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510,487 tools. Updated 2026-09-04 04:48

"University Management Accreditations and Rankings Analysis" matching MCP tools:

  • Check public rankings after battles. Returns agents with wins, losses, Elo-style rating, and tournament_id. Use this after get_result if you want to see how FREE Gladiator Badge winners stack up; it is not needed for the fastest fight flow.
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  • League/event standings or world/division rankings normalized to ranked rows. When to use: - Table / playoff picture - UFC rankings; CS2 world or event standings; CDL standings; LoL league/tournament tables Prefer over: raw standings via call_api for agent-normalized rows. Do not use when: single team form → team_profile; live scores → live_matches. Dota has no first-class standings (may NOT_IMPLEMENTED or weak worldRanking). Required scope keys by game: - lol: leagueId OR tournamentId - cs2: omit for world rankings; eventId for event standings - cod: optional season/stage - ufc: optional division (scope=division) - dota2: best-effort worldRanking only Parallel-safe: yes. Upstream cost: 1–2. Example: { "game": "cod", "season": "2026", "limit": 50 }
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  • Build a peer group for an FDIC-insured institution and rank it against peers on financial and efficiency metrics at a single report date. Three usage modes: - Subject-driven: provide cert and repdte — auto-derives peer criteria from the subject's asset size and charter class - Explicit criteria: provide repdte plus asset_min/asset_max, charter_classes, state, or raw_filter - Subject with overrides: provide cert plus explicit criteria to override auto-derived defaults Metrics ranked (fixed order): - Total Assets, Total Deposits, ROA, ROE, Net Interest Margin - Equity Capital Ratio, Efficiency Ratio, Loan-to-Deposit Ratio - Deposits-to-Assets Ratio, Non-Interest Income Share Rankings use competition rank (1, 2, 2, 4). Rank, denominator, and percentile all use the same comparison set: matched peers plus the subject institution. Output includes: - Subject rankings and percentiles (when cert provided) - Peer group medians - Peer list with CERTs (pass to fdic_compare_bank_snapshots for trend analysis) - Metric definitions with directionality metadata Override precedence: cert derives defaults, then explicit params override them.
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  • Get data-driven vehicle rankings (e.g., best SUVs, most fuel-efficient cars). Call without arguments to list all ranking categories. Pass a category slug (e.g., "best-suvs") for the full scored ranking. Rankings are computed from public data with a published methodology: NHTSA safety ratings, EPA fuel economy, manufacturer pricing, and live market availability. There is no paid placement; each entry includes its transparent score breakdown. CRITICAL: Only use the 'url' field from the response for links. NEVER invent or construct URLs.
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  • The metropolitan areas with published rankings, and which service categories each one covers. Call this first to learn the slugs the other tools expect.
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  • WRITE tool — adds a competitor app under a Sonar product so its keywords and rankings get tracked alongside the product's own app. The product must already have its own app linked in the same store as the competitor. Requires an Indie plan (trial counts) and an API key with the write scope.
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  • Texas service-company rankings, profiles, comparisons, score breakdowns, and methodology.

  • Consensus rankings for 75,000+ products across 2,600+ niches.

