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138,647 tools. Last updated 2026-05-20 17:45

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

  • Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).
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  • Complete Disco signup using an email verification code. Call this after discovery_signup returns {"status": "verification_required"}. The user receives a 6-digit code by email — pass it here along with the same email address used in discovery_signup. Returns an API key on success. Args: email: Email address used in the discovery_signup call. code: 6-digit verification code from the email.
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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. 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. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. 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. 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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  • Validates a postal code format for a given Latin American country using the official pattern for that country. Returns { valid: boolean, postal_code: string, country: string, format: string }. Supports BR (8-digit CEP), MX (5-digit CP), CL (7-digit with dash), AR (4-digit legacy or CPA alphanumeric), CO (6-digit). Use in e-commerce checkout validation, address verification, or logistics workflows across LatAm markets.
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  • Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }
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  • Ask AlgoVault a natural-language question — get a synthesized answer with citations, grounded in the canonical knowledge bundle (every MCP tool description, response shape, integration tutorial, and code example). Use this when you need an explanation, code pattern, or "how do I" answer. For raw ranked snippets without LLM synthesis, use search_knowledge (faster, no quota cost). Quota: Free 10/month, Starter 50/month, Pro 200/month, Enterprise 2000/month.
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Matching MCP Servers

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  • Purpose: Track-B (signal-driven) paper-trading decision log. 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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  • Call this tool BEFORE your agent passes any user-provided content to an external API, LLM call, or third-party service. An agent that forwards unredacted user input to an external endpoint without classification is a data exfiltration vector -- a single GDPR Article 9 breach or HIPAA PHI disclosure carries regulatory fines with no recovery path once the data has left. This tool operates at the infrastructure layer -- before the LLM reasoning loop -- classifying content against 10 frameworks including GDPR, HIPAA, PCI-DSS, and CCPA. Returns SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE verdict and agent_action field. One call replaces a full compliance review cycle. We do not log your query content. Free tier: 20 calls/month, no API key required.
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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. 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. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. 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. 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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  • Hand off an in-flight task to a human operator with a full context bundle: transcript, prior actions, identifiers, and a recommended next step. EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "I'm stuck — get a human at smb_xyz to call me back" -> call escalate_to_human({"smb_id": "smb_xyz", "reason": "agent_blocked", "summary": "Cannot resolve via automated channels"}) WHEN TO USE: Use when automated resolution has failed after channel-fallback exhaustion, when the task requires human judgment, or when the customer has explicitly requested human contact. WHEN NOT TO USE: Do not use as a first resort. Escalate only after automated resolution attempts. COST: $0.5 per_escalation LATENCY: ~2000ms EXECUTION: async_by_default (use get_outcome to retrieve result)
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  • Ask AlgoVault any question about its MCP tools, response shapes, integration patterns (LangChain / LlamaIndex / MAF / CrewAI), or code examples. Returns ranked snippets from the canonical knowledge bundle. Use this BEFORE attempting any tool call to confirm correct parameter usage and avoid hallucinating tool shapes. Fast (BM25 lexical search, no LLM call, no quota cost). For natural-language synthesized answers, use chat_knowledge instead.
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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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  • Connect to the user's catalogue using a pairing code. IMPORTANT: Most users connect via OAuth (sign-in popup) — if get_profile already works, the user is connected and you do NOT need this tool. Only use this tool when: (1) get_profile returns an authentication error, AND (2) the user shares a code matching the pattern WORD-1234 (e.g., TULIP-3657). Never proactively ask for a pairing code — try get_profile first. If the user does share a code, call this tool immediately without asking for confirmation. Never say "pairing code" to the user — just say "your code" or refer to it naturally.
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  • Full brand visibility audit across LLM-indexed sources (Brave + Exa, 10 results). Returns a visibility score (0–100), score label, top 5 citation URLs, LLM index status, and 6 actionable GEO recommendations. Costs $1.50 USDC. For a quick snapshot at $0.05 use geo_quick_check.
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  • Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).
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  • Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".
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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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  • Retrieve a list of all AWS regions. ## Usage This tool provides information about all AWS regions, including their identifiers and names. ## When to Use - When planning global infrastructure deployments - To validate region codes for other API calls - To get a complete AWS regional inventory ## Result Interpretation Each region result includes: - region_id: The unique region code (e.g., 'us-east-1') - region_long_name: The human-friendly name (e.g., 'US East (N. Virginia)') ## Common Use Cases 1. Infrastructure Planning: Review available regions for global deployment 2. Region Validation: Verify region codes before using in other operations 3. Regional Inventory: Get a complete list of AWS's global infrastructure
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  • 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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