"file system" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
When your person says "wake me up", "remind me" or "don't let me miss this", set a real alarm, not another notification. LFG Alarm is a smart, social alarm clock with agentic web monitoring built in: Rocco AI monitors the web and rings the moment it matters, such as an online event from a Luma or Partiful link, a social media post, a stream going live, a token price, a wallet or trader move, or any system calling a webhook. Share one link and friends and family ring together, on any device.
Connect compatible AI assistants to your own registered and authorized Windows or macOS computers for file operations, terminal commands and device management. Free: 1 device, 10,000 MCP tool calls per month; allowance resets next month.
K2 annual reports for Swedish limited companies (aktiebolag). Your AI uploads the SIE file, reviews the figures, reads the tax calculation and INK2, and fills in the annual report step by step in the same guided flow as the web app, while you watch in AI mode. Payment, signing and digital filing with Bolagsverket are always done by you.
jitprompt gives your agents just-in-time instructions. Upload your docs as they are, and your agent gets the right section over MCP at the moment it needs it, instead of 20k tokens of system prompt every turn.
111 tools for AI assistants, so an answer comes from a rule instead of a guess. 84 deterministic utilities. IBAN and VAT validation, cron parsing, regex testing, hashing, JWT decoding, conversion between JSON, CSV, XML and YAML, units, number bases, timezones and date arithmetic. Eleven of them encode Dutch rules that a model tends to get wrong: AOW pension age, inheritance and gift tax, unemployment benefit duration, notice period, severance, the 30 percent ruling, holiday allowance, mileage allowance, and public and school holidays. Five more generate test data that is structurally valid and belongs to nobody: BSN, IBAN, BRP person records, UPA pension files and document numbers. 14 render tools that return a hosted file. HTML or a live URL to PDF, full page screenshots, QR codes, PDF merge and split, images to PDF, image resize, convert and compress, metadata stripping, PDF to DOCX and back. Plus read_page, which returns the readable content of a JavaScript rendered page as Markdown. 4 memory tools give an agent key-value state that survives between sessions, scoped to its own API key. 3 workflow tools list and run saved multi-step workflows on the caller's account. 6 AI tools for the jobs where a model is the right instrument rather than the wrong one: check a message for scam patterns, write a spreadsheet formula, turn a question into SQL, translate code between languages, improve a prompt, draft an email. Utilities and memory are free. Renders and AI tools have a free tier and credit packs. Workflows need an API key, because a workflow belongs to an account rather than to a key. Streamable HTTP at https://toolforte.com/api/mcp
# **RChilli** RChilli MCP Hub is a production-grade Model Context Protocol (MCP) server that exposes RChilli’s HR data intelligence platform through AI-callable tools. Built on 15+ years of HR data intelligence, RChilli supports ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. The tools return consistent, structured JSON responses for recruiting, talent intelligence, skills, matching, compliance, and HR workflow use cases. `userkey` and `subuserid` are injected automatically from the Bearer token and must not be passed manually. <br> --- <br> # **Available Tools** <br> > ### **`analyze_hr_call`** > > Transcribes and analyzes HR or recruiting call recordings to extract speaker turns, sentiment, candidate intent, action items, skill mentions, and structured follow-up data for recruiting operations. <br> > ### **`analyze_skill_adjacency`** > > Identifies related, adjacent, and transferable skills to support skill discovery, talent mobility, workforce planning, and candidate evaluation. <br> > ### **`analyze_skill_gap`** > > Compares candidate or employee skills against a target job and returns present skills, missing skills, readiness score, and development areas for hiring, internal mobility, and workforce planning. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to standardized taxonomy profiles from the moment a recruiter starts typing. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string and returns ranked autocomplete suggestions with canonical skill names and categories. Helps prevent free-text entry errors and keeps skill data clean. <br> > ### **`classify_job_zone`** > > Reads the job profile from a resume or job description and returns its O*NET Job Zone, from Zone 1 through Zone 5, based on the education, experience, and training preparation required for the role. