filing items, line-item search, a screener, insider trades, macro interest rates, news, plus EDINET documents and filing digests for Japanese issuers. **Why this rather than the source** Statements as fields you can compute on, and a screener in the same place. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the fundamentals here, then ask the same agent what social is saying about the ticker — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/finance/mcp for equities, crypto and prediction markets in one place.
Turn documents into structured data with Extend: parse (OCR to markdown), extract fields, classify, split multi-document bundles, and fill PDF forms.
Your agent needs to crawl a site and say what is wrong with it — broken tags, duplicate content, pages nothing can index, resources that never load. **What you can ask for** • "Crawl this site and list every page with a duplicate title or missing description." • "Which pages are non-indexable, and why?" • "Run Lighthouse on these URLs and give me the failing audits." • "Show the internal link graph and the orphan pages." • "Give me this page's raw HTML and its microdata." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-onpage/mcp and sign in with OAuth — there is no key to create or paste. 20 tools: submit a crawl and read its summary, pages, resources, links and waterfall; duplicate content and duplicate tags; keyword density; non-indexable and uncrawlable resources; parsed content, raw HTML, microdata, screenshots and Lighthouse. **Why this rather than the source** A crawler you drive from the agent, with the audit results as structured data rather than a PDF. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find the broken pages here, then ask the same agent what those pages used to rank for — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo/mcp for all of it at once — rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
- Comment.ioOAuthio.comment
Comment.io lets people and trusted agents work on the same files. Review edits and comments in the browser, manage structured data with DocDB, and build apps over your workspace. Connect your preferred agent through a single run tool using OAuth and Streamable HTTP.
Provide structured access to ClinicalTrials.gov data for searching, retrieving, and analyzing clin…
AI takeoff for construction: turn PDF blueprints into structured, traceable quantities.
Zyte MCP is a remote MCP server that lets an agent use Zyte on your behalf. It can download pages with a plain HTTP request or in a real browser, click and type and take screenshots, and pull structured data out of a page with AI, either as a standard type like product, article or job posting or as fields you describe in plain words. It can also search the web from a chosen country, check whether Zyte API supports a site and what a request costs, and report your usage by domain. If you have Scrapy Cloud projects, the same server can list spiders, start and cancel jobs, read logs and items, manage schedules and settings, and read and write collections. Those tools can change and delete things, so review what your agent asks to do. Connect to https://mcp.zyte.com/v1/mcp and sign in to your Zyte account with OAuth. Using the server is free. What your agent does through it is billed at Zyte API prices, on a dedicated mcp_access_key you can track and cap. New accounts get $5 of trial credit for a month.
Twistly turns your ideas, notes, and documents into ready-to-edit PowerPoint presentations. Describe a topic, paste in text, or share a file (PDF, DOCX, PPTX, or TXT), and Twistly builds a structured deck of up to 50 slides with clear layouts and matching visuals in seconds. Pick from 50+ templates to keep a consistent, on-brand look. Everything comes out as a native, fully editable .pptx file, not a web export, so it opens cleanly in PowerPoint with no broken layouts. Edit it by hand like any normal presentation, or use the Twistly add-in for PowerPoint to keep refining and redesigning your slides. Nothing stays locked in a separate editor. Generation runs asynchronously: a tool call returns a job id, the assistant follows the job, and the finished deck arrives as a download link in the chat. Used by students, educators, business professionals, and teams around the world.
# **RChilli MCP Hub** RChilli MCP Hub is a production-grade MCP server that exposes RChilli's full HR data intelligence platform as 17 AI-callable tools across 4 categories. Built on 15+ years of HR data intelligence, it is trusted by ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. Every tool is read-only and returns a consistent, structured JSON response — no raw exceptions, no inconsistent formats. <br> --- <br> # **Tools — 17 Total** userkey and subuserid are injected automatically from your Bearer token — you never need to pass them manually. <br> --- <br> # **🔍 Resume & Job Description Parsing — 3 tools** <br> > ### **`extract_resume_data`** > > Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles with contact details, skills, experience, education, certifications, and taxonomy-enriched data for ATS, HCM, and AI recruiting workflows. When used on a careers page or application form, the same extraction call auto-fills every application field in under 10 seconds — documented to increase candidate conversion by up to 194%. Supports 40+ languages with English-normalized output for global intake, and runs in batch mode to process legacy databases or migration backlogs overnight at scale. Also supports resume reprocessing — re-running previously extracted resumes through the latest extraction logic and taxonomy version to bring older records up to current data quality, without requiring a new document from the candidate. Distinct from bulk import (first-time extraction of a new batch) and from talent data refresh (re-enrichment from a newer submitted resume). <br> > ### **`extract_resume_data_from_url`** > > Accepts a direct URL to a PDF, DOCX, or RTF file and returns the same normalized JSON profile as the Resume Data Extraction tool. Ideal for pipeline automation where resumes are stored in cloud storage, S3, or email attachments. Also supports the same auto-fill, multilingual, and batch-processing capabilities as the core extraction tool for URL-based intake sources. <br> > ### **`extract_job_data`** > > Extracts and converts job descriptions into structured hiring data including job title, required skills, preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements for recruitment automation and candidate matching. <br> --- <br> # **🧠 Skills & Job Taxonomy — 4 tools** <br> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, all aliases, related skills, proficiency levels, and O*NET/ESCO mappings. Use when you need the complete record rather than a ranked search. <br> > ### **`lookup_job_profile`** > > Returns authoritative detail for a known job profile including canonical title, SOC/O*NET code, job family, typical required and preferred skills, salary bands, and work context. