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304,965 tools. Last updated 2026-07-22 07:01

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

  • Validate a mixed batch of medical codes against their source terminologies. Useful for retrospective analysis of legacy databases — flag codes that no longer exist, surface ICD-10 → ICD-11 replacements, and grade activity status where the terminology exposes it. For each input `{ code, terminology }`, returns: - **valid**: whether the code exists in the source terminology. - **active**: whether the code is currently active. Null when the source doesn't expose an explicit active/inactive distinction at category level (CID-10, ATC, ICD-11, RxNorm, MeSH all return null today; SNOMED and LOINC return a real boolean). - **title**: the official label/name when available. - **replaced_by**: a successor code, populated today only for ICD-10 codes that have a primary ICD-11 mapping in the bundled WHO transition tables. - **source**: human-readable provenance of the validation (terminology + release/version). - **error**: non-null only when validation couldn't be performed (network error, SNOMED feature flag off, etc.). `valid: false` + `error: null` means "code not found"; `valid: false` + `error: set` means "couldn't validate". Terminology is **required per code** — auto-detection isn't supported because category codes like "A00" exist in both ICD-10 and CID-10. Accepted values: `icd11`, `icd10`, `snomed`, `loinc`, `rxnorm`, `mesh`, `atc`, `cid10`. Hard cap of 50 codes per call; codes are validated in parallel through their respective clients, so total wall time scales with the slowest upstream + its rate limit (worst case ~10 s for a full batch hitting ICD-11).
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  • Compile TypeScript source (defineIntent() call) into native Swift App Intent code. Returns { swift, infoPlist?, entitlements? } as a string — no files written, no network requests. On validation failure, returns diagnostics (severity, AX error code, position, fix suggestion) instead of Swift. Use: use when TypeScript DSL source should become Swift; use validate for cheaper preflight only. Inputs: source is TypeScript DSL text; options add sandbox, format, plist, or entitlement proof without writing files. Effects: read-only generated Swift/diagnostics; writes no files and uses no network.
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  • Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. The stubs cover IMPORTED dependencies only; the certified code's own enforcement branches (approval gates, policy checks, recovery paths) must be present in full. A `# ...` placeholder reads as an ABSENT control and is graded against you, not as shorthand for one that exists. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.
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  • Validate a single International Bank Account Number (IBAN) against the official ISO 13616 structure for its country. What it checks: the country code, total length for that country, the national BBAN structure, and the MOD-97 check digits. When the bank/branch code maps to a known institution, the response also includes the bank name, BIC/SWIFT code, and country. Returns JSON with fields such as `valid` (boolean), `countryCode`, `checkDigitsValid`, the `formatted` IBAN, and an optional `bank` object. On a malformed input the call still succeeds with `valid: false` and a `reason` (e.g. INVALID_FORMAT, INVALID_CHECKSUM); it does not throw for invalid IBANs. Use this when you have one account number to verify. For many IBANs prefer `validate_bulk_ibans`; to pull IBANs out of prose use `extract_ibans_from_text` first. No account data is stored; validation runs in memory and is discarded.
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  • Redeem a Loppee-issued subscription coupon for a business you manage: runs the exact same validation as validate_coupon, then creates a Stripe Checkout session for the chosen paid plan WITH the discount already applied, and returns its url plus the priced breakdown (original_cents, discount_cents, final_cents) and a redemption_id. IMPORTANT: this tool never charges anyone — the business owner must open the returned url and complete payment on Stripe's hosted page; until then the redemption is 'pending' and is released automatically if the checkout expires. Redeeming counts against the code's redemption limits while pending, so do not call this speculatively — use validate_coupon to check a code. Retry-safe/idempotent for an identical pending business + code + tier + period: it returns the same redemption and live Checkout session instead of creating another. A different plan, period, or coupon is refused with billing_checkout_in_progress while the existing business Checkout remains open; no competing session is created. Requires a scoped management key whose account_id + business_id match and whose allowed_actions include redeem_coupon (owner opt-in), plus enabled online billing (billing_not_configured otherwise); call get_agent_identity first. Machine-readable failures match the owner UI exactly: coupon_not_found (invalid code), coupon_inactive, coupon_expired, coupon_wrong_plan (code is scoped to a different plan), coupon_exhausted (total redemption cap reached), coupon_customer_limit (this business already used it), coupon_requires_paid_plan, plus the standard management auth errors (missing_api_key / forbidden_account / management_rate_limited), billing_already_active, billing_checkout_in_progress, and billing_not_configured. A coupon changes the subscription PRICE only. It never changes verification class, review reputation, plan entitlement, eligibility, or quality band; completed payment grants exactly the chosen plan.
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  • 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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Matching MCP Servers

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  • performance-review MCP — wraps StupidAPIs (requires X-API-Key)

  • AI code review for GitHub PRs with an MCP autofix loop for Claude Code and Cursor

  • Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. The stubs cover IMPORTED dependencies only; the certified code's own enforcement branches (approval gates, policy checks, recovery paths) must be present in full. A `# ...` placeholder reads as an ABSENT control and is graded against you, not as shorthand for one that exists. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.
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  • 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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  • Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.
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  • Purpose: Track-B (signal-driven) paper-trading decision log (Track B = the signal-engine decision path — indicator/Thompson-sampling driven; Track A = the LLM judgement path, see get_llm_trading_decisions). Triggers (casual questions too): "what did the system decide?", "최근에 뭐 샀어? 팔았어?", "why did you buy X?", "show recent buy/sell calls", "오늘 매매 판단 뭐 했어?", "any trades triggered today?". When to call: review recent automated decisions and their outcomes. Prerequisites: market://{market_id}/status recommended for context. Next steps: get_trade_history, get_signals. Caveats: paper-trading decisions only — no real-money order routing. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) decision_filter: Filter by decision (buy, sell, hold) hours_back: Only decisions within last N hours Disclaimer: Information only, not investment advice.
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  • Obtain the CivilQuants customer-side document pipeline — the toolkit the document-heavy skills (tender review, geotechnical / geo-environmental interpretation) use to chunk a tender pack and render a Word pack on the user's machine. Returns the self-unpacking chunking package, the pipeline discipline, and the python-docx render helpers. Universal (free + paid). NOTE: running the pipeline over real documents requires a code-execution client (Claude Code / Codex / VS Code) — a chat connector can read the toolkit but cannot execute it. The full kit is large (~60 KB); pass component='chunking'|'discipline'|'render' for one part (~20 KB each), or omit it for the whole kit.
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  • Free preview of recent US import-tariff changes. Optionally scope to an HTS code prefix (e.g. '6109' or '8471.30'). Returns up to 5 recent changes plus current duty-rate state for a scoped code. Full feed and the LLM brief are paid via x402.
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  • 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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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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  • Run validation and return the detailed execution trace. Shows the exact sequence of validation nodes that ran, whether each was deterministic, and the runtime of each node. Use for debugging, compliance audits, or understanding exactly what the platform checked. Different from validate: validate returns the verdict (PASS / FAIL / REVIEW) and the state vector summary. get_execution_trace returns everything validate does PLUS the per-node trace records. Use validate for normal operation; use get_execution_trace when you need to see inside the pipeline (debugging, audit prep, latency analysis). The trace is the same whether validation passes or fails — every node that ran is recorded with its inputs, outputs, and timing. Args: api_key: GeodesicAI API key (starts with gai_) structured_data: The data to trace validation for blueprint: Blueprint to validate against. Caller must own the Blueprint. Returns: status: "PASS" / "FAIL" / "REVIEW" / "ERROR" determinism_hash: cryptographic hash of inputs + rules trace: ordered list of node records, each with: node_name, node_type, deterministic (bool), runtime_ms, inputs, outputs node_count: number of nodes in the trace deterministic_count: how many nodes were deterministic state_vector: same state_vector validate returns
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  • List the 10 senior-QS skill methodologies CivilQuants exposes (tender review, risk assessment, QS measurement/contract advice, geotechnical + geo-environmental interpretation, earthworks, preliminaries, pavement design, subcontract analysis). Universal discovery — both tiers see the full list. Returns each skill's slug, title, one-line summary and tier; then call get_skill(skill=<slug>) to fetch the methodology body. The skills are paid-tier; a free caller gets a sign-up prompt from get_skill. NOTE: the document-heavy skills (tender review, the interpretation skills) need a code-execution client (Claude Code / Codex / VS Code) plus the chunking pack from get_document_pipeline to run a real tender pack — on a chat connector you can read the methodology but cannot chunk/parse files.
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  • Deterministically validates a FILLED task contract (the JSON structure from get_task_contract_template): completeness of goal/non-goals/boundaries, decidability of each acceptance criterion (vague words, missing measurable markers), automated checks in the validation plan, expected evidence, and leftover placeholders. Returns a verdict (PASS / PASS WITH WARNINGS / FAIL), four dimension scores, and a concrete fix per finding. Validates form and completeness, not correctness. No LLM, nothing stored. lang='de' for German.
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  • Validate a TSX/JSX snippet against real @marmoui/ui prop signatures. Returns `{valid, issues[]}`. Prefer `review_generated_code` for post-generation review (it also auto-fixes namespace patterns and suggests related patterns). Use this tool when validating user-provided or existing code rather than newly generated code.
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  • List Blueprints owned by the calling account. Returns each Blueprint's name, workflow identifier, mode, and field/rule/constraint counts. Use the workflow_name as the 'blueprint' parameter when calling validate. Blueprint modes — important for agents that summarize results: - "observe" (Observation mode): the platform validates data against the Blueprint's rules and returns PASS, FAIL, or REVIEW with repair suggestions. Validation is detection-only; the caller decides what to do with the result. - "enforce" (Execution mode): the platform performs the same validation AND authorizes downstream side effects. Side effects are blocked if validation fails. In this mode, rules that compute derived values (like totals) publish the platform's computed result as authoritative, so downstream consumers see the canonical value rather than the agent's. Both modes run the same rule checks. The difference is what happens after validation, not during it. Scope: only Blueprints created by the calling account are returned. Use validate / repair / etc. with the workflow_name to operate on them. Args: api_key: GeodesicAI API key (starts with gai_)
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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