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Useful as a pre-processing step before extracting data from non-standard document formats. <br> > ### **`detect_bias`** > > Reviews job descriptions and hiring criteria for gendered language, age-coded terms, unnecessary credential inflation, cultural exclusion signals, and related compliance risks. Suggests neutral replacements. <br> > ### **`detect_resume_suspicion`** > > Reviews candidate profile data for possible inconsistencies, unusual claims, timeline conflicts, missing context, and other signals that require recruiter review. <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. <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. <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. Supports multilingual extraction, batch processing, resume auto-fill, and reprocessing of historical resumes. <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 Resume Data Extraction. Ideal for resumes stored in cloud storage, S3, email attachments, or other URL-accessible sources. <br> > ### **`find_matches_in_index`** > > Accepts a resume or job description as input and returns the top matching documents from the indexed corpus, ranked by semantic similarity. <br> > ### **`generate_interview_questions`** > > Creates technical, behavioral, situational, and competency-based interview questions tailored to a role and candidate profile, including question type, competency, and expected answer signals. <br> > ### **`generate_jd`** > > Generates structured job descriptions with title, responsibilities, required skills, preferred skills, qualifications, tone alignment, language support, and inclusivity scoring from role inputs. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Supports radius-based candidate search and workforce planning. <br> > ### **`index_document`** > > Indexes a resume or job description in RChilli Search & Match so it can be searched, matched, updated, and reused across candidate matching, job matching, and similarity-search workflows. <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> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, aliases, related skills, proficiency levels, and O*NET or ESCO mappings. <br> > ### **`map_job_to_skills`** > > Converts job descriptions into granular skill maps with required and preferred skill clusters, proficiency signals, taxonomy tags, and downstream matching attributes. <br> > ### **`match_end_to_end`** > > Orchestrates data extraction, indexing, taxonomy normalization, and matching to return ranked candidates or jobs with scores, explainable results, and an audit trail from a single call. <br> > ### **`normalize_candidate_data`** > > Cleans and normalizes candidate records from multiple ATS or HR systems, resolves field conflicts, deduplicates records, and produces standardized candidate data with a field-level change log. <br> > ### **`normalize_hr_data_with_lov`** > > Maps raw and inconsistent HR data values, including degrees, universities, certifications, job profiles, skills, and locations, to the closest configured List of Values entry with a standardized value and confidence score. <br> > ### **`recommend_learning_path`** > > Builds structured learning paths with prerequisites, skill sequence, certifications, proficiency targets, and LMS alignment based on a person’s profile and target role. <br> > ### **`redact_resume`** > > Redacts configured personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. <br> > ### **`reformat_resume_with_template`** > > Applies one of the RChilli resume templates, TM001 through TM006, to a structured candidate profile and produces a consistently formatted PDF, DOCX, RTF, or HTML document. <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one job description and returns an overall match score, dimension scores, matched skills, skill gaps, and a natural-language explanation. No index is required. <br> > ### **`screen_application_batch`** > > Evaluates a batch of candidates against job requirements and custom criteria, returning scores, insights, skill gaps, and recommended next actions for each candidate. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant’s indexed corpus. Supports Boolean and semantic search modes. <br> > ### **`skill_mapping`** > > Maps raw skill text to standardized RChilli taxonomy skills, aliases, classifications, and related skill information. <br> > ### **`tag_entities`** > > Annotates extracted HR text by wrapping recognized entities in structured XML-style labels. Supported entities include job titles, skills, names, degrees, organizations, locations, countries, dates, and years.
Search open model file records, inspect recorded SHA-256 hashes and license labels, and check estimated memory fit. Public reads need no account. Connect over Streamable HTTP. Public board and request tools require user consent. A matching hash checks bytes; fit and license labels do not establish successful execution or permission for every use. Hosted MCP: https://huggingbay.xyz/api/mcp Install: npx -y hugging-bay-mcp@0.1.13 Docs: https://huggingbay.xyz/docs Repository: https://github.com/barneywohl/hugging-bay
Secure P2P File Transfer, Encrypted Chat & Communication | Decentralized P2P & AES-256-GCM encryption | Zero cloud logs. Zero registration. For humans and autonomous AI agents / MCP servers.
