"IBM MCP Context Forge - Model Context Protocol Framework" 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
Financial market context for AI agents, with listed companies' results: www.prereason.com/agents.md
Oathlayer is an independent check on AI answers. It checks each answer against independent sources, gives it a clear verification tag, and records it with a permanent doc number that anyone can look up free at oathlayer.com/verify. It reports what happened; it does not judge right or wrong. Works on any topic and with any AI model. Tool: verify_output. Connect at https://mcp.oathlayer.com. The Five Pillars of Trust: Verification Successful - Oath Approved; Verification Threshold Not Met; Various Perspectives Apply; Complex Question - Threshold Reserved; Clarity Needed to Continue. Citation Checks: paste a case citation and Oathlayer checks it against official court sources. Each citation gets a short tag: Confirmed, Unofficial, Mismatched or Not found. If trusted sources report a case was overruled, it says Reported overruled. It checks the citation only, not whether the case supports your argument. Also available as Oathlayer Chat (no programming needed) and an API (oathlayer.com/docs). Free trial: 5 free verifications, no card needed (oathlayer.com/signup). Then pay as you go: one credit per check, credits valid 12 months, no subscriptions.
Free, read-only Movie MCP Server from FlickDirect Inc. Version 1.5.1 finds movie and TV facts, cast, available country-specific dates and ratings, trivia, quotes, site-wide news and reviews, CinemaCon coverage, eligible stored interview and show transcripts, podcasts, streaming, soundtrack and Additional Songs links, daily deals and current shop listings. Canonical FlickDirect links accompany matching information, including ordinary movie questions. Reviewed T2 and X2/X-Men 2 defaults are checked against published titles and years; explicit title/year context takes priority. Quote results provide descriptions, brief exact excerpts when supplied, and source links, without invented wording or speakers. Music and purchase links preserve published affiliate parameters; no track lists or scene placement are inferred. Bounded recovery and fresh retrieval preserve the same two read-only tools and existing inputs. Shop inclusion is not a formal endorsement; fetch time does not prove currentness. No full articles, reviews, transcripts, scripts or photographs. Free public access with no FlickDirect account or API key. Usage-derived diagnostics expire within seven days. Host applications control tool selection and final presentation; this does not train model weights.
Data Oil is a multi-model database built for object storage. It uses a log-structured storage engine, snapshot isolation with optimistic commit, and a change feed from the write-ahead log. Its SQL dialect supports joins, window functions, CTEs, set operations, graph traversal and pattern matching. It has its own HNSW vector index and BM25 full-text index, and a write is searchable by keyword and vector as soon as it commits. Access is over HTTP, the PostgreSQL wire protocol (extended protocol). We have a founder's program available for projects which may be of interest https://dataoil.tech/founders.html if your interested lets chat ;)
AiSeek is a global change-intelligence platform that connects AI assistants and autonomous agents to structured, source-linked information about developments across the world. Rather than providing a conventional news feed, AiSeek focuses on identifying meaningful changes, preserving their context, and making the underlying sources accessible. What a source says (fact) is kept apart from what it may mean (hypothesis). Coverage spans artificial intelligence, technology, global economics, financial markets, companies, government policy, science, and other major domains. The MCP server provides sixteen read-only tools. Every name follows one pattern: list_, get_, search_ or verify_. Changes and context: • list_changes — Browse the newest published changes, newest first, 20 per page. • search_changes — Search changes by words in English, Chinese or Japanese, ranked by relevance. • get_change — One change with its sources: outlet, link, date, evidence level and role. • list_topics — Browse curated topics, optionally by coverage domain. • get_topic — One topic and up to 50 of its recent changes. • get_latest_brief — The latest daily or weekly brief. • list_scheduled_events — Upcoming central-bank decisions, data releases, earnings, rule dates and more. Companies: • search_companies — Find a company by name, alias or ticker. • get_company — A company's profile, listings, recent changes, stated facts and relations. Countries and SeekEarth: • list_countries — Every country with its name and this week's number of changes. • get_country_heat — Change Heat per country against its own normal, with a seven-day series. • get_earth_view — One place and moment on SeekEarth: changes, heat and the Earth's layers. Evidence (SeekTrace): • get_change_history — A change's evidence receipts and what AiSeek knew at an earlier time. • get_receipt — One evidence receipt. • verify_receipt — Check that a stored receipt is unchanged. • list_corrections — Public corrections, newest first. Tool calls need an AiSeek API key (Authorization: Bearer or the x-api-key header), made at aiseek.dev/en/account; the handshake and the tool list are open. Optional connection defaults go on the endpoint URL: ?locale=en|zh|ja&limit=1-50&domain=MARKETS. Answers carry sources and links, never quoted passages.
Find laptop replacement parts by model, with an official OEM source link on every fit.
The right context at the right time, from the world's leading data providers. One API. 800+ tools.
Fresh context for agents. Ask, search or follow stories. Each fact cites a Synorb Manifest.
Queries live HackerNews discussions, front-page velocity, and developer sentiment on any framework
# **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
Ask your Search Console data in plain language and get real numbers, not guesses. The GSC Wizard MCP plugs your live Google Search Console data straight into Claude, ChatGPT, and any MCP-capable assistant - so the model reads your actual clicks, impressions, position and CTR instead of inventing them.
Give your AI agent the context of a video link: transcript, summary, chapters and timestamped answers. Hosted, no account or key needed.
Tesla Model 3 and Model Y collision parts by OE number. CAD prices, ships Canada-wide, read-only.
A calorie MCP that looks up calories and macros from a 4M+ food catalog, not model guesses.
Studocu helps students find study documents, set course context, ground answers from the open web, and manage authenticated study projects—attaching sources, reading artifacts, creating quizzes or mock exams, and keeping durable study memory—from any MCP client.
Web research across search, Reddit, YouTube, local businesses, reviews, ad libraries and more. Every answer carries line-numbered citations; up to 1,000 rows per call without loading them into context.
Search, rank, and compare 500,000+ AI models, datasets, papers from 13+ platforms. Hardware-aware model selection with VRAM and license constraints. 5 tools: search, rank, explain, select_model, compare.
A fast, secure, and LLM-friendly Model Context Protocol (MCP) server that scrapes job listings from major platforms (LinkedIn, Indeed, Google) and converts them into structured Markdown format.
