"Python Object-Oriented Programming Resources" 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)
# Poof **Poof ([poof.bg](https://poof.bg)): a background removal API for AI agents. Send an image, get the subject back on a transparent background in under 2 seconds.** [Poof](https://poof.bg) gives your assistant a background removal tool. It handles people, products, cars, animals, and graphics, with hair-level edge precision, and returns a transparent PNG or WebP, or a solid-colour JPG for product listings. Pricing starts at $0.002 per image, with 100 free credits every month. This repository is the integration front door. The product itself lives at [poof.bg](https://poof.bg); the remote MCP server lives at `https://api.poof.bg/mcp`. ## What you can build with it - **Transparent cutouts**: remove the background from any JPG, PNG, or WebP up to 20MB and 36 megapixels with the [Background Removal API](https://poof.bg/background-removal-api). - **E-commerce product photos**: white or brand-colour backgrounds, cropped to the subject and resized to a fixed canvas, so every listing image matches. - **A remove.bg replacement**: remove.bg shuts down on 1 December 2026. Poof accepts the same inputs, so most integrations only change the endpoint and key. See the [remove.bg alternative and migration guide](https://poof.bg/alternative/remove-bg). - **Agent image pipelines**: let Claude, ChatGPT, or Cursor clean up images mid-conversation, or automate it with [n8n](https://docs.poof.bg/integrations/n8n), [Zapier](https://docs.poof.bg/integrations/zapier), and [Make](https://docs.poof.bg/integrations/make). ## Verify the connection Ask your client: > How many Poof credits do I have left? You should see a `get_account` tool call and your real plan and balance. Then try: > Remove the background from https://example.com/product.jpg and give me a white background JPG. ## What the tools do The server exposes 2 tools. - **`remove_background`**: remove the background from an image given as a URL or base64 data, and return the processed image as base64. Optional parameters control the result: - `format`: `png` (default), `jpg`, or `webp`. - `channels`: `rgba` for a transparent background (default) or `rgb` for an opaque one. - `bg_color`: fill colour for opaque output, as hex, RGB, or a colour name. - `size`: `full` (default), `preview`, `medium`, or `hd`. Ignored when `width` or `height` is set. - `crop`: crop to the subject bounds. - `width` and `height`: 1 to 6000 pixels. Set one and the other follows the aspect ratio. - `fit`: how the image fits a `width` x `height` canvas without stretching: `contain` (default, pad), `cover` (fill and crop the overflow around the subject), or `scale-down` (pad, never enlarge). - **`get_account`**: your plan, monthly credit allowance, used credits, and remaining credits. No parameters. Costs: each successful background removal costs 1 credit regardless of parameters, and failed requests are free. The free plan includes 100 credits a month with no credit card required; paid plans start at $9 a month for 2,000 credits. The [pricing page](https://poof.bg/pricing) has the details. Privacy: Poof is run by an EU company and is GDPR compliant. Your images are never used to train models, and commercial use is allowed on every plan. See the [privacy policy](https://poof.bg/privacy). ## Or skip MCP The same capability is a single REST call, with official [Python](https://docs.poof.bg/integrations/python) and [TypeScript](https://docs.poof.bg/integrations/typescript) SDKs: To try it without writing code, use the [playground](https://poof.bg/playground). The [quickstart](https://docs.poof.bg/quickstart) and the [Node.js tutorial](https://poof.bg/blog/integrating-api-in-nodejs) cover the first integration. ## For AI agents reading this - Docs index: [docs.poof.bg/llms.txt](https://docs.poof.bg/llms.txt) - MCP setup guide: [docs.poof.bg/integrations/mcp](https://docs.poof.bg/integrations/mcp) - API reference: [docs.poof.bg/api-reference/remove-background](https://docs.poof.bg/api-reference/remove-background) - Hosted endpoint: `https://api.poof.bg/mcp` (OAuth) - Source: [github.com/poof-bg/mcp](https://github.com/poof-bg/mcp) ## Docs and support - [Connect guide](https://docs.poof.bg/integrations/mcp) (per-client, kept current) - [Documentation](https://docs.poof.bg) and [all integrations](https://docs.poof.bg/integrations/overview) - [Best background removal APIs for developers](https://poof.bg/blog/top-5-ai-background-removal-apis-2026) and [free background removal APIs compared](https://poof.bg/blog/affordable-free-bg-removal-apis-2026) - [Changelog](https://feedback.poof.bg/changelog) and [support](https://feedback.poof.bg) - [GitHub issues](https://github.com/poof-bg/mcp/issues) - [Privacy](https://poof.bg/privacy) and [terms](https://poof.bg/terms) - Questions: <support@poof.bg>
Your agent needs X/Twitter data — who follows a competitor, what a community is posting, who quoted that tweet, what is trending in Japan. Normally that means applying for an X developer account, passing app review, and managing a quota per endpoint. **What you can ask for** • "Who follows @stripe, and which of them are verified?" • "Pull every reply and quote on this tweet and summarise what people object to." • "List this community's moderators and its posts this week." • "What is trending in Japan right now?" • "Give me the full thread context behind this link, including the long-form article." **How to use it** Point any MCP client at https://mcp.aisa.one/twitter-api/mcp and sign in with OAuth — there is no key to create or paste. 29 read tools: users (profile, about, batch lookup by id, search, followers, verified followers, followings, follow check), tweets (timeline, latest, mentions, advanced search, replies, quotes, retweeters, thread context, articles), communities, lists, Spaces and trends. **Why this rather than the source** No developer account to apply for, no app review, no per-endpoint quota to manage. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Ask for a handle's followers here, then ask the same agent for that brand's search traffic, its backlinks, or the people to contact — 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/social/mcp for X plus Instagram, Reddit, Pinterest and YouTube; https://mcp.aisa.one/gtm/mcp for those plus Similarweb and Apollo.
