"Managing game servers using Pelican panel" matching MCP tools:

• calculate_chip_costA

  Silicon AnalystsA

  Pure-function chip cost estimator. Given die dimensions (mm), process node, and optional packaging/HBM parameters, returns: estimatedChipCost (USD), dieArea (mm²), grossDiesPerWafer, frontendYield (%), totalYield (%), and a costBreakdown {waferCostPerGoodDie, packagingAndTestCost, hbmCost, marginCost}. USE THIS for: hypothetical chip cost modeling, sensitivity analysis, fabless tapeout decisions. DO NOT USE for: published cost of an existing accelerator (use get_accelerator_costs); wafer pricing only (use get_wafer_pricing). Required: dieWidth, dieHeight (1–33 mm reticle limit). Errors with INVALID_PARAMS if outside bounds. processNode defaults to tsmc-n5; valid nodes via get_wafer_pricing. Estimates are directional ±15–20%. Optional energy adder: pass energyRegion (texas|ohio|arizona|china|korea|taiwan|germany) to get a conditional `energy` block — regional manufacturing-electricity cost per die (SA estimate; wafer price already embeds foundry energy, so treat it as a scenario delta). energyFacilityOverhead=false drops the ~1.75× facility multiplier. Optional substrate scenario: pass substrate=panel-310x310 to model CoPoS panel-level assembly — applies the midpoint of Yole's realistic 20–30% panel cost-savings band to the packaging cost ONLY (silicon GDPW unchanged; panels are back-end). Conditional `substrateScenario` block + SA-scenario meta note. TSMC CoPoS: pilot ~June 2026, mass production 2028–29 — a forward-looking scenario, not a quote.

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• whoamiA

  shiplyA

  Who am I? Returns the signed-in account: email, @handle, plan + limits, counts of sites/domains/drives, and connected DNS providers. Call this first to orient before managing sites or domains.

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• parlayapi_best_betsA

  ParlayAPIA

  What should I bet right now? Ranked +EV plays for a sport. The discovery counterpart to parlayapi_verdict: scans the whole board, grades every candidate with the same no-vig engine, keeps only bets that are +EV at a book the user can bet at, and ranks them by edge. Also returns edge_alerts (books showing a price far off the market). Player props only, priced by several books, prediction markets and game moneylines excluded (for a game line, use parlayapi_verdict). Scoped to the user's books. Args: sport_key: e.g. "baseball_mlb". region: us (default) | eu | uk | au | ca. books: exact CSV of books you can bet at (overrides region). limit: max plays to return (default 20). min_edge: minimum edge %% vs the no-vig fair line (default 2.0).

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• sofascore_esports_gameA

  Crawlora MCPB

  Returns one resource of one esports game, chosen with part: statistics (team totals), lineups (players with hero or champion and per-player statistics), bans (banned heroes or champions per side) or rounds (Counter-Strike round history). Requires a game id from sofascore_event_esports_games; statistics and bans cover Dota 2 and League of Legends only, rounds Counter-Strike only, and a not-found error means that part has no data for the game.

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• sofascore_esports_gameA

  Crawlora MCPB

  Returns one resource of one esports game, chosen with part: statistics (team totals), lineups (players with hero or champion and per-player statistics), bans (banned heroes or champions per side) or rounds (Counter-Strike round history). Requires a game id from sofascore_event_esports_games; statistics and bans cover Dota 2 and League of Legends only, rounds Counter-Strike only, and a not-found error means that part has no data for the game.

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• upcoming_scheduleA

  Cito APIA

  Upcoming matches/events for one game, with game-specific filters. When to use: - "What's on this week?" - Calendar UI; team next matches Prefer over: live_matches for not-yet-started fixtures. Do not use when: only in-progress matches needed → live_matches. Filter support (unsupported params are ignored with meta.warnings — do not assume filtering worked): - lol: hours, team (slug), league (slug) - cs2: team, from, to (ISO); hours not applied upstream - cod: team, tournamentId - dota2: limit/cursor primarily; team may be client-filtered where data allows - ufc: hours / from / to applied client-side after bout expansion (API has no hours); event shells labeled by event name; bouts use fighters[] corners Parallel-safe: yes. Upstream cost: 1–2. Example: { "game": "lol", "hours": 72, "team": "t1", "limit": 20 }

