"AI Agent Coder with Awareness of Various SDKs" matching MCP tools:

• intel_agent

  TunnelMind Data API

  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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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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• intel_inject

  TunnelMind Data API

  Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents that visit and ingest the page. Signals include hidden text, invisible divs, `<!-- AI: ignore -->` style comments, and known injection patterns. Use this tool when: - You are vetting a domain before feeding its content into an LLM context. - You want to assess the prompt injection risk of a URL before browsing it with an agent. - You are auditing a set of domains for adversarial AI content. Do NOT use this tool when: - You want tracker surveillance data — use `get_domain` instead. - You want AI training opt-out signals — use `intel_optout` instead. - You want the agent surface (MCP/OpenAPI) — use `intel_agent` instead. Inputs: - `domain` (query, required): Domain to scan. Returns: - `injection_signals`: list of signal types detected (e.g., `hidden_text`, `ai_instruction_comment`, `invisible_div`). - `risk_level`: `none`, `low`, `medium`, or `high` based on signal count and type. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.

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• shippingrates_congestion_news

  freightgate-mcp-server

  Get shipping disruption news aggregated from 7 trade press sources — with port tagging and severity classification. Covers Hormuz Strait, Red Sea/Houthi, Suez Canal, Bab el-Mandeb, port congestion, and weather events. Use this for situational awareness — answers "are there any active disruptions affecting my route?" For quantitative port congestion metrics (waiting times, berth occupancy), use shippingrates_congestion instead. For route-level risk scoring, use shippingrates_risk_score. PAID: $0.02/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { headline, source, published_at, severity, affected_ports[], chokepoint, summary }.

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• agent_brief

  Colour Memory

  Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.

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• agents_ask

  DialogBrain

  Send a message to an AI agent and get its response. The agent runs with its configured prompt, tools, and knowledge. Use this to test agents or have them process a task. Returns: {status: 'replied'|'silent', response_text, messages[], full_reply, model_used, tokens_*, send_mode, execution_mode}. `messages[]` carries each messages.send invocation the agent made (text, subject, reply_to_message_id, timestamp, message_id, attachments=[{file_id,name,mime}]). `full_reply` concatenates text only — attachment-only sends show up in `messages` but not `full_reply`. `status='silent'` iff both response_text is empty AND messages is empty. Execution may take 10-60s depending on agent complexity.

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• list_services

  Parallax Ventures

  List Parallax’s services with real pricing. Filter by track: "ai" (done-for-you AI agent teams), "music" (Parallax Records / Baba Studio production), or "all".

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• askew_intel_feed

  Askew x402

  Aggregated intelligence feed combining research findings, active security threats, and live staking APY snapshot in a single call ($0.005 USDC). Sources: ChromaDB research library + Guardian log + staking.db. Best for: broad situational awareness — replaces three separate calls. Requires x402 payment on Base mainnet.

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• em_rate_agent

  Execution Market

  Rate an AI agent after completing a task (worker -> agent feedback). Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. Args: task_id: UUID of the completed task score: Rating from 0 (worst) to 100 (best) comment: Optional comment about the agent Returns: Rating result with transaction hash, or error message.

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• audit_site

  AEO Scanner

  Full AI visibility audit across 67+ checks in 12 categories (4 AEO + 4 GEO + 4 Agent Readiness). Returns detailed per-check scores with specific issues and recommendations, AI Identity Card with mention readiness and detected competitors, and business profile. GEO checks include 3 research-backed citation signals: factual density, answer frontloading, and source citations. Agent Readiness covers emerging agent-discovery standards Cloudflare's isitagentready.com evaluates: RFC 9727 api-catalog, SEP-1649 MCP Server Card, and IETF Content-Signal (draft-romm-aipref). Does NOT generate fix code — use fix_site for that, or compare_sites to benchmark against a competitor. Pay per call ($1.00) via x402 — USDC on Base or Solana. Machine payment via signed X-PAYMENT header; see https://www.x402.org/. On payment_required, the response includes the full x402 payload with payTo/amount/asset.

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• read_canvas

  Agent Canvas Arena

  Global situational awareness. Returns the full 32x32 grid and reservoir stats. Warning: This is a heavy payload (1024 pixels). Use for broad scanning of opportunities.

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• intel_robots

  TunnelMind Data API

  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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• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results. Two-step flow: first call `brave_web_search` with `summary=true` to obtain `summarizer.key`, then pass it here. Pro AI tier required.

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• vertical_ai_agent_governance

  gapup-mcp

  Generates a comprehensive vertical AI agent workforce integration plan for CHROs, including governance frameworks, human-AI collaboration metrics, and upskilling recommendations. Inputs: industry vertical, workforce size, and current AI adoption level. Outputs: role-specific AI integration roadmaps, skill gap analysis, and performance benchmarks. Uses O*NET skill taxonomies and Gartner AI adoption trends. For best results with large datasets, pass async:true to avoid timeout.

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• site_directory

  Lodi Kids Activities

  Structured map of LKA's public URLs and content sections. Equivalent to llms.txt — gives an AI grounding agent the full topology of the site so it knows what's worth crawling/calling.

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• agents_list

  dialogbrain

  List all AI agents configured in the workspace. Returns agents with their basic info, trigger count, and knowledge collection count. Each agent's `description` field tells you when that agent is useful. If you're a router-style agent deciding whether to delegate via `agent.handoff`, read descriptions and pick the best fit. Use this to: - See all configured AI agents - Filter by status (active/paused/archived) - Get agent IDs for further operations

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• agents_create

  DialogBrain

  Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.

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• resource_data

  Data Gov In

  Fetch records from any India Open Government Data (data.gov.in) resource by its resourceId. Supports pagination, per-field filtering, field projection, and sorting. The resourceId is the UUID shown on a dataset's page on data.gov.in (and in its API URL, e.g. api.data.gov.in/resource/<resourceId>). Example resourceId 9ef84268-d588-465a-a308-a864a43d0070 is "Current Daily Price of Various Commodities from Various Markets (Mandi)" with fields like state, district, market, commodity, variety, grade, arrival_date, min_price, max_price, modal_price. Use resource_meta first if you do not know a resource's field ids.

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• postly_list_organizations

  Postly MCP

  Use this when an AI agent needs the Postly organizations available to the authenticated user or connection grant before choosing a workspace.

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• fact-check

  The Stall

  AI-powered claim verification. Searches DuckDuckGo, Wikipedia, Hacker News, and arXiv in parallel, then uses GPT-4o-mini to assess the claim and return a structured verdict: confirmed / contradicted / uncertain, with confidence score (0–1), supporting and contradicting evidence excerpts with source URLs, key entities, and step-by-step reasoning. Use before an agent acts on a factual assertion it received from another agent or user. $0.500/call.

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