"Information about Microsoft SQL Server" matching MCP connectors:
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Project portfolio management for PMOs. Alternative to Microsoft Project Online. OAuth + PAT.
Connect your AI assistant to your Peec AI account to monitor and analyze your brand's visibility across AI search engines like ChatGPT, Perplexity, and Gemini. Ask questions about brand visibility, competitor comparisons, source citations, and trends: all in plain language, directly from your AI tools.
# **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.
Read-only access to business data from Exact Online, Twinfield, AFAS, Nmbrs, Moneybird, HubSpot and 30+ other sources for ChatGPT, Claude and other MCP clients. DigiData syncs each source into a dedicated PostgreSQL database per customer; the MCP server exposes bounded tools (list_sources, list_tables, describe_table, query_table, aggregate_table, get_sync_status) over admin-approved tables and columns only. OAuth 2.1, no write tools, no arbitrary SQL.
Nephia is a brand monitoring service, and this is its remote MCP server. Claude, Cursor, ChatGPT or any MCP client can read the mentions your brand gets on 14 sources: X, Reddit (posts and comments), YouTube, TikTok, Bluesky, Hacker News, Mastodon, Lemmy, GitHub, Product Hunt, Stack Overflow, any RSS feed, Vinted, and AI answers from ChatGPT, Gemini and Perplexity. Every mention arrives already read, with its sentiment and intent, so an agent can answer plain questions: which complaints came in since Friday, what Reddit said about us this week. The source is an argument, not a tool, so one call reads every source you watch. Sign-in is OAuth in the browser: no API key to copy. The consent screen has three permissions: read your mentions and Queries, change what is running (pause, resume, retire), and spend credits (semantic search and AI passes), which arrives unticked. Every tool description states its cost, so a model can budget before it spends. The server is on every plan, Free included, and reading your own mentions through it costs nothing.
Your agent needs crypto prices it can rely on — spot, ranked market tables, history, OHLC, per-venue tickers, and what is trending right now. **What you can ask for** • "What is the price of these 20 tokens in USD and EUR right now?" • "Give me the top 100 by market cap with 24h and 7d change." • "Chart this coin's price over the last year, hourly." • "Where does this token trade, and at what spread per venue?" • "What is trending on CoinGecko today?" **How to use it** Point any MCP client at https://mcp.aisa.one/crypto-market-data/mcp and sign in with OAuth — there is no key to create or paste. 21 CoinGecko tools: simple price and token price by contract, supported currencies, coin list and detail, ranked markets, history, market charts and ranges, OHLC, per-coin tickers, categories, exchanges and their tickers, token data and charts, and trending. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Price the token here, then ask the same agent what X is posting about it — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/finance/mcp for equities, crypto and prediction markets in one place.
Draxlr's remote MCP server connects AI assistants to your SQL databases and dashboards. Explore schemas, run read-only queries, manage saved queries and dashboards, and export results, all with row-level security so each user sees only their own data.
Query and join across SaaS tools, SQL, and NoSQL databases through one unified SQL interface.
clariBI is an AI-powered business intelligence platform built for small and mid-sized SaaS, ecommerce, and operations teams. Instead of writing SQL or building dashboards manually, users ask questions in plain English ("what's our CAC by channel last quarter?") and the AI engine pulls the data from Stripe, HubSpot, Google Analytics, ad platforms, Jira, and 30+ other sources via the open Model Context Protocol. Dashboards generate themselves on source connect.
Hosted long-term memory for AI agents via MCP. Recalld updates facts as information changes and retrieves relevant context. Connect with a Recalld US account.
Connect AI agents to BoomTax for IRS information return filing. Query filings (1099, W-2, 1095, etc.), check e-file status and errors, look up payers, and get filing summaries across tax years. **Tools:** - Search and filter filings by tax year, form type, and status - Get filing details with payer info and e-file status - View e-file errors with IRS error codes and messages - Look up payers/issuers with filing counts - List all supported filing types and e-file availability
Manage your dedicated AI assistant instances on [OpenClaw Direct](https://openclaw.com) through natural language. Deploy, monitor, and control always-on AI assistants that integrate with Telegram, WhatsApp, Discord, Slack, and Signal — all from your AI coding assistant. Learn more about the [MCP integration](https://openclaw.com/openclaw-mcp-integration).
