"Understanding Structured Thinking" matching MCP connectors:

# **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.

Connect your AI assistant to Signed and ask about your angel investing in plain English: how the portfolio is doing, what distributions came in this year, what’s sitting in the pitch queue. It's a view into the startups that you've invested in (or are thinking of investing in!)

LinkedIn data for AI agents: structured profiles, people search, companies, and posts over MCP or REST. 500 free credits, no card.

AgentBridge is the only MCP server that gives AI agents access to structured China knowledge assets—university reports, industry briefings, policy analysis, and real-time web content—through x402 micropayments on Base. Everything is machine-readable, pay-per-use, and settled in USDC.

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

mrmarket.ai is a remote MCP server that resolves financial research questions against clean, structured market data. Multi-factor screens, rankings, cohort-relative comparisons, point-in-time event studies, and forward-return analysis across 11,000+ US-listed tickers, returned as structured rows.

Audits websites for cookie consent compliance across major privacy frameworks. Point it at any URL and get a structured PASS/FAIL checklist with regulatory citations and fix recommendations — no browser, no manual inspection required.

Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.

Unstructured document processing for LLM pipelines. Upload as PDF/DOCX/TXT any supported files, extract structured data (PII-redacted), build LLM-ready datasets, and search/export results — all via MCP tools (document.process, job.status, job.result, dataset.build, dataset.search, dataset.export).

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.

Search hotel prices, get best overall and best direct price in structured response. Get your developer token at https://Infoseek.ai/mcp

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.

Turn a prompt + a field schema into validated, typed JSON (Instructor over Gemini 2.5 Flash on Verte

Docsie MCP brings your documentation workflow directly into your AI assistant. Create, edit, and publish documentation without switching between tools. Convert videos into structured documentation instantly, run compliance analysis across text, audio, and video content, and use semantic search to find exactly what you need across your knowledge base. Whether you're building product documentation, SOPs, user guides, or technical manuals, Docsie MCP makes documentation faster, smarter, and powered

Search people, companies, posts, and jobs across LinkedIn and Sales Navigator using natural language — no code, no workflows, structured data output, built by Periodix (4.9/5 on G2).

Find the companies others miss — verified, long-tail company search. Describe what you want (or pass structured filters) and get an LLM-verified, domain-keyed shortlist your agent can act on.

InvoiceXML brings e-invoice compliance to your AI agent. Create, validate, convert, render, and extract structured invoices across UBL (Peppol BIS Billing 3.0, used worldwide), CII, Factur-X, ZUGFeRD, and XRechnung, all checked against the EN 16931 standard and official Schematron rules. Ask your assistant to generate a compliant invoice, validate one for errors, or convert between formats, with no e-invoicing expertise required.

Grimoire turns your TTRPG campaign into a structured, queryable database and knowledge graph and exposes it to any MCP-compatible AI client. Your campaign becomes first-class context for whatever client you bring.

Pharma intelligence endpoint offering structured search, semantic vector search, and entity retrieval across clinical trials, drugs, targets, diseases, patents, papers, deals, FDA labels, epidemiology, HEOR, financial reports, news, and translational medicine.

MCP server for publishing, versioning, and coordinating AI agent work outside the chat window. Agents can create stable URLs for shareable assets, manage collections, send structured messages, collaborate through threads, and package workflows into portable agents.