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649,985 tools. Updated 2026-10-12 01:22

"How to run C++ code" matching MCP tools:

  • Run the classic operations-research teaching demo: pooled queueing (one shared queue, c servers) vs separate queues (c independent queues, one server each, λ/c traffic to each). Both runs have identical total capacity (c × μ) and identical total arrivals (λ), so the offered load ρ is the same; the only structural difference is whether arrivals share a queue or split into c isolated streams. The pooled configuration ALWAYS produces shorter waits — that's the whole teaching point. Use this when the user asks 'should we pool our resources?' / 'should we cross-train?' / 'why do banks have one line instead of c?' / 'what's the cost of siloing my call center into specialist queues?'. Returns both runs side by side with the pooled-vs-separate wait delta. ANTI-FABRICATION: numbers come from two real DES runs. Quote them VERBATIM.
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  • Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.
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  • [free] Describe this connector: flagship-first tools layer (search/answer as the front door), how to install (Claude Code / Cursor / npm), free vs paid tiers, and discovery URLs. Call this first.
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  • Get ONE test case's result inside a run: status code, timing, the response body and headers, every assertion outcome, extracted variables and any script output. This is the read to make when a run failed and you need to know why. The id comes from get_test_run's results[].id, not from get_test_results (run ids). Requires project context.
    ConnectorAPI key
  • Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and Go, the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as `query`. If the first results miss, rephrase once and retry.
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  • 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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  • Returns SEC Form C filings — Regulation Crowdfunding offerings, 2016-05→present: startup raises on Wefunder / StartEngine / Republic and other funding portals. One record per filing with the issuer (legal form, jurisdiction, incorporation date, website), the PORTAL (name + CIK + CRD), offering terms (security type — SAFEs appear as 'Other' with the description, price, target and maximum amounts, deadline, oversubscription), and the issuer's own DISCLOSED FINANCIALS (total assets, cash, revenue, net income, debt — current + prior fiscal year, dollars) plus employee count. Use this when the user asks about: startup crowdfunding activity, what a company raised on a portal, portal market share, early-stage issuers in a state, or revenue/assets of a crowdfunding company. filing_type maps the form family: 'offering' (C, C/A) | 'progress_update' (C-U) | 'annual_report' (C-AR — re-discloses financials yearly) | 'termination' (C-TR). is_withdrawal covers the -W variants. One issuer CIK typically has a chain: C → C-U → C-AR… — filter cik + sort asc to read it. Financial fields are null where a variant omits them. Reg A+ (Form 1-A 'mini-IPOs') is a different form family — planned as its own dataset; Reg D private placements are in get_private_placements. Pure-publisher posture: issuer-reported numbers as filed, parsed by KeyVex — Form C financials are self-reported and generally unaudited (reviewed at most); treat them accordingly.
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  • Train a new version of the model. Two steps: the first call changes nothing and returns what the run will use from the workspace's allowance plus a confirmation code; show that to the user, and only after they explicitly agree call again with that exact code. The user's yes is not the code. A code works once, for this model and these arguments, for 10 minutes.
    ConnectorOAuth
  • 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 6-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 by 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 6 min, above the observed range). 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; a standard production_ready run holds the code for 24 hours only, then an automatic clean-up deletes it), 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, as the 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 stored run keeps findings and recommendations; a production_ready standard run also holds the code for 24 hours, after which it can no longer be used and an automatic clean-up deletes it). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK (europe-west2, London). Code is sent to OpenAI (US) for the check, under the EU SCCs and UK Addendum (no-training): a certification keeps a stored run with us and is persisted at OpenAI; our operational security and cost logs, and OpenAI's abuse-monitoring retention, also apply. A private session cannot be certified. Payloads are 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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  • Run a generic M/M/c queue simulation. Provide an arrival rate (λ, arrivals/hour), a service rate per server (μ, customers/hour each server can finish), and a server count (c). Optional: distribution shapes, service coefficient of variation, run length. Returns per-hour metrics and an overall summary (avg wait, queue length, offered load, throughput). This is the primary tool for 'how many servers do I need?' / 'what's my average wait?' style questions. ALSO preferred over simulate_scenario for what-if questions about scheduled scenarios (Coffee Shop) when the user wants flat uniform numbers — pull the peak params from describe_scenario and run them here. That usually matches user intent better than collapsing a schedule. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run. Quote them VERBATIM in your reply. Do not round, estimate, or compute derived figures from training-data recall. If the user asks a follow-up about the same configuration, re-call this tool rather than recalling numbers from earlier in the conversation.
    ConnectorNo auth
  • 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 6-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 by 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 6 min, above the observed range). 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; a standard production_ready run holds the code for 24 hours only, then an automatic clean-up deletes it), 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, as the 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 stored run keeps findings and recommendations; a production_ready standard run also holds the code for 24 hours, after which it can no longer be used and an automatic clean-up deletes it). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK (europe-west2, London). Code is sent to OpenAI (US) for the check, under the EU SCCs and UK Addendum (no-training): a certification keeps a stored run with us and is persisted at OpenAI; our operational security and cost logs, and OpenAI's abuse-monitoring retention, also apply. A private session cannot be certified. Payloads are 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.
