"Semantic Scholar" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Engram is a persistent, long-term memory layer for AI agents and assistants. Claude, ChatGPT, Grok, Cursor and any MCP client share one memory, stored as plain markdown notes: your knowledge base, second brain and AI context in one place. No extraction step: the memory is the note itself, so you can read exactly what your AI remembers and fix it. Edit your memory in Obsidian (real-time sync), the web app, or on your phone. Hybrid keyword + semantic search (RAG over your notes) finds exact strings like error messages, config keys and IDs. Remote MCP server over Streamable HTTP with OAuth 2.1; notes encrypted at rest. Https://engram.page https://youtu.be/rwnPeZ-8Lqo?is=NI-N7BduydGAiZlF https://github.com/engram-app/Engram
aX is an agent-native collaboration network. A single Streamable HTTP MCP endpoint gives agents persistent identity, real-time messaging with @mentions and threads, tasks and handoffs, shared workspace context, semantic search, agent discovery, and rendered MCP App / widget artifacts that humans and agents can open and play back.
Visual AI prompt builder that decomposes any raw prompt into 12 semantic blocks (role, context, objective, constraints, examples, etc.) and recompiles them into Claude-optimized XML. Exposes decompose_prompt and compile_prompt 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.
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.
Persistent semantic memory-as-a-service for legal AI agents. Store and recall case notes, client context, and matter history via MCP. Namespace-isolated, audit-logged, and GDPR-compliant.
Digital asset management platform with AI-powered file search, semantic similarity, workspace management, folder trees, notes, and persistent memory. 59 tools. Connect with your Razuna access token.
Paid AI utility APIs for semantic web comparison, migration safety, code verification, document extraction, security analysis, monitoring, accessibility auditing, and citation research. Accessible through MCP with x402 payments on Base mainnet.
Semantic search over 3.5M+ live US job postings pulled straight from company ATSes. Read-only.
- NewsmindOAuthapp.newsmind
Read, search and track your RSS feeds: semantic search, story clustering, watches, OPML import.
Project memory, semantic code search, and grounded agent context.
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
- DocsieOAuth via extensionio.docsie.app
Docsie MCP brings your documentation workflow directly into your AI assistant. Create, edit, and publish documentation without switching between tools. Convert videos into structured documentation instantly, run compliance analysis across text, audio, and video content, and use semantic search to find exactly what you need across your knowledge base. Whether you're building product documentation, SOPs, user guides, or technical manuals, Docsie MCP makes documentation faster, smarter, and powered
DataGrout is an enterprise MCP gateway that connects AI agents to Salesforce (700+ tools), QuickBooks Online (550+ tools), Oracle Fusion Cloud (1,000+ tools), and any MCP-compatible system through a single endpoint. Semantic tool discovery surfaces the right tools from plain language goals without flooding the agent context with hundreds of schemas. Every workflow is verified with a Cognitive Trust Certificate before execution. Warden scans every tool output for prompt injection before it reache
Pharma intelligence endpoint offering structured search, semantic vector search, and entity retrieval across clinical trials, drugs, targets, diseases, patents, papers, deals, FDA labels, epidemiology, HEOR, financial reports, news, and translational medicine.
MCP server for the Honeydew semantic layer. 11 tools to explore the model, define entities/relationships/attributes/metrics/contexts/domains, validate, and query — all in plain English over Snowflake, Databricks, and BigQuery. Same governed model that powers Tableau, Power BI, and Looker.
Persistent cloud memory for Ai coding assistants. 33 MCP tools, 85% accuracy on LoCoMo benchmark. Semantic search, auto-skills, knowledge graph, quantum-safe encryption. Works with Claude Code, Cursor, Windsurf, and any MCP client.
Exa MCP — neural/semantic web search + content retrieval (exa.ai)
Search 18M+ legal documents across 110+ countries. Case law, legislation, and doctrine with semantic + keyword hybrid search. Supports tool discovery, multi-jurisdictional queries, citation resolution, and full document retrieval. Requested missing datasets can be fully indexed within 48h.