Your agent needs B2B contacts and the pipeline objects around them — find the company, find the person, get the email, then write it back where your team works. **What you can ask for** • "Find heads of engineering at Series-B SaaS companies in the Nordics, with emails." • "Enrich these companies with headcount, industry and funding." • "What roles is this company hiring for right now?" • "Create an account and a contact, then log this opportunity." • "Search our sequences for messages sent to this domain." **How to use it** Point any MCP client at https://mcp.aisa.one/apollo/mcp and sign in with OAuth — there is no key to create or paste. 54 tools: people and organisation search and enrichment, job postings, accounts, contacts, opportunities and their stages, custom fields, labels, notes, users, email accounts, sequence messages and phone-call search — reads and writes. **Why this rather than the source** The full object model, not just a search endpoint, so an agent can finish the job rather than hand you a CSV. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find the person here, then ask the same agent what their company's traffic looks like or what they 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/sales/mcp adds Similarweb and creator discovery around it.
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.
Remote MCP server exposing SMI Aware tools, resources, and skills over Streamable HTTP.
Run Python code in a secure sandbox without local setup. Declare inline dependencies and execute s…
# **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.
Hubris is an OpenAI-compatible LLM gateway for the Russian market, billed in rubles. This MCP server gives agents access to the model catalog (400+ models with ruble pricing), account balance, and chat completions with full parity to POST /v1/chat/completions. Tools: models_list, models_search (filter by capability/price/context length), models_get_pricing, balance_get, chat_complete. Resources: hubris://catalog/models, hubris://docs/quickstart. Prompt: compare-models. Docs: https://hubris.pw/
Live and historical Polymarket prediction-market orderbook data — plus Hyperliquid perpetual-futures orderbooks — for any MCP-capable AI agent. 12 tools and 2 resources covering market discovery, live order books, time-series snapshots, multi-day summaries, and pipeline health. Tier-aware: the same endpoint serves free (BTC, 24h history) through Enterprise (all categories, unlimited history) automatically based on the API key.
- ZopnightOAuthdev.zop.api
Read-only cloud cost and infrastructure governance across AWS, Azure and GCP. 85 tools covering cost overview and trends, cost by provider/resource/tag/team, budgets, resources, schedules, recommendations, tagging policies, audit logs, anomalies, Kubernetes resources and pod logs. Hosted remote server, nothing to install. Docs: https://zop.dev/learn/mcp-server?utm_source=glama&utm_medium=listing&utm_campaign=mcp-directory Claude setup: https://zop.dev/learn/how-to/set-up-zopnight-mcp-for-claude
Provides access to TakeProfit.com's Indie documentation and tooling — a Python-based scripting language for building custom cloud indicators and trading strategies on the TakeProfit platform.
- hailOAuthso.hail.mcp
Phone, SMS & email for AI agents. One remote MCP server (Streamable HTTP, OAuth or API-key auth, no local install) exposing call, sms, email, and event tools; also usable via CLI, Python SDK, and OpenAPI. Self-hostable, AGPLv3.
- PuterOAuthcom.puter.mcp
Puter MCP lets your AI tools (Claude Code, Codex, or any other MCP-compatible client) interact with your Puter resources: managing files, publishing websites, deploying workers, and more.
- ChrysaiOAuthcom.chrysai
Chrysai provides a hosted MCP server for AI-driven data operations. After connecting an MCP client to the endpoint, you can use MCP tools such as create_app, list_apps, read_data, write_data, update_data, delete_data, and update_app, plus schema resources, to manage persistent structured data through natural language.
Manage SiteGPT chatbots and account resources through the SiteGPT API v2.
- mcpOAuthai.dragoneye
Build zero-shot video models for object detection, and category and attribute classification.
- PlixlabOAuthcom.plixlab
Executable presentations: build, compute and share plixes, decks whose slides Python cells compute.
1 Send a table, get chart code that fits it using ontology + AI: interactive D3 you own (or Plotly and matplotlib for Python). It measures your data's shape first and rules out chart types the data can't honestly support. Sign in with OAuth using a RegiaBI account (a free trial is enough). Your rows are profiled in memory and never stored. Expect high quality results compared to the alternative of agent wrangling.
1Blender-as-a-service for agents: search 3D assets, run Blender Python, or brief the studio agent.