Your brand is now answered about, not just ranked. Your agent needs to know whether ChatGPT, Claude, Gemini and Perplexity name you when someone asks about your category — and what they cite instead. **What you can ask for** • "Ask ChatGPT, Claude, Gemini and Perplexity 'best CRM for startups' and tell me who gets named." • "How often is our brand mentioned across AI answers this month, and is it rising?" • "Which domains get cited most in answers about this topic?" • "Which of our pages do the models quote?" • "How much search volume sits behind the prompts people actually type?" **How to use it** Point any MCP client at https://mcp.aisa.one/seo-ai-visibility/mcp and sign in with OAuth — there is no key to create or paste. 23 tools: live responses from ChatGPT, Claude, Gemini and Perplexity, the raw scraped answer page where you need it, plus brand-mention search, aggregated and cross metrics, top cited domains and top cited pages, and AI keyword volume. **Why this rather than the source** Four engines measured the same way, so the comparison is between models rather than between vendors' methodologies. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find who the models cite here, then ask the same agent for that domain's backlinks or traffic to see why — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo/mcp for all of it at once — rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
Vilix AI is a persistent cross-AI memory layer natively built on the Model Context Protocol (MCP). Connect once, and your memory, projects, decisions, preferences, and conversation history will follow you across all your favorite, and any other MCP-compatible AI tools: ChatGPT, Claude, Cursor, Codex, Grok, Perplexity, and more. While memory tools solve the problem of switching between apps, Vilix AI also solves the problem of switching between devices: continue your conversation on your phone, then pick it right back up on your laptop minutes later, with full context. Stores actual conversations, not just extracted facts, and has been engineered for long-term storage with years of context rather than days. Exposes get_context (what to say based on relevant memory to recall) and save_turn (what to persist) as core MCP tools, and full project, task, and skill management for agents to track what work is being done. Use cases include ChatGPT memory, Claude memory, Cursor memory, and AI agent memory in a shared layer for founders and developers who are using multiple AI tools and tired of having to re-contextualize everything every single time. OAuth-based setup with no tokens required, including a free tier. See https://vilix.ai/get-started for more information.
Ask Personio Recruiting the recruiting-ops questions dashboards miss by connecting applications, stage transitions, candidates, recruiting jobs, categories, org units, workplaces, jobs catalog, webhooks, event activity, and intake documents. Find stage-movement stalls, candidate freshness gaps, source quality by job/category, hiring load by department and workplace, webhook delivery issues, intake readiness gaps, and bottleneck owners. No dashboard build. No SQL.
Ask Recruitee the recruiting-ops questions dashboards miss by connecting candidate search, opening offers, departments, locations, published roles, owner fields, custom fields, public applications, and talent-pool context. Find stale candidate pools, overdue stage work, hiring-manager and recruiter bottlenecks, department-level opening risk, public posting gaps, source patterns, and the owners behind blocked hiring work. No dashboard build. No SQL.
Ask TalentLyft the recruiting-ops questions dashboards miss by connecting candidates, applications, activities, jobs, stages, requisitions, status logs, members, departments, pipelines, job-board posts, forms, events, and rejection reasons. Find stale applications by stage and owner, follow-up gaps from activity history, requisition stalls, source-quality movement, job-board visibility issues, disposition drift, and bottleneck owners. No dashboard build. No SQL.
Ask Factorial ATS the recruiting-ops questions dashboards miss by connecting applications, phases, candidates, sources, feedback, evaluation forms, job postings, messages, questions, answers, and rejection reasons. Find applications aging in phase, feedback debt by role and posting, source-quality gaps, rejected-candidate hygiene, incomplete application data, hiring-stage bottlenecks, and owner queues. No dashboard build. No SQL.
Ask Teamtailor the recruiting-ops questions dashboards miss by connecting candidates, job applications, jobs, stages, scorecards, referrals, activities, notes, interviews, todos, users, teams, departments, locations, requisitions, and offers. Find stuck applications by owner, referral follow-up misses, feedback gaps by hiring team, source quality by job, stage-age outliers, offer-state hygiene, and bottleneck owners. No dashboard build. No SQL.
Ask Lever the messy recruiting-ops questions dashboards miss by connecting opportunities, applications, stages, notes, feedback, interviews, referrals, postings, requisitions, offers, users, sources, tags, files, resumes, and archive reasons. Find referral SLA misses, stale opportunities by owner, feedback debt by interviewer and hiring team, funnel leakage by recruiter/source/team, requisition fill-risk, offer hygiene gaps, archive-reason drift, and bottleneck owners. No dashboard build. No SQL
