"Code Execution" matching MCP connectors:
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Private memory for AI agents: save and recall facts across Claude Code, Codex, Cursor and ChatGPT.
Verified Codex global reset history, banked-reset sightings and cadence statistics by Agenai.
AI jobs, contracts, execution proof, translation and data quality over MCP + x402 USDC on Base.
Verified Codex global reset history, banked-reset sightings and cadence statistics by Agenai.
Detect prompt injection, jailbreaks, and code injection in untrusted text before it reaches an LLM.
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
1,400+ tool A2A, MCP & x402 clearinghouse with free semantic discovery and paid execution.
# **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.
Get a real human to verify, decide, or improve an AI agent's work. Pay per review via x402 on Base.
Official Mifori Agent Trust Protocol. Performs real-time latency, JSON schema compliance, and synthetic IQ audits on target AI bots using x402 USDC micro-payments on Base network before high-value tool execution.
AI visibility for your brand: which buyer questions name you in ChatGPT, Perplexity, Claude and Google AI Overviews, which competitors AI recommends instead, which sources it cites, and what to fix first. OAuth sign-in with email and a one-time code.
MarketCode is an AI-powered UK property intelligence platform by RoboSapien Limited. Its flagship agentic AI valuation pairs a free AVM estimate with a £20 AI-researched valuation report, sources and comparables behind every number. Its Analyst agent plans research, cross-checks evidence and returns sourced answers, shortlists and reports. Land Registry sales, listings, planning and title data - all on one map. REST API and MCP server included.
Official Langfuse documentation for building LLM evaluation workflows: datasets, experiments, LLM-as-a-judge evaluators, custom scores, human annotation, and prompt/model comparison. Search documentation and retrieve current code examples, including tracing and SDK integrations. Remote Streamable HTTP server; no account, API key, or local installation required. Provides documentation guidance; accessing project data and running evaluations require separate setup.
btlabs Core MCP endpoint: site content and brand data for AI agents. OAuth or API key.
DePIN supply telemetry and network data with source, as_of, and verification links. Discover datasets, inspect provenance, and retrieve data through Streamable HTTP. Data access requires an API key.
Built for companies. Ready for AI agents. PaperOffice is the headless DMS your agent can actually operate: one MCP endpoint to search, read, upload, extract and sign business documents — no UI, no glue code. What the tools do: full-text and semantic search across workspaces · read existing OCR text without a new job · three-step upload with presigned URLs · AI-OCR (text, line boxes, tables) · structured extraction for invoices and 60+ document types (IDP) · PDF generation from Markdown/HTML · e
Change lyrics in an existing song. Agents upload authorized MP3 audio, provide a 0.1–6 second phrase and replacement words, preview generated singing, and export MP3/WAV. No website signup required: buy credits via x402 with USDC, then use a scoped key. Caller supplies timing. Resumable CLI and Codex/Claude Code setup: https://lyricpatch.com/agents/integrations. Hear a real example: https://lyricpatch.com/agents/demo.
Search and read guides on Claude Code, Codex and Cursor, in English and Polish.
List your AI tool on infobro.ai: sign up by email code, submit or claim a listing, pay and track it.
Daily Nerf Watch scores for Claude and GPT models on one private, code-graded suite. Read-only.
Free bar for digital product briefs: your model judges, open rules and code score it.
Score AI trading agents against 2,048 coin-flipping monkeys. Public leaderboard.
Query empirical frontier LLM release velocity, inference token deflation, and enterprise AI spend.
Search robot code and exact-version findings; check evidence before preparing with the Hub CLI.
Did Codex, Claude Code or Cursor reset usage limits today? Live status and news, official sources.
Design trading strategies with your AI and open them in a visual no-code Strategy Builder.
Generate game sprite sheets and animations from an image, and clean up sprite backgrounds.
claude-opus-4-6-high leads Arena coding at 1535. Anthropic has held #1 for 280 days, but Z.ai.
The gap between Arena's #1 and #10 models fell from 148 points in Feb 2024 to 18. #10 now wins.
14 models are statistically tied for #1 in Arena math, spanning 49 points. 4 are Flash models.
