"PDF" 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.

DC Hub is the neutral, real-time data layer for data-center infrastructure, exposed as a Model Context Protocol server so any AI agent can both query it and cite it. Coverage: 21,000+ facilities (search, profile, score, alternatives); 232 markets scored by the DCPI Data Center Power Index; the DCGI Data Center Gas Index (per-state natural-gas suitability for siting); live grid telemetry across 7 US ISOs (fuel mix, carbon intensity, demand, prices) plus a one-call all-ISO scoreboard; interconnection-queue depth; 2,000+ tracked M&A deals and a hyperscaler-capex tracker; and site factors. — fiber routes, water-stress, tax incentives, nearby substations & transmission. Why agents choose it: it's the only data-center-intelligence source an LLM can query live and cite — every full-data response includes a Source: DC Hub, CC-BY-4.0 attribution line. It's the MCP-native alternative to quarterly PDF research: live JSON, no contracts, no NDAs. Access: Streamable HTTP at https://dchub.cloud/mcp. Free tier with no signup; free email-verified dev key for higher limits; paid tiers for full data volume.

Turn any idea, URL, doc, or PDF into on-brand visuals: 100+ formats, native in 150+ languages

Describe a house or upload a DWG/vector-PDF plan and get a walkable, editable 3D model.

Generate HTML to PDF documents in bulk or single — raw replacements or based on conditions, loops

This is a Unified MCP Server for full document control: PDF manipulation (merge, split, rotate, insert, delete), OCR/data extraction from PDFs/images, PDFs/images conversion, compression, encryption, and watermark management—all without juggling multiple tools.

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).

E-signature API for AI agents: send contracts, sign PDF documents, track and download signed files.

Tailor resumes, generate cover letters, render CVs as PDF, and browse 22+ templates.

An MCP server for converting data between 16 formats including JSON, YAML, TOML, CSV, XML, Markdown, HTML, Excel, PDF, and more. Supports single transforms, batch fan-out to multiple targets in one call, and JSON reshaping with dot-notation path mapping. 240 conversion pairs, zero config.

Live MCP endpoint delivering web scraping, PDF extraction, and Base analytics via x402 USDC micropayments.

Turn an HTML template plus JSON into a finished PDF: a quote, invoice, work order or certificate.

Turns a saved HTML template plus JSON into a finished PDF document: a quote, an invoice, a work order, a certificate. Sixteen tools for listing templates, reading the fields a template expects, and making documents, folders, versions and the bin. Authenticated with a per-organization API key.

PDF accessibility checks (veraPDF PDF/UA-1), auto-fix and Markdown conversion. EU-hosted.

Consulte contas, cartões, transações e orçamentos da sua conta financeira Vessell.

All public upAPI operations as MCP tools: web scraping, search, screenshots, PDF, OCR and more.

Proctor AI is a free AI grading tool built for teachers who lose evenings to marking. Upload a single PDF of the whole class and the agent splits the papers, reads handwritten answer sheets, grades each one against your rubric, runs integrity analysis for copying and AI-written text, and drafts personalised feedback for every student. The teacher reviews and approves every mark. Now with a Claude MCP so you can grade a class by chatting with Claude. Trusted by 2,000+ teachers.

Parse, extract, split, and ask over digital PDFs (text layer, no OCR) from Cursor and Claude.

MCP server for detecting and redacting PII (Personally Identifiable Information) in PDF documents.

Turn HTML or a live URL into polished PDF and Excel files — hosted downloads, async jobs, history.