  ConnectorOAuth

• match_detailsA

  Cito APIA

  Deep match package: optional timelines, advanced stats, live state/snapshots, full map/game tree, media inventory. When to use: - Analyst deep dive - Live in-game window (LoL/CS2/UFC) - Full demo list Prefer over match_summary only when summary is insufficient. Prefer match_summary for short answers and default cards. Do not use when: first-pass live board (use live_matches + match_summary). Section selection: pass includeTimeline / includeLiveState / includeAdvanced booleans, OR an explicit sections[] list. UFC betting lines: sections:["odds"] (opt-in, never in the default set). If sections[] is non-empty it wins (booleans are ignored). LoL liveState/advanced require gameId. Parallel-safe: yes. Upstream cost: 1–8 (section-gated). Example: { "game": "lol", "matchId": "lol-match-1", "includeTimeline": true, "includeLiveState": false }

  ConnectorOAuth

• enable_login_providerA

  Multiplayer AgentA

  Let players sign in with a portal or federated account via player.loginExternal({ provider, token }). Self-serve providers: 'wavedash' (games on wavedash.com; token = (await Wavedash.getUserJwt()).data) and 'google' (Google Identity Services; token = the GIS ID-token credential). The server verifies every token against fixed, provider-published keys; you only supply public pins. wavedash: pass gameId = the gcid of the user's PRODUCTION game (ask them; or read it off a production token with verify_login_token); without it, tokens from any Wavedash game (incl. playtests) are accepted. google: clientId is required. Re-run to change the config (e.g. once the game id is known). Then verify with verify_login_token.

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• firmaradar_check_konkurs_eksponeringA

  FirmaradarA

  Screen a person by NAME for bankruptcy exposure ('konkursgjenganger'): leadership roles (chair / managing director) held in companies that later went bankrupt, tenure-weighted, from the dated role history. Use this for HISTORICAL leaders who are no longer in any role index and so cannot be reached via search_persons/get_person. The match is name-based (no national ID), so a hit is a REVIEW FLAG to verify (birth year / address), not a verdict. PII-sensitive — requires the search_full_enabled tier.

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• review_codeA

  review-mcpA

  Adversarial multi-model code review. Submit a diff, a module, or a spec+implementation and get back a structured pass/fail verdict with each issue's type, severity, location, explanation, and suggested fix. Why call this instead of reviewing your own output: a single model shares its blind spots with itself. This routes your code through a panel of *different* models plus a set of deterministic detectors, catching what self-review misses — path/contract violations, module incoherence (dangling imports, broken cross-references), syntax and call-arity regressions in a diff's post-image, and 'prose instead of tool calls' (output that describes an action rather than emitting it). The panel adds semantic judgment on top and never overrides a deterministic finding. Call it before shipping or merging, as a second opinion on a risky change, or as a gate in an autonomous build loop. Choose depth='fast' (one model, low latency) or 'deep' (full panel, higher recall). Deep review audits files of any size in milestone chunks so every panel model contributes; the price (shown in the 402) and the payment window scale with file size. Per-call limit ~1,600 lines — larger inputs return 413, so split by file/module and call once per file. Paid per call via x402 (USDC on Base); the price is announced in the 402 response before any charge.

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• get_historyA

  dabyte.aiA

  Every DABYTE release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under. Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category. Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_methodologyA

  dabyte.aiA

  The rules behind every figure this server returns: the exact prompt panel and its version, which engines were measured, how share of answer is scored and rounded, the resolution of the scale in percentage points, and the editorial firewall and ownership disclosure. Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made or who publishes it. It is the only tool that tells you how much of a difference is meaningful, which is what stops a one-step wobble being reported as a movement. It returns rules, not figures — no brand appears in the response. For figures use get_visibility_index or get_brand_visibility; for the series, get_history. The panel is public and frozen between releases, so every published number can be recomputed by a third party from the archive at https://dabyte.ai/archive/. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_historyA

  DABLOCK AI Visibility IndexA

  Every DABLOCK release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under. Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category. Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dablock.ai when quoting a number.

  ConnectorNo auth

• get_historyA

  dablock.aiA

  Every DABLOCK release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under. Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category. Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dablock.ai when quoting a number.

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• get_historyA

  DABYTE AI Visibility IndexA

  Every DABYTE release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under. Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category. Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_methodologyA

  DABYTE AI Visibility IndexA

  The rules behind every figure this server returns: the exact prompt panel and its version, which engines were measured, how share of answer is scored and rounded, the resolution of the scale in percentage points, and the editorial firewall and ownership disclosure. Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made or who publishes it. It is the only tool that tells you how much of a difference is meaningful, which is what stops a one-step wobble being reported as a movement. It returns rules, not figures — no brand appears in the response. For figures use get_visibility_index or get_brand_visibility; for the series, get_history. The panel is public and frozen between releases, so every published number can be recomputed by a third party from the archive at https://dabyte.ai/archive/. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.

