"Clever Cloud" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Control your existing Philips lights, Sonos speakers, Roborock vacuums and other supported devices through Port OS cloud. Give ChatGPT, Claude and other MCP-compatible assistants access to the devices you connect. Turn off the living room lights, play music on your speakers, adjust the thermostat, run a vacuum in a room, check a camera snapshot or find a connected tracker. Available actions depend on your linked provider accounts and each device’s supported capabilities. Port OS runs in the cloud; no local server is required. You choose the account, device and capability permissions each assistant receives. Device operations require user OAuth; public tool discovery does not grant device access. Port OS is in alpha. Setup: https://portoperatingsystem.com/docs/getting-started/ Support: https://portoperatingsystem.com/contact/
Control your Philips lights, Sonos speakers, Roborock vacuums and more with Port OS cloud.
Cloud Android 14 phones that Claude, Claude Code, Cursor and Codex can use over MCP. Ready in ~15 s. Plain-English tasks return pass/fail, steps, screenshots and video. 1¢ per phone-minute, AI included, 100 free minutes a month.
Give your agent its own cloud: Postgres, Redis, a sandbox or any Docker image, in one MCP call.
FFmpeg Micro - The FFmpeg Api - www.ffmpeg-micro.com Transcode videos from n8n or Make using FFmpeg in the cloud. MCP Server: https://github.com/ffmpeg-micro/ffmpeg-micro-mcp/
Skills, agents and memory in the cloud, plus a gateway to your other MCP servers.
Deploy the apps you build with AI, with databases and domains, and call 200+ AI models on one key.
WhatsApp for AI agents — your own number or the official Cloud API: messages, media, templates.
Let cloud agents talk to you: progress updates spoken on your Mac, and replies you send back.
I connect Claude to your own Oracle Cloud EPM pod and help the people who run EPM check jobs, run rules, load data and read metadata or a slice of numbers. Each person connects as themselves, so Oracle applies their own roles. I encrypt credentials on my server; they never reach Claude. Every call passes a policy check and is logged. Risky writes ask for confirmation first, with a preview of what will change. On a production pod that is every write. The activity log holds no cell values.
Private memory for AI agents: save and recall facts across Claude Code, Codex, Cursor and ChatGPT.
# **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.
Path-scoped project memories, rules and skills for coding agents, on your machine or Pathrule Cloud.
Costory is the FinOps agent your team builds on. Claude plus a single provider's MCP works for one person and one cloud. It breaks when you add more providers, more people, and more tools to maintain. - Multiple providers: AWS, GCP, Azure, LLM APIs and data clouds normalized and allocated to teams and features in one model, including allocation from telemetry (Kubernetes, Datadog, dbt). - Multiple users: one hosted setup with OAuth, per-user access and enterprise SSO, instead of local credentials and prompts per person. - Maintained for you: tools, skills and connectors kept up to date by Costory, so reorgs and new services don't break your agent. Your agent finds savings, explains every change in spend and issues recommendations that your coding agent can implement. It works in Claude, Cursor and any MCP client, plus Slack. Customers include Alan, Deezer, Delivery Hero, Voodoo and Pennylane. Pricing: Starter from $300/month, flat: unlimited users and data sources, and a fair use that 80% of customers never outgrow. Nothing is ever cut off, and agents connected through the MCP use no AI credits. 30-day free trial.
Search, read and organize the screenshots in your ScreenVerb cloud library.
Review your AI agents' work in minutes, not hours of chat. Approve it or send it back.
Google Ads, GA4, Search Console and Tag Manager for AI assistants. Every change is proposed first.
Spending control for AI agents: approved budgets, a decision before each payment, signed receipts.
Hosted MCP for Celesto cloud computers. Create, start/stop, exec, publish ports, and delete hardware-isolated sandboxes over OAuth.
Search US TTB alcohol label approvals, permittees and processing times. Read-only; account required.
