"Python Object-Oriented Programming Resources" matching MCP connectors:
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Crawl and scrape websites to clean markdown, and track what changed between crawls.
Official MCP server providing AI assistants with direct access to Stimulsoft Reports & Dashboards developer documentation. Enables semantic search across FAQ, Programming Manual, Server Manual, User Manual, and Server/Cloud API references across all Stimulsoft platforms (.NET, WPF, Avalonia, WEB, Blazor, Angular, React, JS, PHP, Java, Python).
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.
Can I use this URL for RAG, summarize it, train on it or index it? Check observed RSL declarations and machine-readable usage/licensing terms before processing supported content. Use acqpath_capabilities first; acqpath_rights_quote prepares a private quote. Stable HTTP: https://api.getacqpath.com/v1/rights/preflight — no AcqPath account or API key. Fresh 0.02 USDC, deep 0.05 USDC on Base; verified signed offer authoritative. Public payment uses acqpath-request-binding nonce support or the configured AcqPath SIWX adapter for official TS/Python x402 signers. Generic zero-config clients remain unsupported; mainnet paid E2E is unverified. No paid crawl purpose. UNKNOWN is not permission; no license, legal clearance or legal advice. The legacy acqpath_quote routing tool is disabled. Keep private claims outside model context. Integrate: https://developers.getacqpath.com/from-glama
Search German open educational resources (OER) via AMB/schema.org metadata, open licenses only.
Read-only facts about Beacon by EVAA: what it is, its offers and its AI search resources.
Infrastructure layer for agentic commerce. Measure website AI readiness, connect to agentic commerce protocols (MCP, UCP, AP2, GEO), and search resources.
Read-only search and Markdown access to liz's public docs, prompts, resources, and an MCP App.
Search and resolve Naturepedia, Robbie's Razor, and GC-MRD-v2.0 canonical resources.
dev.to developer blog & tutorial feed. $0.01/query. Register in-session — free testnet funds.
Search curated Lorcana resources, evidence, taxonomies, and public change history.
Search AI-native jobs, inspect application forms, and fetch free interview-prep resources.
Remote MCP server exposing SMI Aware tools, resources, and skills over Streamable HTTP.
The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
Interact with your Google Bigtable resources using natural language commands.
Search 585+ free cancer support resources across Washington State in 7 languages
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Interact with your Google Cloud Firestore resources using natural language commands.
# **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.
PyPI MCP — wraps the Python Package Index (PyPI) JSON API (free, no auth).
