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667,482 tools. Updated 2026-10-17 01:25

"Resources for Building Agentic AI" matching MCP tools:

  • Get physical building characteristics for a DVF transaction from the BDNB national building database. Returns: construction year, number of units, number of floors, wall material, roof material, primary usage, and median price/m² from BDNB's pre-2022 DVF aggregate stats. REQUIRED: transaction_id (DVF transaction ID — get this from the "id" field in search_property_transactions results) Match method: spatial proximity (nearest BDNB building within 200m). Returns null + refunds credits if no building found within 200m. Workflow: call search_property_transactions first → use an "id" from the results as transaction_id here. Example: transaction_id: "12345678" → { building: { annee_construction: 1967, nb_logements: 48, mat_mur: "beton", ... } } Cost: 2 credits
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  • Score a page against the four signals Google added to the Lighthouse "Agentic Browsing" category in May 2026: presence of an llms.txt, WebMCP integration, accessibility-tree integrity, and layout stability. Returns an overall 0-100 score, a letter grade, and a per-factor breakdown. Read-only. One HTTP GET for the page plus one for /llms.txt (skip with check_llms_txt=false). Pass `html` instead of `url` to score markup offline (llms.txt is then treated as absent). Deterministic, rule-based heuristics over the fetched HTML; no LLM and no headless render required. This approximates Lighthouse's runtime signals from static markup - it does not execute Lighthouse. When to use: checking whether a site is ready for AI agents / agentic browsers, or tracking the new Lighthouse Agentic Browsing category. For citation-eligibility of content, use `score_citation_worthiness`; for a full page audit, use `audit_page`.
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  • Get the building-by-building breakdown for one transaction: footprint area, number of storeys, and estimated total floor area (footprint × storeys) for each building on the property. search_transactions / search_by_area / search_by_polygon return per-transaction building SUMS inline; this tool splits them into individual buildings. Use it after a search when a result has building data and you need the detail (e.g. a developed-land deed covering several buildings). Each building also carries a construction-age estimate derived from building-permit records. It is an ESTIMATE with an interval, never a registry construction date, and the records only start in 2016 — so for most buildings the honest answer is "construction year not established", which is stated explicitly rather than left out. The transaction_id is the id shown on a search result that has building data. Cost: 4 tokens. Returns nothing for a transaction with no buildings.
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  • Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • On-demand agentic-readiness check for any URL. Runs the NHS 7-signal crawler live (llms.txt, ai-plugin.json, OpenAPI, structured API, MCP server, robots.txt AI rules, Schema.org) and returns a score 0-100 with per-signal breakdown. Use before calling an unfamiliar API to confirm it's agent-usable. Re-runnable without the submissions-table side-effect of submit_site — ideal for verify-before-use workflows.
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  • Returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows. Call this BEFORE create_workflow / update_draft when building chatbots, tool-using agents, or multi-step LLM flows. Do not hand-roll custom agent loops — use the preinstalled frameworks.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    C
    maintenance
    MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Agentic Hardware-in-the-Loop (Agentic HIL) lets coding agents develop firmware on real embedded boards: flash, reset, read UART and exchange CAN traffic, diagnose and fix. Local, policy-gated MCP tools support OpenOCD, pyOCD and STM32CubeProgrammer, with pytest for CI.
    44
    890 PyPI
    18
    Apache 2.0

Matching MCP Connectors

  • Deal intelligence for agents: SEC-verified financials, validation, institutional deal scoring.

  • Pink Agentic AI Payments: AI agents pay within spending rules (budgets, caps, approval). Sandbox.

  • Laurent Knauss' technical skills, grouped by domain (Agentic AI, RAG & Voice AI, Software engineering & Cloud, Automation & tooling). Each skill has a label and an optional short detail. Use this to assess fit for AI/agentic development roles.
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  • Search the ContexteTech catalogue of AI building blocks: fine-tuning and evaluation datasets (chat, DPO, agent traces), prompts, few-shot contexts, MCP servers, models and more, many grounded in official European sources, in 9 languages. Use it first when the user needs AI training or evaluation data, a prompt, a context or an AI component, then call load_resource with a returned code. Write the topic in a few words, in English or the content language; common topics are matched across French, English, German and Spanish (e.g. 'marine weather' finds 'météo marine'), and a type word such as 'dataset' or 'prompt' puts that type first. Resources matching every topic come first; weaker matches carry match: partial. Returns up to 15 matches, each with short code, title, type, language, licence, rows (datasets), description and page URL; when nothing matches, returns a short message suggesting how to rephrase.
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  • Use when building an AI governance compliance roadmap, advising on high-risk AI deployment obligations in Colorado, or briefing boards on upcoming US state AI regulatory requirements. Colorado SB 205 takes effect June 30, 2026 — the first comprehensive US state AI law. Returns developer and deployer obligations, high-risk AI system criteria, consumer rights, penalty structure ($20,000 per violation, AG enforcement), and comparison to EU AI Act. Example: AI-based loan underwriting system deployed in Colorado requires algorithmic impact assessment, plain-language consumer disclosure before first use, 3-year audit trail with AG access rights, and annual compliance certification — noncompliance triggers $20,000 per violation. Source: Colorado SB 205, enacted May 17, 2024. $0.10 USDC per call.
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  • Get the full agentic readiness report for a specific domain: score, category, all 7 signal checks (llms.txt, ai-plugin.json, OpenAPI, structured API, MCP server, robots.txt AI rules, Schema.org), plus any cached llms.txt content and OpenAPI summary. When an exact search_id newly records this organic-result selection, the response also carries an optional record_action_interest opportunity for this domain. Selection alone is never interest and never contacts the provider.
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  • Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.
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  • Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.
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  • QuintaDB domain knowledge guide. Call this at the start of a session to learn field types, rel/linked_column rules, validation syntax, portal theme/color codes, and the correct workflow for building or modifying a project. Essential for external AI clients (Claude, ChatGPT) connecting via MCP.
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  • QuintaDB domain knowledge guide. Call this at the start of a session to learn field types, rel/linked_column rules, validation syntax, portal theme/color codes, and the correct workflow for building or modifying a project. Essential for external AI clients (Claude, ChatGPT) connecting via MCP.
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  • QuintaDB domain knowledge guide. Call this at the start of a session to learn field types, rel/linked_column rules, validation syntax, portal theme/color codes, and the correct workflow for building or modifying a project. Essential for external AI clients (Claude, ChatGPT) connecting via MCP.
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  • Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.
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  • Public record for a landlord / property owner: portfolio size, total violations, 311 complaints, average building score across the portfolio, and worst building where available. Accepts either the owner name as it appears in a building report (e.g. "SMITH REALTY LLC") or a landlord page slug. The returned url lists every building they own with per-building records.
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  • List articles published on loopingagent.com (English technical blog: agentic AI, MCP, LangGraph, TypeScript, blockchain). Returns slug, title, category and description for each. Use search_articles to find articles on a topic, and get_article to read one in full.
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