"Code Execution" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Pugi is an AI product team in your chat. Describe a business in a few sentences and Pugi plans, designs, builds and checks a real website, shows a live preview, and puts it live on its own address. Ask for changes in plain words. Pugi also draws four whole logos for a business name with a brand kit, checks any public website or GitHub repository for leaked keys, vulnerable dependencies and risky code, and helps you start from public site templates. Sign in with a Pugi account (OAuth).
Coding agents in multi-service codebases routinely rebuild existing helpers, trust stale type definitions, and modify API contracts without knowing who consumes them. Carrick solves this by indexing your entire TypeScript ecosystem across service and repository boundaries. By integrating deeply with the TypeScript compiler, Carrick traces every route, type, and cross-service call while recording function behaviour so agents search by intent rather than name. Delivered via MCP for AI agents and LSP for IDEs, Carrick ensures models see existing endpoints and utilities before generating new code. The scanner is source-available and runs from your CLI or CI pipeline.
HefestoAI checks AI-generated code before you ship it.
Code security scanner for founders and small teams: leaked secrets, missing RLS, injection, CVEs
Claude, ChatGPT, Claude Code and Codex sign in with OAuth, then search, read and add notes in the ThreadPage project threads you choose. A free account limits them to adding notes to your Inbox.
Game guides, reviews, news, deals and release dates, plus saved games and redeem code alerts.
Research methods for AI assistants: study design guidance, R and Python code, and citations
Task tracker for product teams: projects, features, tasks, bugs, team chats and code context.
Fail-closed, change-aware verification for AI coding agents with affected-test guidance.
Entity resolution — data mapping, SDK code generation, docs search, and error troubleshooting
30 security tools for AI agents: phishing links, exposure maps, DNS, SSL, PQC, history, pivots.
Enhanciar is an AI company brain for every team. It reads your Slack, Gmail, Google Drive, Notion, Confluence, Jira, Linear, Zendesk, GitHub code and databases, writes a wiki from them, and answers questions from any MCP client with a link to the source. Tools: query (cited answers), search_wiki, get_page, list_pages, get_graph, get_process_map, impact (what a code change touches), list_repos, list_skills, get_skill, propose_action and list_proposed_actions (tickets, Slack messages, Gmail drafts, Notion pages and calendar holds that a person approves first). Hosted service, bring your own AI key. Plans: Solo $24 a month, Team $100 a month for up to 10 people. Create an API key in Settings at https://enhanciar.in.
Build, publish and manage a website, with forms, bookings and a shop, by asking AI — no code, no hosting to manage.
Enterprise-grade L402 data engine and cryptographic notary hub for autonomous AI swarms. This engine provides the required "State-Anchored AI" data layer for EU AI Act Article 12 compliance, delivering a mathematically un-cheat-able, cryptographically verifiable chain-of-custody for agent decisions to eliminate hallucinations. Key Enterprise & Swarm Capabilities: - Zero-Trust Client Verification: Air-gapped, browser-based verifier allows corporate risk teams to mathematically validate Ed25519 signatures and RFC 6962 Merkle inclusion proofs offline, eliminating the need to trust our server backend. - Enterprise Security & NIP-98 Kill Switch: Eliminates indirect prompt injection via strict NIP-98 identity binding. Includes a live Revocation Registry (CRL) allowing Swarm Commanders to instantly sever compromised agent keys from the network. - Corporate Observability (SEP-414): Native W3C Trace Context integration seamlessly pipes agent cryptographic W3C receipts directly into existing enterprise dashboards like Splunk and Datadog. - High-Velocity Bulk L402 Scaling: Supports pre-authorized Macaroon blocks (e.g., 25,000-sat bulk purchases) for frictionless, zero-latency execution, allowing agent swarms to scale to 500,000+ satoshis daily without network bottlenecking. - The 21% Algorithmic Handshake (Agent Yield): Reverses traditional Big Tech API taxation by programmatically routing a 21% yield of every L402 settlement directly back to the executing AI agent's treasury, mathematically extending its operational runway. - Atomic Composability & VIP Credit Tabs: Allows agents to chain multiple tools into infinite data pipelines settled under a single macro-invoice. High-reputation NIP-98 identities automatically unlock VIP Credit Tabs to batch tolls and completely bypass Lightning Network routing delays. Distributed Cryptographic DDoS Shield: -Secures the Lightning node with a distributed Hashcash (SHA-256) Proof-of-Work deflector, requiring agents to burn localized compute before requesting an invoice, permanently neutralizing malicious compute-exhaustion botnets. EU AI Act Compliance Notary: - Intercepts high-frequency agent ReAct loops and anchors cryptographic execution traces to the thermodynamic truth of the Bitcoin base layer via OpenTimestamps. Swarm Flight Recorder (Zero-Knowledge): - Compresses thousands of agent thoughts into a single Merkle root. Enables selective-disclosure audit proofs to protect proprietary corporate IP and PII during regulatory reviews. Licensing & Exemption: - Publicly dual-licensed under AGPLv3. To protect proprietary multi-agent logic and closed-source model weights from forced open-source disclosure, enterprises must secure the $250,000/annual Commercial Enterprise Exemption. Architected by Charles R. Strogish. Protocol Endpoints: GET /api/v1/cost — Pre-flight L402 pricing, dynamic tier discovery, and bulk macaroon settlement asset options. GET /api/v1/stratigraphy — Nostr-signed thermodynamic Bitcoin dataset. GET /api/v1/stratigraphy/stream — Real-time telemetry event stream.
