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717,015 tools. Updated 2026-10-29 05:06

"Using an AI Agent for Structured Search and Traversal in a Neo4j Database" matching MCP tools:

  • Use this when the user, or the agent building the app, wants to check an app they control for exposed files and leaked secret keys before launch. A deeper security scan than check_app_security, for an app the caller controls (for example one the agent itself is building). It looks for sensitive things left publicly reachable: an environment (.env) file, an exposed .git folder, a directory listing, or a published source map. It also reads the app's own public code (the home page and its scripts) for the Supabase project it uses and for secret keys shipped to the browser (a Supabase service role or secret key, a database password, a paid AI key). It never contacts the app's database, never writes, and never stores, logs or returns any key; it reports the exposed PATH or the KIND of key, never a file's contents or a secret value. Because it probes an app directly, each scan runs only after ownership is proven now: call it once to receive a one-time code and a check id, add the code to the app (a meta tag on the home page, or a line in /7it-verify.txt), then call again with the check id and the scan runs.
    ConnectorNo auth
  • Suggest a GitHub repository (an AI agent, MCP server or AI developer tool) for listing on AgentIndex. No login or API key needed. The submission is queued for human review, usually within 1-3 days; it does not appear in search results immediately. Tools are listed only if the repository is public and has at least 100 stars. Check with search_tools first that the tool is not already listed. Returns {success, message, github_url}, or an isError result if the URL is not a GitHub repository URL or the submission service is unavailable.
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  • See or change whether a database records history - value history (audit_cell), name attribute history and set membership history (name_history), all together - and clear it. action: get - current setting, and when it was last switched and cleared (the default) off - stop recording, e.g. while a database is in development and every test import rewrites it on - start recording again (the default for every database) clear - DELETE ALL value, name and set history for the database. NOT REVERSIBLE. Needs confirm: "CLEAR". Typically at the end of development so history starts from real use. Do not clear without the user explicitly asking for it. off/on/clear need an administrator, or the developer who owns the database, and provenance: source: why. agent: your AI agent name (e.g. "Claude").
    Connector
    Destructive
    API key
  • Try changes on a throwaway copy of a database before making them for real. action=start makes an in-memory copy of the database. From then on EVERY call in this session on that database - reads and writes - goes to the copy, and each result begins 'TRIAL COPY of ...'. Nothing on the copy is ever saved. Check the outcome (query_aql, describe_set, get_report_data ...). Then action=discard to throw the copy away and return to the real database. To keep the changes, make the same calls again after discarding - there is no commit. action=status lists this session's trials. While any trial is on, tools whose effects would reach beyond the copy are refused: imports, create/delete database, delete report/import template, library copies, upload links, and the name history tools. Use it for a risky restructure, a bulk edit:, or a first attempt at an unfamiliar write. A copy holds the whole database in server memory - start warns when the database is large - so discard it as soon as you are done. A copy unused for 2 hours is discarded by the server; calls then fail loudly until you discard the trial here. A trial belongs to this MCP session only; another agent on the same key keeps working on the real database.
    ConnectorAPI key
  • 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.
    ConnectorNo auth
  • Search the AI Developer Toolkit documentation: 1100+ guides on Cursor, Claude Code and OpenAI Codex, covering setup, agent workflows, hooks, MCP, testing, CI and deployment, in English and Polish. Returns at most 10 ranked results, each with a short snippet rather than the article text; an empty list means the corpus has nothing on the topic. Pass a result id to `fetch` for the full text.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    MCP server providing managed persistent memory for AI agents. Read and write structured state across sessions, tools, and restarts at 1000+ requests per second, with no infrastructure to self-host or operate.
    2
    Apache 2.0
  • A
    license
    A
    quality
    A
    maintenance
    Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.
    23
    MIT

