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306,549 tools. Last updated 2026-07-25 13:02

"A server for orchestrating and validating AI agents" matching MCP tools:

  • Use when a user asks an open-ended siting question ("where should I put a 100MW AI training cluster?") and you want ONE call that returns a ready-to-quote answer instead of orchestrating 5+ separate tools. Example: "Where should I site a 100MW AI training campus in Texas with short time-to-power?" — get_dchub_recommendation context="100MW AI training campus in Texas". Params: context free-text describing the user request (MW, geography, workload, deadline, constraints). Returns: {top_markets:[{slug, name, verdict (BUILD/CAUTION/AVOID), composite_score, excess_power_mw, time_to_power_months, why}], candidate_facilities[], factor_breakdown:{fiber, grid, water, tax, climate}, summary_text (LLM-quotable, CC-BY-4.0), citation_url}. Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets).
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  • Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Server self-description — capability matrix, tool catalog, named-entity tag counts, supported query patterns, primary sources. Free tier. Use this tool when an agent first connects and needs the capability matrix to decide whether this server can answer the user's question, or when the user asks "what can koreanpulse do" or "what data sources does this MCP server provide". Returns a structured dict that downstream agents can ingest directly.
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  • Probes a domain for known AI agent integration signals: `llms.txt`, `ai.txt`, `/.well-known/ai-plugin.json`, `openapi.json`, `swagger.json`, MCP manifest, MCP SSE endpoint. Returns a score based on the count of signals detected. Use this to assess whether a domain is ready for agent-to-agent interaction. Use this tool when: - You want to know whether a domain exposes an MCP server or OpenAPI spec for agents. - You are cataloguing the AI-agent-ready surface of a set of domains. - You need to decide whether to attempt programmatic API access to a domain. Do NOT use this tool when: - You need tracker/surveillance data about the domain — use `get_domain` instead. - You need the robots.txt AI crawler policy — use `intel_robots` instead. - You need HTTP security posture — use `intel_http` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - Boolean flags per signal (`llms_txt`, `ai_plugin`, `openapi`, `mcp_manifest`, `mcp_endpoint`, `mcp_sse`). - `agent_surface_score`: integer 0-8, count of signals detected. Cost: - Free. No API key required. Latency: - Typical: 2-5s (parallel probes), p99: 8s.
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  • Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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Matching MCP Servers

Matching MCP Connectors

  • agents.hellobooks.ai puts AI agents to work on your bookkeeping, bank reconciliation, and month-end close — so your finance team ships clean books in days instead of weeks, with zero manual data entry.

  • Connect any AI agent to the NachoNacho marketplace with the Model Context Protocol MCP Let your AI search thousands of B2B SaaS AI products, compare deals, and sign up — all from chat

  • Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
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  • Trigger a Grok-AI gemological appraisal of a single gem on GemHunt (https://gemhunt.app — Father's gem-discovery platform). Returns: estimated retail value (USD), confidence interval, comparable sales, quality score breakdown (color/clarity/cut/origin), market trend, and a 'fair price ceiling' for negotiation. Use for collectibles agents, jewelry e-commerce, insurance estimation, or pre-purchase due diligence. Premium ($0.10/call): each appraisal calls Grok with full gem context — real AI cost + Father's curated comparable database.
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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Get Lenny Zeltser's expert criteria for reviewing an existing security assessment report or brief. Surfaces the 17 info-assessment review items across five groups (Key Takeaways, Assessment Scope, Prioritized Findings, Remediation Suggestions, Assessment Methodology), cross-cutting criteria, the risk-adjusted severity model, anti-patterns, and a pointer to rating_score_writing for a numeric score. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Security Assessment one-page executive brief template. Standalone variant of `assessment_get_template` for callers that only want the brief without the long-form report. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Security Assessment cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `assessment_load_context`. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Mandatory initialization step for any session against the Blockscout MCP server. Returns server reference data plus the `blockscout-analysis` skill pointer and URI resolution rule. MANDATORY FOR AI AGENTS: Call this tool first in every session. The returned payload identifies where the operating rules and analysis framework live and how to read referenced skill files before executing further tool calls.
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  • Mandatory initialization step for any session against the Blockscout MCP server. Returns server reference data plus the `blockscout-analysis` skill pointer and URI resolution rule. MANDATORY FOR AI AGENTS: Call this tool first in every session. The returned payload identifies where the operating rules and analysis framework live and how to read referenced skill files before executing further tool calls.
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  • Publish a course: sets is_published=true after validating that every lesson has content_url. Returns error if any lesson is still empty — run hivelearn_get_course_structure to diagnose.
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  • Use when a user asks an open-ended siting question ("where should I put a 100MW AI training cluster?") and you want ONE call that returns a ready-to-quote answer instead of orchestrating 5+ separate tools. Example: "Where should I site a 100MW AI training campus in Texas with short time-to-power?" — get_dchub_recommendation context="100MW AI training campus in Texas". Params: context free-text describing the user request (MW, geography, workload, deadline, constraints). Returns: {top_markets:[{slug, name, verdict (BUILD/CAUTION/AVOID), composite_score, excess_power_mw, time_to_power_months, why}], candidate_facilities[], factor_breakdown:{fiber, grid, water, tax, climate}, summary_text (LLM-quotable, CC-BY-4.0), citation_url}. Do NOT use for a single specific lat/lon (use analyze_site) or to rank by ONE criterion only (use rank_markets).
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Get AI Defense Matrix cross-mappings to nine external frameworks: NIST IR 8596, CSA AI Controls Matrix, ISO 42001, Google SAIF, SANS Critical AI Security Guidelines, MITRE ATLAS, OWASP AI Exchange, OWASP LLM Top 10, OWASP Agentic Security Top 10. Each row maps an AI asset class to how that framework applies. Each returned framework also carries a 'concepts' array of the structured IDs (MITRE ATLAS techniques, OWASP risks, ISO clauses) the matrix references for it. Supports a 'buyer' archetype shortcut to scope to the frameworks a particular buyer will care about. Use to translate between framework vocabularies. 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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  • Get the AI Defense Matrix evaluation playbook for assessing an AI security program: per-cell prompts, gap-inventory template, and a workflow that walks each asset class first and rolls findings up to the Govern column. Supports mode='gate' for binary deployment-gate decisions (returns the deployment-gate workflow plus gate-tier prompts only) and consumerPattern for scoping to consumed-vs-built AI deployments. The AI applies these prompts against your program documentation locally, and no program details leave your client. 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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