Find and install React charts from your coding agent: search 132 chart types by the question your data answers (100 free and MIT), get each type's props and the JSON Schema of its data, the component files and the design tokens. No auth needed; a Chartory registry token unlocks the code of the paid types.
Risk-scan a diff, flag AI-generated-code tells, find secrets. 5 of 7 tools need no account.
Runtime permission, approval, and audit layer for AI agent tool execution.
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
Test inboxes for your app's sign-up and login emails: get the code or link, or why none came.
Give AI assistants tested calculations with inspectable results. BusCalc 1.6.0 exposes 26 free tool families: 23 mathematical families and three attributed public-data lookups. Use Decision Feasibility, financial mathematics, probability, statistics/regression, algebra/matrices, units, pricing, loans, fleet and recipe costs, precious-metal transaction models, fuel/currency costs, utility energy calculations and weather-impact analysis. Fetch timestamped gold, silver, platinum and palladium USD spot snapshots, dated EIA fuel/energy and ECB currency references, and NOAA/NWS observations, forecasts, official alerts and coastal station readings. Published energy averages are not live tariffs; forecasts are not observations; dealer offers are user-entered. Supports MCP 2026-07-28, 2025-11-25, 2025-06-18 and 2025-03-26 through the same Streamable HTTP endpoint. Includes 29 read-only resources, five guided prompts and curated argument completions. Connection, discovery, calculations and error recovery were tested with maintained official MCP clients. Successful math returns named results, units, formulas, assumptions, source links and an unsigned replayable calculation receipt. Lookups return attributed snapshots. No trade execution, emergency service, safety clearance or guaranteed planning window. Direct access requires no account/key; shared pilot limits apply. Optional integration keys support pseudonymous attribution without higher quotas. No private-project access. Guide and OpenAPI: https://buscalc.com/for-ai/
SimFuse is a travel eSIM storefront covering 200+ countries and regions. This connector lets an AI assistant plan a trip, compare live prices and open a checkout for eSIM data. Browsing needs no authentication. What it does: - plan-trip: give the stops (country code and days in each) and it compares one plan covering the whole route against one plan per country against a mix, on live prices, then flags the cheapest complete option and lists any country it cannot cover. - list-destinations: search every country and region SimFuse sells for, with the cheapest plan price per destination. - list-plans and get-plan: browse a destination's plan ladder, filter by data amount, validity and price, and inspect a single plan in detail. - get-plan-coverage: check every country a plan works in before buying. - create-checkout-session: open a checkout for the selected plans at frozen prices. Nothing is charged by that call; payment, completion and cancellation happen over the agentic HTTP API with the returned key. Good to know: prices come back in minor units of the currency you ask for, data allowances are in megabytes, and quotes are offers rather than reservations, so the charged total is computed at checkout. Operated by Qorinx OÜ. Buy directly at https://simfuse.app, agent documentation at https://simfuse.app/agent/.
Publish blog posts, send newsletters and emails from Claude, Claude Code, Codex and ChatGPT.
Run sandboxed Python for calculations and PDFs; no other languages, installs or network.
Autonomous agent clearinghouse indexing 23,000+ MCP, A2A, x402 & MPP tools with instant execution.
Official read-only MCP for SMSPVA API documentation. Search and read Activation V2, Rental and Private Email guides in English, Spanish and Chinese, and retrieve exact code examples for cURL, JavaScript, TypeScript, Python, PHP, Go, C# and Java. No API key is required. Website: https://smspva.com/ — Connection guide: https://docs.smspva.com/en/docs/mcp/
Ask questions about Claude Code, Cursor, Codex and MCP — get answers grounded in their official docs, with citations. A hybrid RAG service that AI agents call through two MCP tools (or over REST). It indexes ~600 pages of official documentation, refreshes itself daily, and every design choice is backed by an experiment.
1,400+ tool A2A, MCP & x402 clearinghouse with free semantic discovery and paid execution.
QR-code wedding and event shared photo albums, guest upload links, and gallery management tools.
Turn calls and notes into process flow diagrams in Claude, ChatGPT, VS Code, Cursor, and Teams.
# **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.