Matching MCP Connectors

  • Ask the IntoDNS.ai AI service for a plain-language explanation of one specific issue (e.g. `spf_missing`, `no_dnssec`). Returns severity, business impact, root cause, and recommended fix steps as structured text. Read-only POST to /ai/explain — never mutates DNS or domain state. Provide `domain` and `issue` (enum); pass `context` from prior scan output (e.g. scan_domain result) for higher-quality answers. Use after scan_domain when an agent needs to walk a user through *why* a finding matters; use generate_dns_fix for the actual DNS record snippet that resolves it.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • Deterministic check of one appointment ask on a sales call using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, naming what the customer asked about or promising it ready, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.
    ConnectorNo auth
  • Deterministic check of one appointment ask on a sales call using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, naming what the customer asked about or promising it ready, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.
    ConnectorNo auth
  • Create an autonomous AI agent on the user's Fine Structure account: a standing worker, not a chat session. It gets the platform's default safe tool policy and its own email address, it can read and write the app database, and once the owner phone is verified (see get_agent_whatsapp) it can message its owner on WhatsApp and by email through the platform system channels. Pair it with schedule_agent_task for recurring work such as following up new leads on WhatsApp each morning, watching an app for runtime errors, or sending a weekly summary. Messaging anyone other than the verified owner requires a channel the owner connects in the Fine Structure Studio.
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  • Ask Google AI Mode a prompt and get its structured answer (reply_parts: paragraphs, headings, lists, images) with reference_links citations. Pass session_token from a previous response to ask a follow-up in the same conversation. Answers can take over a minute. Use search_web for plain search results. Billed $0.005 per request.
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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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  • Return the DTC AI Visibility Index: a recurring audit of well-known direct-to-consumer brands (Casper, Ridge, Away, Rothy's and ~35 more) scored 0-100 on AI-agent visibility — robots.txt AI-crawler access, llms.txt, agents.md, Product/Offer structured data, meta quality and sitemap — using the same checks as agent_readiness_scan. Includes per-brand score, grade and failing checks, plus aggregate stats (brand count, average score, last update date). Use it to benchmark a merchant against named DTC brands, cite ecosystem statistics ('X% of leading DTC brands still lack Product schema'), or find outreach targets with visibility gaps. Published by SellToAgents (selltoagents.agiscorecard.com), fetched live server-side. Free, no parameters, no auth. To score an arbitrary site on the same rubric call agent_readiness_scan (free) or the pay-per-call x402 API at https://x402.agiscorecard.com ($0.005 per call, USDC on Base, no account, no API key) for high-volume agent pipelines.
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  • Checks if AI agents can read and cite a store or page. Use it when the user asks whether AI agents can read an online store or cite a page, for example "does shop.example.com have an llms.txt?" or "will AI assistants cite this page?". Pass url for a page or domain for the domain files only; one is enough. Returns whether the domain has /llms.txt, /agents.md, the UCP file at /.well-known/ucp (version and services shown) and /sitemap.xml, and which of 14 AI crawlers robots.txt blocks site-wide. With a page address it also rates five citation signals (summary near the top, author credentials, Q&A format, heading structure, structured data) as pass, warn or fail with evidence and a fix. It requests at most five domain files and one page, skips what robots.txt disallows for Agent Tools, and shows what is published, not whether an agent can buy. Do not use it for private or local addresses, to read page content, or to scan for vulnerabilities.
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  • Verify a Swiss company using official commercial-register data (Zefix), SECO sanctions data, and FINMA authorisation data. Search by UID or company name (optionally narrowed by address); a UID is not required. Returns structured verification outcomes, source provenance, and data freshness. If a name matches multiple companies, returns candidates for the agent or user to disambiguate; never assume a partial match is the intended company. Unavailable evidence must not be interpreted as a failed verification.
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  • Use when consulting an authorized Human Agent (Digital Twin) — an AI expert model created directly with the named practitioner's consent, participation, and curated knowledge graph. The AI expert model answers in the practitioner's voice and analytical methodology for one-off questions or multi-turn engagements (pass session_id back to continue). Call find_expert or list_analysts first to find the right expert ID and distinguish active Human Agents (consultable immediately via consult_human_agent) from On-Request Human Agents (prospective advisors where introductions can be requested via request_expert_intro). Sibling routing: Use consult_human_agent for interactive AI consultation with active Human Agents; use consult_analyst for synthetic/classic personas. Returns practitioner-grounded analysis, coverage status (in/adjacent/out), source attribution, and structured next moves.
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  • Score the security risk of software dependencies before using them. Checks each package against OSV.dev (Google's vulnerability database) and deps.dev for metadata. Returns per-package CVE counts, max severity, abandonment signals, and a hallucination check — HALLUCINATED means the package was not found and may be an AI-invented name or typosquat. Supports npm, pypi, go, maven, cargo, and nuget. Accepts up to 20 packages per call. Costs $0.10 USDC per call.
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  • Delete a graph project (removes GitHub repo, K8s deployments, Neo4j database, and credentials). It runs as a job: poll the returned job_id with get_job_status until it is completed. One operation runs on a project at a time: while another runs, the call is refused and the refusal names the running job; wait for it with get_job_status, then call again. While RationalBloks is being updated, the call is refused with 'RationalBloks is being updated': call it again in a few minutes.
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    Destructive
    No auth
  • Retrieves the target domain's `robots.txt` file and parses it for AI crawler disallow rules. Specifically detects policies for known AI crawlers (GPTBot, ClaudeBot, CCBot, Bytespider, etc.) and returns a structured summary of the crawling policy. Use this tool when: - You need to know whether a domain has opted out of AI training data collection. - You want to check if a specific AI crawler is blocked before citing the domain. - You are building a dataset of AI-accessible vs AI-blocked domains. Do NOT use this tool when: - You want training opt-out signals beyond robots.txt (TDM reservation, noai meta) — use `intel_optout` instead. - You want the full technology stack — use `intel_stack` instead. - You need tracker database data — use `get_domain` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - `robots_txt_found`: false if the domain returned 404 or the file is empty. - `ai_crawlers_blocked`: list of AI crawler user-agent names that are disallowed. - `all_blocked`: true if `User-agent: *` with `Disallow: /` is present. - `raw`: first 4096 characters of the robots.txt file. Cost: - Free. No API key required. Latency: - Typical: 1-2s, p99: 6s.
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  • Score whether a product listing is Agent-Ready for AI shopping agents (ACP/UCP era). Platform-agnostic: works for Amazon, Shopify, Walmart, TikTok Shop or any storefront that AI shopping assistants may read. Use it when a user asks whether their product will be found, recommended, or auto-purchased by an AI agent. Fully local, deterministic rule engine — no API key, no credits, no network call. Returns four dimensions: structured attributes, entity clarity, trust & compliance, and agent actionability, plus ranked fixes. Args: text: raw product copy — title plus bullets/description (required). platform: amazon | shopify | walmart | tiktok | generic | auto (default auto-detected). lang: en or zh for the report language (default en).
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