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303,872 tools. Last updated 2026-07-15 23:16

"namespace:com.onrender.idea-reality-mcp" matching MCP tools:

  • Find MCP servers in the directory. Searches the standalone MCP directory (PulseMCP / official MCP registry import) unioned with x402 services that also expose an MCP endpoint. Returns normalised entries with a ready-to-use streamable-http `call_hint.mcp.url`. Args: intent: Natural-language description of the tool/capability needed. top_k: Max servers to return (1-20). chain: Optional payment-network filter for paid MCP servers. require_healthy: When true, only return servers marked health=ok.
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  • FREE, no payment required. Instant trust check of any MCP server: returns only the 0-100 score, A-F grade, tool count, latency and a one-line verdict — no detailed report. Use this FIRST, before integrating any third-party MCP server, to see at a glance whether it is technically trustworthy; an unreliable MCP wastes your tokens and can break your workflow. For the full actionable report (per-tool documentation coverage, functional probe results, score breakdown, plain-language summary) call evaluate_mcp; to pick between alternatives call compare_mcps. Set 'url' (required) to the target's MCP endpoint (Streamable HTTP), e.g. https://host/mcp.
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  • Returns VoiceFlip MCP server health and version metadata. No authentication required. Use this first to verify the server is reachable from your MCP client.
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  • Pre-flight reality check for on-chain AI agents. Verifies token addresses, prices, chain IDs, and contract existence before your agent acts. Catches hallucinated addresses that would cause irreversible losses. FREE: 5/day per IP, unlimited with API key. Returns: { verified: bool, hallucination_risk: 'none'|'low'|'medium'|'high'|'critical', correct_value, correction, source, confidence } Supports batch mode (up to 10 claims). Always run before any on-chain tx.
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  • Tracked data-center M&A / capex deal flow with the DCPI grid-reality verdict overlaid on each deal market — "what is the real play?". Returns recent deals (buyer, seller, value, market) + each market DCPI verdict and time-to-power; with a paid key, the per-deal autopsy read (long-dated land/power option vs near-term build vs queue gamble). Progressive disclosure to keep the default cheap: by default each read ships only a comparables COUNT (the verdict text is always included); pass comparables="summary" for the top-2 grounding signals, or comparables="full" to expand the complete cited set for a deal you're drilling into. Try: deal_autopsy limit=15.
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Matching MCP Servers

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    Pre-build reality check for AI coding agents — searches 5 real databases (GitHub, Hacker News, npm, PyPI, Product Hunt) to check whether an idea already exists before you build it.
    Last updated
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    MIT

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  • Pre-build reality check for AI coding agents. Scans GitHub, Hacker News, npm, PyPI & Product Hunt — returns a 0-100 reality signal before you build. Supports quick (2 sources) and deep (5 sources) parallel search.

  • Free read-only AI coding verification tools: verification-debt calculator, task-spec lint, search.

  • Returns Reality Graph's free fill-in template (v0) for a verifiable task contract: goal, non-goals, boundaries (may change / must not change / forbidden), 3-7 yes/no acceptance criteria, validation plan, expected evidence, assumptions, open questions — with a filled example and fill-in guidance. Write the contract before an AI agent runs; verify the result against it after. format='json' returns a machine-fillable JSON structure; default is a compact markdown skeleton. Set lang='de' for German. Static content, nothing stored.
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  • Purpose: Continuous self-calibration evidence. Each entry shows the auto-tuned lag_hours and sensitivity per cell, derived from real backtest outcomes. Proves the system adapts to measured reality rather than static heuristics. Triggers (casual questions too): "does the system self-correct?", "시스템이 스스로 보정해?", "how is it calibrated?", "튜닝 상태 보여줘", "is it adapting to what actually happened?". When to call: after get_prediction_accuracy, to show the system updates itself. Prerequisites: get_prediction_accuracy recommended for context. Next steps: get_monthly_accuracy_trend. Caveats: `last_backtest` timestamp indicates tuning freshness. Args: category: Optional category filter target_market: Optional target market filter Disclaimer: Information only, not investment advice.
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  • Pre-flight reality check for on-chain AI agents. Verifies token addresses, prices, chain IDs, and contract existence before your agent acts. Catches hallucinated addresses that would cause irreversible losses. FREE: 5/day per IP, unlimited with API key. Returns: { verified: bool, hallucination_risk: 'none'|'low'|'medium'|'high'|'critical', correct_value, correction, source, confidence } Supports batch mode (up to 10 claims). Always run before any on-chain tx.
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  • Fetches ONE deployment by its numeric id (from list_deployments). Returns its current status, the public access_point URL, the underlying VM id, AND the build_log — read this when status is 'build_failed' or 'error' to see exactly why the on-VM build/run failed (no SSH needed). Also returns a reality report_markdown showing the REAL provisioned size + cost (the plan was only an estimate; the user may have up-sized).
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