ParseHub.dev is a hosted document-to-records service with a remote MCP server. Agents can upload a CSV, PDF, or Word file, summarize and analyze CSV data (filter, sort, group_by, and more), read text from PDF and Word files, extract attendees, agenda items, and action items from meeting notes, ask a question about an uploaded document, and store files as database records to list and read later. Each account sees only its own files and records. **Endpoint:** `https://parsehub.dev/mcp` (Streamable HTTP, stateless) **Auth:** API key in the `X-API-Key` header (keys start with `mcp_`), or `Authorization: Bearer <API key or JWT>`. Every request without credentials returns 401. **Plans:** Free (100 API requests/month, 1 document upload), or 5,000 requests/month on the $9/month or $42/year plan. Every MCP request counts toward the allowance. **Tools:** upload_csv, summarize_csv, analyze_csv, upload_document, read_document, extract_meeting_notes, ask_document, database_status, list_database_documents, get_database_document, store_document, extract_stored_meeting_notes, export_database (administrators only). Not affiliated with ParseHub (parsehub.com), the web-scraping tool. ParseHub.dev does not scrape websites. By Poietes LLC. Docs: https://parsehub.dev/guide#connect-via-mcp
씨고 나노섬유 카세이프 맞춤 차량용 필터 규격 조회, 환경 리스크 진단 및 B2B 공조 솔루션
Crawls any website and generates a standards-compliant /llms.txt file and JSON-LD schema bundle.
PDF in, editable Word (.docx) out, with a download link for the converted file.
Bank statement PDF to CSV, OCR, Office to PDF, PDF to Word, any file to Markdown, merge and split.
Your AI agent builds native Gutenberg blocks, pages and templates on your WordPress design system.
Fastio gives your team and AI agents a workspace for each project, bringing source files, comments and new work together. Connect a compatible MCP client to find files, answer questions from project material, and save results where the team can review and use them. \ Sign in through Fastio and choose the workspaces the connection can reach. Read Only connections expose read tools; Read & Write connections also allow changes. \ • **Files and folders:** browse, read, upload, and organize files, with version history and trash. • **Search:** find files by keyword; search file content and meaning when Deep Indexing is enabled and files have been processed. • **Metadata:** read, set, and search metadata fields, including extracted fields where available. • **Shares:** create and manage shares for sending or collecting files. • **Comments and members:** read and add comments, and manage workspace membership within your permissions. • **Activity:** search workspace events to see what changed. \ Hosted by Fastio, with no server installation required. \ • **Claude and most MCP clients:** https://mcp.fast.io/mcp/tools • **ChatGPT:** https://mcp.fast.io/mcp/operations • **ChatGPT and Codex:** Fastio plugin • **Claude Code, Cursor and similar coding agents:** https://mcp.fast.io/mcp/code • **Setup and client-specific capabilities:** https://mcp.fast.io/docs \ Paid plans. Monthly plans include a trial of up to 30 days. A card is required, and billing begins when the trial ends or trial credits run out, whichever comes first. No free plan.
Manage game servers, configurations, players, and groups through the GameServerApp MCP server.
ScanSing reads printed sheet music. Give Claude a PDF or a photo of a score and it gets back MusicXML, the format MuseScore, Sibelius, Finale and Dorico open. It is the recogniser inside the ScanSing app for choir singers, offered as a connector. Once a score is recognised, Claude can answer questions about it without recognising it again: - what the key, time signature, tempo and length are, and each part's range, clefs and lyrics; - one voice on its own, for example just the alto line of a choir piece; - the score or a part moved to another key, with notes respelled for the new key signature; - a MIDI file, one track per part, to listen to or rehearse with; - a short-lived download link to open the result in a browser or notation app. It handles PDFs up to 40 pages and PNG, JPEG, HEIC, TIFF and WebP images, can recognise selected pages of a long PDF, and can split a songbook into one MusicXML per piece. A file can be given as a web link or a small image; in Claude Code, a file on your computer is sent through a one-time upload link. Recognition spends pages from your ScanSing plan, so it is the only tool that is not read-only. Everything else works on the stored result and is free. The first connection with a new email comes with 20 free pages; paid plans start at $9 a month for 300 pages. Scores and results are deleted about an hour after recognition and are never used for training.
Make your AI better at system maintenance. Prompts your AI to organize use cases behind each system.
Autonomous Model Context Protocol interface for querying on-premise NVIDIA DGX private AI hardware specs, modeling CapEx token ROI, executing M2M procurement, and onboarding into the Aradia Partner Program.
Backend for AI build SAAS applications - auth, dynamo db, functions, file storage, etc