    ConnectorNo auth
  • Calculate a grade point average for a New Zealand university. Use for any NZ GPA question: 'what is my GPA', 'work out my grade point average from these grades', 'how much will a C drag my GPA down'. CRITICAL: New Zealand uses a NINE POINT scale, not the American 4.0 scale. A+ 9, A 8, A- 7, B+ 6, B 5, B- 4, C+ 3, C 2, C- 1, D and below 0. A GPA of 7.0 is a strong New Zealand result. Do NOT convert to or from a 4.0 scale: the bands do not correspond and any conversion will be wrong. It is credit-weighted, so a grade in a 45 credit paper counts three times a grade in a 15 credit one, and failed papers still count their credits at zero points. Args: courses: One dict per paper, each with "grade" (A+, A, A-, B+, B, B-, C+, C, C-, D+, D, D-, U, F or J) and "credits" (commonly 15). scale: Optional overrides if the institution weights grades differently. Returns: The GPA, the credits and weighted points it came from, and each paper's contribution so the result can be shown rather than asserted.
    ConnectorNo auth
  • List EVERY test inside a TestRail run, walking all pages server-side so the caller never handles offsets. Returns each test with its T-id (test instance, e.g. T1234), C-id (underlying case_id — feed this to testrail_get_case), title, and status_id. Filter by statusIds (TestRail status: 1=passed, 2=blocked, 3=untested, 4=retest, 5=failed) to e.g. list only failed tests when triaging. Capped at 20 pages (5000 tests); check capReached before concluding a test is not in the run.
    ConnectorOAuth
  • List all 71 available programming languages and their IDs for code execution. Common IDs: 71=Python 3.8, 63=JavaScript (Node.js), 62=Java, 54=C++ (GCC), 60=Go, 73=Rust, 51=C#, 46=Bash. Returns full list with compiler/interpreter versions (Judge0 CE)
    ConnectorNo auth
  • 'Run once now' — run ONE lane immediately through the same code the sourcing tick runs, with force: an OFF lane still runs (that is what run-once means while configuring), a DRAFT play still runs (how you try a lane before launch), a PAUSED play does not. REAL: provider calls are made, leads are inserted, the play's daily budget and the org's monthly lead quota are spent. Only call it when the human asked for a run in this conversation; to look without spending, use gtm_preview_source. Returns the run's funnel (scanned / dropped / droppedIntent / added / duplicates / capped), the unmapped ICP facets, and `skipped` + `reason` when it could not run (no_seat, needs_config, icp_empty, daily_cap, lead_quota, no_results, provider_error).
    Connector
    Destructive
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  • All company UIDs at a physical address. Street and city match exactly (case-insensitive); house number and postal code narrow it. Matches the physical address only, never the c/o line — for a cluster around a shared fiduciary, start from a company and use find_colocated_companies. Newest registration first. Pair with get_companies for detail.
    ConnectorOAuth
  • Amtrak station lookup by code or name — station name, 3-letter code, city/state, street address, timezone, coordinates, and how many trains are currently inbound. "Where is the Amtrak station in Denver?", "what is station code NYP". Example: amtrak_station_info({ station: "Denver" })
    ConnectorNo auth
  • Search the Hong Kong C&SD table catalogue by keyword (e.g. 'exchange rates', 'unemployment', 'merchandise trade') and get back matching table ids + titles to use with censtatd_get_table. Backed by the data.gov.hk open-data index of C&SD tablechart datasets. Note: not every C&SD table is indexed there; ids can also be read off the table URL on data.censtatd.gov.hk (the '310-31001' part of web_table.html?id=310-31001).
    ConnectorNo auth
  • PLN exchange rate for one currency over time. Specify the table (A/B for mid-rate, C for bid/ask) and a 3-letter ISO 4217 code (e.g. USD, EUR, GBP, CHF, JPY). Defaults to the latest rate; optionally pass a single date, last_n recent points, or a start_date/end_date window (max ~93 days, working days only). Use this for one currency; use exchange_rate_table for the whole list.
    ConnectorNo auth
  • Retourne les températures à cœur réglementaires obligatoires en cuisson par type d'aliment en restauration française (GBPH Restaurateur DGAL) : volaille 74 °C, bœuf haché 70 °C, porc 70 °C, poisson 63 °C, etc. Inclut le refroidissement rapide (de +63 °C à +10 °C en moins de 2 heures), la remise en température à +63 °C minimum et les recommandations de sécurité spécifiques aux populations sensibles (enfants, femmes enceintes, immunodéprimés). Distinct des températures de conservation disponibles via get_haccp_temperatures. [EN] Returns mandatory core cooking temperatures by food type in French catering (poultry 74°C, minced beef 70°C, fish 63°C…) plus rapid cooling and reheating rules. Optional 'type_aliment'.
    ConnectorNo auth
  • Retourne les températures à cœur réglementaires obligatoires en cuisson par type d'aliment en restauration française (GBPH Restaurateur DGAL) : volaille 74 °C, bœuf haché 70 °C, porc 70 °C, poisson 63 °C, etc. Inclut le refroidissement rapide (de +63 °C à +10 °C en moins de 2 heures), la remise en température à +63 °C minimum et les recommandations de sécurité spécifiques aux populations sensibles (enfants, femmes enceintes, immunodéprimés). Distinct des températures de conservation disponibles via get_haccp_temperatures. [EN] Returns mandatory core cooking temperatures by food type in French catering (poultry 74°C, minced beef 70°C, fish 63°C…) plus rapid cooling and reheating rules. Optional 'type_aliment'.
    ConnectorNo auth