"Using LLM for Automated Code Review and Validation" matching MCP tools:

• analyze_reviewA

  ListingGood MCPA

  Analyze a negative Amazon review for root cause and a suggested response (async; 2 credits). Extracts the underlying issue from a critical review and drafts a brand-appropriate response angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: the negative review text (required). marketplace: marketplace code (default US). lang: zh or en (default en).

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• resolve_product_codeA

  EU Trade ExplorerA

  Resolve a free-text query or CN code(s) into validated product code(s) with descriptions -- the recommended first step before using a code as `product` in any other tool's `query`. Saves the search -> validate -> (optional) subtree round-trip: a bare keyword runs a search, a single code (or comma-separated list) is validated and described directly. Tip: Comext/CN nomenclature is frequently coarser than a colloquial product name (e.g. there is no code for "glass jars" alone -- only heading 7010, which bundles jars with bottles, flasks and closures). Check `has_subcodes` and, if useful, set `include_children=true` to see whether a finer sub-code is actually a better match before committing to one code for a whole report.

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• validate_vat_numberA

  Jithox E-Invoice ReadinessA

  Deterministic structure/checksum validation of an EU VAT number (incl. the Belgian modulo-97 checksum) with the normalized identifier and a stable issue code. Format plausibility only — not a live VIES result. Validation and readiness only; never sends a Peppol invoice and gives no legal, fiscal or compliance guarantee.

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• generate_strategic_reviewA

  TrendIdeaA

  Generate an executive-level strategic review report for an idea, synthesising all available validation data (market research, competition, SWOT, revenue model, VC score) into a concise go/no-go assessment with actionable recommendations. Requires prior validation data (run request_revalidation first if none exists). Returns cached report instantly if one exists, otherwise generates fresh analysis. Spends 2 credits only when generating new content. Not read-only; pass an ideaId you own.

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• netfluid__netfluid_voucher_checkA

  NetfluidC

  Performs a validation on a Netfluid voucher code. The voucher is not redeemed, only validated @param voucher_code: The Netfluid voucher code, format is 4 sets of integers, e.g. 1234-4321-1234-4321 @return: a json object

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• post_debug_codeA

  webbersites-x402-data-apiA

  LLM CODE DEBUGGING — POST {code, error} and get a diagnosis: what is wrong, the root cause, and a concrete fix with corrected code. Paste the failing snippet plus the error message or stack trace; any language, up to 20,000 chars combined. Optional {language} and {context} ('happens only on the second call'). Fast cheap LLM under the hood. Want deterministic no-AI lint instead? POST /api/lint/:language ($0.002). ($0.01 per call, paid via x402)

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• sandbox_backtestA

  dMoERA CreatorB

  Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.

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• ebook_checkA

  Ebook File CheckA

  Validates one EPUB (a directly hosted public URL or a file attached in chat) against the EPUB 2/3 specification using a pinned local EPUBCheck 5.3.0 engine run in a no-network sandbox. Each call takes exactly one source: the url parameter or the file attachment, never both. Returns the automated verdict: pass boolean, EPUB version, message counts by severity (fatal/error/warning/usage), findings grouped most-severe-first with EPUBCheck rule id (RSC-005, OPF-014, …), file path and line/column, plus a structural inventory (spine items, media types, remote-resource state), SHA-256 of the exact bytes, and truncation metadata. Use for one-file readiness and diagnosis: 'is this ebook valid', 'does this EPUB have specification problems before upload', 'what is wrong with it'. A pass means only that the bytes satisfied the automated profile; it never renders the book, judges accessibility, or guarantees any store will accept it, and human review of how the book reads and looks is always required. Downloads and validates a live EPUB under a 110-second call deadline; tell the user before the call.

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• chat_knowledgeA

  AlgoVault — Crypto Quant Trade CallsA

  Returns a synthesized natural-language answer with citations, grounded in the AlgoVault knowledge bundle (every MCP tool description, response shape, integration tutorial, and code example). Use when you need an explanation, code pattern, or how-to; for raw ranked snippets without LLM synthesis use search_knowledge (faster, no quota cost). Read-only: calls an LLM, no other side effects. Quota: Free 10/month, Starter 50, Pro 200, Enterprise 2000.

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• axint.validateA

  AxintA

  Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number, message: string, suggestion?: string }. Returns an empty array [] when validation passes. Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Inputs: source is TypeScript DSL text; strictness options affect diagnostics only and never emit Swift. Effects: read-only diagnostics; writes no files and uses no network.

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• fda_device_classA

  FDA Data MCPA

  Lookup FDA device classification details by product code. Returns device name, device class (I/II/III), medical specialty, regulation number, review panel, submission type, and definition. Requires: product code (3-letter code from 510(k), PMA, or device product listings). Related: fda_product_code_lookup (cross-reference across 510(k) and PMA), fda_search_510k (clearances for this product code), fda_search_pma (PMA approvals for this product code).

