"An analysis of financial or statistical charts" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Satellite SAR analysis: ship, oil slick, building change, and time-series change detection
AXL MCP lets AI assistants create and manage landing pages, courses, email campaigns, CRM records, and marketing workflows inside AXL. Built for growing expert businesses, it turns chat requests into real work across sales, marketing, and course delivery. An AXL account is required. Sign in securely with OAuth 2.1. Website: https://axl.tech/developers/mcp . Setup guide: https://docs.axl.tech/mcp . Watch AXL in 77 seconds: pages, courses, CRM, and automation. Product overview: https://www.youtube.com/watch?v=jlhR9CafIww
Taokeh is accounting software for Malaysian SMEs — double-entry books, LHDN e-Invoice (MyInvois), SST, and full statutory payroll — and this connector opens a company's live books to the AI its owner already uses. 59 tools. The reads answer real questions from the ledger: P&L and balance sheet with server-computed comparisons, cash position, A/R and A/P aging, per-channel marketplace sales, an 8-week cash-flow forecast, tax position, document search with e-Invoice standing, and a one-call daily brief. The writes are drafts only — expenses, invoices, bills, quotes, purchase orders, receipts, credit and debit notes, adjusting journals, bank-statement imports and bank-row suggestions — every figure re-checked by the server, every draft waiting for a human tap in Taokeh. The AI can also work the Shoebox: staff snap paper on free phone logins, and the connector lists the pile, reads each photo, and files the draft with the original attached — the server maps its own stored copy, so the document trail stays byte-perfect. One connection is bound to one company at consent; no tool takes a company argument. Migrating from another system? The same connector stages the chart of accounts, opening balances, contacts, products, historical documents and workspace settings onto the owner's own review screens. Bring your own AI subscription — no per-call fees.
The vetted, cross-LLM marketplace of doer agents — itself an MCP server.
Reach your own phone from an AI agent: notifications, approval questions, reminders, ring, files.
Create, edit, review, and explicitly publish Live or Snapshot Markdown Documents in mdedit.ai.
The MCP server behind goodbotbad.bot, where the crowd rules AI transcripts good bot or bad bot.
Media intelligence analysis for audio, video, and images via the Echosaw MCP server.
Analyze Product performance on Platforms like youtube, instagram etc. To optimize Marketing.
Connect your AI to any database — PostgreSQL, MySQL, or SQL Server — in seconds.
Ask your AI assistant a cost question. Get allocation, correlation, and explanation in one response. Costory connects Claude, Codex, or Cursor to normalized cost data across AWS, GCP, Azure, Datadog, OpenAI, and Anthropic. https://costory.io Free trial 14 days, 250 USD / month up to 10M Spend
SEC dilution data, live market data, and news for U.S. equities, with point-in-time as-of queries
Specialist tools for any job — a lead, an image, a song, live data, and more.
DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.
Access Versium's B2B2C identity graph directly through your AI agent. Generate targeted lead lists, enrich records with contact and firmographic data, validate emails, and size audiences — all through natural language. No manual exports or API coding required. Requires an active Versium REACH subscription with API access.
Twitter/X, Instagram, Reddit & TikTok data for AI agents. Billions of posts. No API keys.
Millions of dated buying and momentum signals: who raised, who's hiring, what changed and when
# **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.
Official hosted MCP server for Flicker (flicker.finance). Crypto, stock and FX market data (prices, screeners, funding rates, exchange stats, news), Flicker's published analysis zones and per-position assessments, plus the authenticated user's multi-exchange portfolio: positions, trades, balances and exchange connections, watchlist, alerts and notification preferences (watchlist/alerts/preferences are editable, everything else read-only). Does not place or manage trades. Streamable HTTP, OAuth r
Social media analytics, video analysis, and competitor intel for any MCP-compatible AI agent.
