"ManuS Invitation Code" matching MCP connectors:
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Search public open-source code, documentation, metadata, vulnerabilities, changelogs, and examples.
Scores code against a repo's own incident history, rolled into an 8 index Production Reliability Index (PRI). Exposes suggest_code, save_recommendation, and get_scan_report as tools.
Verify identities, screen for AML/PEP risk, and detect fraud across Africa, Asia, Europe, and the Americas — directly from your AI assistant, with no custom integration code required.
Build product adoption your way - Product adoption infrastructure no-code or all-code.
IconScout MCP lets AI coding tools like Claude, Cursor, VS Code, and Devin pull icons, illustrations, 3D assets, and Lottie animations directly while building. The difference from plain asset search is that it picks assets that match the style of the site or UI you're working on, so everything stays visually consistent.
Compliance frameworks delivered to AI agents. Nizh gives your agent read access to your organization's compliance program — SOC 2, ISO 27001, CMMC 2.0, NIST, and more — as MCP tools, so it can check posture, read a control's objectives before changing code, and record where evidence lives.
# **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.
Join.cloud is Slack for AI agents. Create rooms, invite agents, and let them collaborate in real-time. Agents communicate via broadcast messages or DMs, and each room is a standard git repository for code collaboration (clone/push/pull).
AgentiSend is a transactional email API: send through verified domains with a message log, budgets, a preflight and refusals that name the fix; built so AI agents can send on your behalf. The hosted MCP server at https://api.agentisend.com/mcp exposes 82 tools (44 on tools/list; the rest via list_more_tools). A send is one message or a batch of up to 500 items with per-item results. preflight_email runs every gate a real send runs, sends nothing, and costs nothing. Every 4xx returns code, message, and fix. There is no tool for unsolicited mail. Auth is a bearer API key or OAuth 2.1 with dynamic client registration. Starter prompts: "Send this receipt to the customer." "Preflight this send and tell me what would stop it." "What can I spend today?"
Onymu is an MCP server for domain name search, domain availability checking, and social media username lookup. Check a domain name against hundreds of TLDs in one call, including .com, .io, .ai, .co, and country-code extensions. Generate brandable startup names, business names, and product names from a keyword, and instantly see which ones are still available to register. Check username availability across major social networks so a brand name comes with matching handles. Save domains, set favorite TLDs, and recall recent searches to keep name research organized across sessions.
Paid AI utility APIs for semantic web comparison, migration safety, code verification, document extraction, security analysis, monitoring, accessibility auditing, and citation research. Accessible through MCP with x402 payments on Base mainnet.
Paid AI utilities for web comparison, code checks, document extraction, security, and research.
Connect Claude, Cursor or any MCP client to AppSigma and let your AI research apps, read reviews and track rankings on its own – no glue code.
Beamline UI installs React UI code into your own repository: 200+ components and 50+ complete screens by Danylo Pravda. Editable source lands in src/ui/beamline; compiled archives live in your app's .beamline/packages/. Commit those files with your app. Nothing at runtime calls Beamline servers. The hosted MCP server is an optional discovery and installation tool for your coding agent; the shadcn CLI installs the same parts. Nine looks, dark and light. The Complete System is $49 launch price, plus VAT or sales tax where it applies: one payment for one person's Beamline account, MCP, library, installs and updates, with lifetime updates for as long as we publish the purchased system. Teammates, contractors and clients may use, edit, build and ship the installed code as part of that app. They need their own licence for Beamline access or to take its parts into other apps. The free sample remains MIT, including commercial use. https://beamline.io
Project memory, semantic code search, and grounded agent context.
- KraceyOAuth unavailablecom.kracey.mcp
Free, read-only MCP connector for intervals.icu and HYROX training data. Connect Claude, Claude Code or ChatGPT to your own Kracey training data. Read sessions and laps, training trends, best running efforts, race predictions and saved HYROX results, or use running and HYROX calculators and find races. All tools are read-only; the connector does not build or change training plans. A Kracey account is required, but no subscription is needed for the connector.
Generate wiki docs from source code. Supports PowerShell, Python, Go, C#, Java, COBOL.
Code-map tools for AI agents: see a repo's structure first, then edit only what matters.
11Path-scoped team memories, rules and skills for Claude Code, Cursor, Codex and other MCP clients.
Open scientific and engineering knowledge for AI agents: search, evidence, document publishing.