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• axiorank_author_detectorA

  AxioRank: Zero-Trust for AI AgentsA

  Describe a risk in plain English and AxioRank's AI proposes a custom content detector (regex or keyword) and SAVES it DISABLED for your review. An LLM never arms detection unattended. Outbound content categories only (secret/pii/destructive/injection/egress). Requires the `policies:write` scope and an AI-assessments-enabled plan (Team+).

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• get_iban_formatA

  ibanchecker-mcpA

  Return the IBAN format specification for a country, covering 90 supported IBAN-using countries. Returns JSON describing the country's total IBAN length, the BBAN layout (bank code, branch code, and account number positions and lengths), an example IBAN, and the SEPA-membership flag. Use this to understand or display how a country's IBAN is structured, to build input masks, or to explain a validation failure, not to validate a specific number (use `validate_iban` for that). An unsupported or unknown country code returns an error result describing the problem.

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• search_cpt_codesA

  Loa Healthcare PricingA

  Canonical code-lookup tool for this server. Search Loa's CPT/HCPCS index using exact codes, clinical terms, or consumer phrases. Use this first when the user does not already know the CPT code, before calling pricing tools.

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• get_agent_framework_docsA

  Velane CloudA

  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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• ado_review_xpp_prA

  ALM X++ MCP ServerA

  WHEN: reviewing a PR that modifies X++ code or AOT objects and you need D365-specific insights. Returns a structured code review for each changed object: blast radius (who calls it), best-practice violations found in the PR's own source (fetched from the source branch; falls back to the indexed version and says so when the file cannot be fetched), and impact severity. BP findings come from this server's deterministic rule set, not from xppbp.exe -- a PR is uncompiled, so Microsoft's checker cannot run on it. Use find_error_patterns for Microsoft rule text. Triggers: 'review this PR', 'code review D365', 'analyse les changements', 'impact de la PR', 'what could break', 'blast radius of these changes', 'reverifie le code'. Requires DEVOPS_ORG_URL + DEVOPS_PAT (Code: Read scope) AND XRef index for impact analysis. Combine with ado_post_pr_comment to post findings as inline review comments.

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• suggest_refactoringA

  ALM X++ MCP ServerA

  WHEN: developer wants to improve code quality before a PR merge or code review. Triggers: 'refactor', 'clean up', 'simplify', 'too long method', 'nested ifs', 'code smells', 'améliorer le code'. Suggest concrete refactoring actions for YOUR custom D365 F&O X++ code. [!] Only runs on custom/extension code (D365_CUSTOM_MODEL_PATH). Refactoring standard Microsoft code is not actionable. Analyzes: long methods (extract method), deep nesting (guard clauses), row-by-row operations (set-based), large switch statements (strategy pattern), hardcoded strings (constants), unprotected CLR calls (error handling), wide transactions (narrow scope). Returns before/after code examples.

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• analyze_receiptA

  Ardaro Purchase Document ReviewA

  Extract and validate structured expense data from receipt text or a provider-neutral receipt extraction. Use when an agent needs merchant, date, tax, total, line items, confidence, duplicate detection, and arithmetic validation before creating or reconciling an expense. The result is advisory and always requires human review. Paid tool: 0.25 USDC per accepted request. Requires an owner-authorized x402-capable client. Use get_document_example with service='receipt' for a fixed free example.

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• analyze_receiptA

  Ardaro Purchase Document ReviewA

  Extract and validate structured expense data from receipt text or a provider-neutral receipt extraction. Use when an agent needs merchant, date, tax, total, line items, confidence, duplicate detection, and arithmetic validation before creating or reconciling an expense. The result is advisory and always requires human review. Paid tool: 0.25 USDC per accepted request. Requires an owner-authorized x402-capable client. Use get_document_example with service='receipt' for a fixed free example.

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• institution_review_packetA

  LiquiLens — the Failure RadarA

  Build a compact cited human review packet from canonical fresh-vetted Indian radar records. Exact slug or uniquely normalized full name only; no fuzzy matching or new score. Preserves stale/dark gaps and validation hashes. Its content hash detects alteration; it is not attestation or a publication-time proof.

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• approve_agent_planA

  NexusTrade Financial MCPA

  ⚠ INDIRECTLY COSTS LLM CREDITS — approving the plan unfreezes the agent which then runs more planning + execution LLM calls. Manual approval required; do NOT call unless the user explicitly told you to advance their Aurora agent. Approve a semi-automated agent that is waiting in pending_plan_approval or pending_action_approval. Auto-detects which approval the agent needs and emits the matching state-machine event so the agent resumes execution. Returns 400 if the agent is not in a pending-approval state.

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