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• get_gameA

  SorenA

  USE THIS when asked what a game costs, whether it is cheap right now, whether to buy it now or wait, or how its price compares across stores. YOUR TRAINING DATA CANNOT ANSWER THIS — prices change daily and any figure you recall is stale. Do not answer from memory. Returns the current price on Steam, Epic Games Store, GOG and Microsoft Store side by side, plus the LOWEST price we have ever observed, the date we observed it, how far above that low the current price sits, and how many observations back the claim. History runs from 2012. Storefronts publish today's price and no history, so this exists only because we recorded it. Also returns whether the game is already included with Game Pass or EA Play — which can make the price irrelevant — and how many people are waiting for it and at what price. Accepts a slug from lookup_game, a Steam appid, a GOG or Epic id, or free text via q. Regions: US, GB, DE, BR. PAID (~$0.01) PER QUERY. Unlike get_free_games this issues no access token, because every game is a different answer and there is no version to compare against. Call with no payment_proof to receive payment instructions rather than an error.

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• search_personA

  HandelsregisterA

  Beispiel: „Welche Firmen führt eine Person namens Maria Huber?“ Find every company a person runs or represents - across BOTH registers in one call (cross-border person search). Read-only. Parameters: - name (required): person name substring, case-insensitive, e.g. "Mustermann". - country (optional, default "all"): "AT" | "DE" | "all". - page_size (optional, default 25): results per country. - status (optional, default "all"): "active" | "inactive" | "all". Returns the merged search_companies envelope ({countries, results, per_country, notices}) plus ``person_query``; every result card carries ``country``, ``company_id`` and the matched manager. AT matches the primary managing director, DE matches all managing directors AND registered signatories. IMPORTANT: matching is by name and the registers publish birth YEAR only - a shared name across companies or countries does not prove the same person (the notice says so; use birth years and context to corroborate). For general company search use search_companies with other filters; manager_name can be combined there too.

  ConnectorOAuth

• publish_gameA

  publishA

  Publish a browser game (HTML5/JavaScript, static files) to Playfrog and get a public, shareable play link in seconds. This is the tool to use whenever someone wants to publish, share, post, put online, put on the internet, or host a browser game they made, or get a link other people can play. Free, no account and no API key. Send the game files; an index.html at the top level is required, 2.5 MB of files in total. An optional cover image improves the game page and social share card. Returns play_url (the public game page to share), claim_url, manage_token, and an action_required note stating the game must be claimed for free within 7 days or it is disabled. Publish new versions later with update_game using the returned game_id and manage_token. Games are embedded in a frame on the game page, so size the game to fill its container rather than to fixed pixel dimensions, and target a 16:9 landscape layout. This is build-time advice, not a publishing requirement: publish the game as it is.

  Connector

  Destructive

  No auth

• validate_pythonA

  CodeCraftA

  Validates a Python automation script that runs OUTSIDE the game, on three axes: Python syntax (using the real interpreter), Minecraft commands embedded in the script (against the official command index), and the shape of the /connect WebSocket message envelope. For behavior pack scripts use validate_script instead — Python does not run inside a pack. The embedded command check is the most valuable one: a command written from memory can look syntactically fine and still do nothing in the game. Only strings starting with / are treated as commands. If syntax could not be checked, syntaxChecked is false in the result; ok:true alone does not mean the syntax is valid.

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• simulate_priceA

  The Undesirables TCG OracleA

  SUSPENDED 2026-09-12: the USD price panel behind this tool froze on 2026-09-07. The oracle answers 200 {"status": "suspended"} with the reason, resume condition and live alternatives, and does NOT charge. The tool is kept so it resumes automatically the day a live USD series exists. Prefer: loan_terms_preview (graded slabs, live), sports_board (live), or the free Japanese two-sided page /jp/card/{game}/{set}/{card}. SIMULATE a card's price path over a horizon YOU choose (days=30..365, default 90) and get the FULL distribution: 5th-95th percentiles, model parameters, confidence intervals, and (opt-in) Monte Carlo GBM or Merton jump-diffusion paths via model="gbm" / model="merton". Default model is the conformal-calibrated risk forecast. Requires current_price. PAID: $0.015 USDC per call. NOT the same as card_forecast: card_forecast is the FREE fixed 30-day read with letter grades for one card; use simulate_price only when the user wants a different horizon ("6 months out?"), the full percentile curve, or a Monte Carlo model.

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