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string (min 2 chars) and returns up to 10 ranked autocomplete suggestions with canonical names and categories. Prevents free-text entry errors and keeps skill data clean at point of entry. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title string and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to taxonomy profiles from the moment a recruiter starts typing. <br> --- <br> # **🛡️ Redaction, Documents & Utilities — 7 tools** <br> > ### **`redact_resume`** > > Redacts personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. Configurable redaction scope. Idempotent. <br> > ### **`reformat_resume_with_template`** > > RChilli's Resume Reformatting tool accepts any structured candidate profile and applies one of six branded templates (TM001–TM006) to produce a consistently formatted output document in PDF, DOCX, RTF, or HTML — ensuring every candidate is presented in a standardized, professional layout regardless of how their original resume was structured. Designed for staffing firms, recruitment agencies, and enterprise HR teams who need to control candidate presentation at scale, it eliminates manual reformatting effort and enforces brand consistency across all submissions. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Preserves formatting fidelity. Useful as a pre-processing step before data extraction on non-standard file types. <br> > ### **`tag_entities`** > > RChilli's Named Entity Recognition tool takes already-extracted HR text and annotates it by wrapping each recognized entity in a structured XML-style label inline — returning output such as `<job_title>Senior Data Engineer</job_title>`, `<skill>Python</skill>`, `<city>Austin</city>`, `<degree>Bachelor of Science</degree>`, and `<organization>Google</organization>` — covering 10+ HR-specific entity types including person name, state, country, date, and year. Unlike data extraction tools that produce separate field lists, tag_entities preserves the full original text structure with entities labeled in place, making the output immediately consumable by ATS field-mapping pipelines, candidate profile builders, and content annotation workflows without any offset calculation or post-processing. <br> > ### **`extract_contacts`** > > Identifies and structures names, emails, phone numbers, LinkedIn URLs, and addresses with field-level confidence scores from candidate records, emails, or documents. Safe for GDPR/CCPA workflows. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Enables radius-based candidate and job search and supports workforce planning analytics. <br> > ### **`classify_job_zone`** > > RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its O/*NET Job Zone — one of five standardized levels ranging from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium preparation), Zone 4 (considerable preparation), to Zone 5 (extensive preparation required) — based on the education, experience, and training criteria defined by O/*NET. The returned Job Zone level enables downstream workflows such as candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization without any manual O/*NET lookup. <br> --- <br> # **🎯 Search & Matching — 3 tools** <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one Job Description (no index required) and returns an overall match score, dimension scores, skill gap list, and natural-language explanation. Bias-controlled and audit-ready. <br> > ### **`find_matches_in_index`** > > Accepts a resume or Job Description as input and returns the top-N most similar documents from the indexed corpus ranked by semantic similarity. No index setup required for the input document. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant's pre-populated index. Supports Boolean and semantic search modes. Requires documents to be indexed before use.
Gnosari is a free AI chatbot for your website that collects data. Visitors chat, and every conversation becomes a typed, structured record: name, email, budget, timeline, whatever you asked for. The Gnosari MCP server lets Claude, ChatGPT, Cursor or any MCP client create and run your Gnosari chatbots through natural conversation. Create Gnosaris, define what data to collect, share conversation links, and review the structured data your conversations capture, without leaving your AI chat. Everyt
SEC filings, financial statements, metrics, insider and institutional holdings as structured data
A structured financial modeling layer for AI agents. Build, version, and audit financial models without drift, then export to Excel, from Claude or any MCP client. Learn more: https://layerz.cc/for-agents
ShaBaas Pay MCP lets AI agents securely create PayTo agreements, initiate payments, and check statuses via structured tools. Authenticate with your ShaBaas Pay API key and control tool access from the dashboard.
A portable context layer for MCP-speaking AI clients. Connect any client to one endpoint and it boots with your containers — structured context, live work state, and accumulated knowledge — carried across every client you use.
Structured web data from 38 platforms: Google, ChatGPT, Gemini, YouTube, Amazon, Reddit, TikTok
Video transcripts, metadata and structured data from YouTube, TikTok, Instagram, X; web scraping.
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