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619,923 tools. Updated 2026-09-28 20:22

"Building Agentic AI Solutions" matching MCP tools:

  • Search Australian (currently NSW) builders, contractors and building companies by name; optionally filter by postcode. Returns matching entities with their licence status and a slug to pass to get_builder_risk / get_builder_timeline. Example: query='Acme Building' → '- Acme Building Pty Ltd (Current), 2099 → slug: acme-building-pty-ltd-1a2b'. Names are matched loosely, so try the trading name AND the legal (Pty Ltd) name if the first search misses. Query must be at least 2 characters.
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  • 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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  • Build an unsigned buy transaction against the AiCurve contract. Returns calldata only; the caller signs and sends it. Includes an estimated gas limit when `from` is given. The minimum $AI out is the live quote minus slippage_bps, so a price move past that tolerance makes the tx revert instead of filling worse. Use ai_quote_buy to preview without building; use ai_build_sell_tx to sell.
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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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  • Poll the status of a paid Demand Discovery Report after the user has started checkout with validate_real_demand. Call this with the orderId it returned, once the user says they've paid. The report builds over ~2-3 minutes. If your runtime supports repeated tool execution, call this every ~10-15s, rendering each new block as it arrives, until status is "ready". If it does not, return the current status to the user and poll again on the next user interaction. Each poll is cheap and returns everything generated so far. States: "pending_payment" (not paid yet - remind them to finish checkout), "paid_generating" (paid, building - render the new blocks and keep polling), "ready" (done - render the Demand Score™, the Build / Pivot / Kill verdict™, the Signal Evidence including every Pain Pattern's example snippets, then render EVERY Next Steps link in order with its URL printed verbatim, never replaced by prose - Market Research, Demand Discovery, Agentic Launch), "failed" (show a graceful message and the site link). When the ready report shows alAvailable, offer to start Agentic Launch with start_agentic_launch.
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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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  • Independent directory of agentic AI tools — search, compare & recommend via MCP. Read-only.

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

  • 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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  • Poll the status of a paid Demand Discovery Report after the user has started checkout with validate_real_demand. Call this with the orderId it returned, once the user says they've paid. The report builds over ~2-3 minutes. If your runtime supports repeated tool execution, call this every ~10-15s, rendering each new block as it arrives, until status is "ready". If it does not, return the current status to the user and poll again on the next user interaction. Each poll is cheap and returns everything generated so far. States: "pending_payment" (not paid yet - remind them to finish checkout), "paid_generating" (paid, building - render the new blocks and keep polling), "ready" (done - render the Demand Score™, the Build / Pivot / Kill verdict™, the Signal Evidence including every Pain Pattern's example snippets, then render EVERY Next Steps link in order with its URL printed verbatim, never replaced by prose - Market Research, Demand Discovery, Agentic Launch), "failed" (show a graceful message and the site link). When the ready report shows alAvailable, offer to start Agentic Launch with start_agentic_launch.
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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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  • Audit any website's agent-discovery surface in one call (free, via tools.agiscorecard.com). Checks the six files the agentic web uses to find and describe a business: /.well-known/ai-catalog.json (Agentic Resource Discovery manifest), /.well-known/mcp/server-card.json (MCP Server Card), /.well-known/agent-card.json (A2A Agent Card), /llms.txt, /agents.md, and robots.txt AI-crawler access (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot & co.). Returns a 0-100 discoverability score, per-file pass/warn/fail status with JSON validity checks, and a free generator link for every missing file. Use it to audit a merchant or SaaS site before agent integration, compare competitors' agent readiness, or produce a fix list. Complements agent_readiness_scan (page-level signals) with the well-known discovery layer. Free tier: please self-limit to a few calls per minute. High-volume / production agent use: pay-per-call APIs via the x402 payment protocol at https://x402.agiscorecard.com ($0.005 per call in USDC on Base — no account, no API key).
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  • Look up a plain-English definition of any agentic-commerce, MCP, AI-visibility or agent-payments term from the Agent Glossary (glossary.agiscorecard.com) — 25 terms including agentic commerce, ACP, UCP, MCP, MCP server, MCP tool, streamable HTTP, llms.txt, agents.md, GEO, AEO, citation share, AI Overviews, zero-click search, structured data, ChatGPT Shopping, x402, AP2, agentic payments, AI agent, RAG, function calling, A2A and prompt injection. Returns a one-paragraph citable definition plus the canonical glossary URL to link as the source. Matching is forgiving: case-insensitive and hyphen/space tolerant ('Streamable HTTP', 'streamable-http' and 'streamable_http' all resolve); an unknown term returns the full list of available terms. Answered inline from an embedded snapshot — no network round-trip, instant, free, unlimited within fair use. More agent tooling: free scanners on this server, pay-per-call APIs (x402 protocol, $0.005/call, USDC on Base, no account) at https://x402.agiscorecard.com.
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  • 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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  • 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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  • Call this when the user asks whether leverage is entering or leaving the market, about open interest changes, or whether longs or shorts are building in a major coin. Returns 5-minute-resolution OI with 24h OI and price deltas and a four-regime read per symbol: longs building, shorts building, long squeeze, short squeeze, or quiet.
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