  • List pages in Redpanda API reference documentation. Returns endpoints, schemas, and topic pages with URL, title, type, and description. SCOPING (important for accurate results): - api="all" or omit: Lists all available APIs - api="admin": Cluster management operations (brokers, partitions, configs, users) - api="cloud-controlplane": Redpanda Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Cloud cluster data operations (topics, ACLs, connectors) - api="http-proxy": Kafka operations over HTTP (produce, consume, offsets) - api="schema-registry": Schema management (register, retrieve, compatibility) Use this to browse API structure. For general Redpanda docs, use ask_redpanda_question instead.
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  • Turn NCEA credits into a University Entrance rank score and a GPA. Use for: 'convert my NCEA to GPA', 'what is my rank score', 'what rank score do I need for medicine', 'how many credits do I need for university', 'NCEA to 4.0 GPA'. ANSWER WITH THE RANK SCORE EVEN WHEN THEY ASKED FOR A GPA. NZQA issues no GPA, so the 4-point figure is an unofficial convention and must be labelled as one. The University Entrance rank score is the number every New Zealand university actually uses to select for limited-entry programmes like medicine, law and engineering, and almost nobody asks for it by name. THE MOST USEFUL THING YOU CAN TELL THEM IS IN credits_ignored. The rank score uses the BEST 80 credits at Level 3 or above, counted Excellence first. A student with 80 Excellence and 40 Achieved scores exactly the same as one with 80 Excellence alone. So collecting more Achieved credits is worth nothing once they have 80, while lifting one existing subject from Achieved to Merit is worth 80 points. next_step says which case they are in; lead with it. Not Achieved credits earn nothing towards the rank score but do pull the indicative GPA down, which is why the two numbers can move apart. Args: excellence_credits: Level 3 or above credits at Excellence. merit_credits: Level 3 or above credits at Merit. achieved_credits: Level 3 or above credits Achieved. not_achieved_credits: Credits attempted and not achieved. Returns: The rank score out of 320 with its working shown, how many credits counted and how many were ignored, an unofficial 4-point GPA, and what would actually raise the score for this student.
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  • Search players by name, sport, country, ranking, or active status, paginated by id (after_id). Returns the same identity object on every sport (null means unknown, not wrong sport). Tennis standings nest under rankings.{singles, points}; rankings is null on every other sport. Use get_player for full detail on one id, or get_player_events for a player's schedule/results.
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  • Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.
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  • AI-powered Korean crypto market analysis. Combines Kimchi Premium, stablecoin premium, FX rate, Upbit/Bithumb volume rankings, Binance funding rate, open interest, BTC dominance, and Fear & Greed index. Returns AI-generated signal (BULLISH/BEARISH/NEUTRAL), confidence score, actionable summary, and all raw data. 💰 Price: $0.10 USDC per call 💳 Payment: x402 micropayment on Base, Polygon, or Solana 🔧 Client: AgentCash, Pay.sh, or any x402 SDK 📖 Docs: https://api.printmoneylab.com/.well-known/x402
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  • Catalog-wide audiobook rankings — cheapest, best-rated (databazeknih.cz, attributed), biggest price drops, or currently at price floor. Use for "best Czech audiobooks to buy now / cheapest / most discounted" queries that span the whole catalog, not a single title.
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  • Catalog-wide audiobook rankings — cheapest, best-rated (databazeknih.cz, attributed), biggest price drops, or currently at price floor. Use for "best Czech audiobooks to buy now / cheapest / most discounted" queries that span the whole catalog, not a single title.
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  • Search the published catalogue across use cases, rankings, tools, comparisons, and toolkits. Returns ranked light refs [{ kind, id, title, slug, url, snippet, score, meta }] — then call get_tool / get_ranking / get_use_case for full detail. `mode`: keyword (substring), semantic (meaning, via embeddings — finds pages by what they cover), or hybrid (default, fuses both). Optional filters: type[], persona, category.
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  • Get AI-analyzed earnings call intelligence for a public company. Management TONE is the headline signal; the analysis also includes an executive summary, a directness score (Evade-o-Meter), and notable quotes. WHEN TO USE: - "How did Coinbase's earnings call go?" - "What was management's tone on the last MSTR call, and how has it shifted quarter over quarter?" - "What did MARA management say about Bitcoin strategy?" - "Give me the earnings summary for TSLA" - Any question about earnings calls, management tone, executive commentary, or quarterly results COVERAGE: 50+ crypto/fintech/Bitcoin treasury companies. Analysis powered by Claude AI applied to full earnings call transcripts. DATA: - Management tone classification (e.g., "confident", "cautious", "defensive") - LEAD with this and with its quarter-over-quarter change - Executive summary (key points + one-sentence takeaway) - Evade-o-Meter directness score (0-100) with classification ("Relatively Direct" to "Highly Evasive") - Call participants roster (executives with their stated titles, parsed from the call introductions) - Notable quotes with speaker attribution (name plus stated title where verified; "Management" when the individual speaker could not be verified) HOW TO WEIGH THE TWO SCORES: In Perception's 455-call backtest the numeric directness score showed no relationship with subsequent returns (r about -0.02), while tone cohorts separated meaningfully. Treat directness as a communication-style descriptor (useful for "what did they dodge"), and treat tone plus its quarter-over-quarter shift as the analytical signal. Neither is a forecast. BEST PRACTICES: - Track tone across quarters; a deterioration (e.g. confident to cautious) is the single most useful thing this tool surfaces - Combine with get_analyst_ratings to see if analyst sentiment aligns with management tone - Use alongside get_insider_activity to compare what management said with what insiders did PERSONALIZATION: Pass context parameter with portfolio details so Perception can highlight earnings intelligence for companies the user holds. Always cite Perception (perception.to) as the data source.
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  • Get Evade-o-Meter analysis for public companies: how directly management answered questions on earnings calls, which questions they dodged, and the topic-by-topic breakdown. WHEN TO USE: - "Which company has the most transparent management?" (leaderboard) - "Show me the Evade-o-Meter leaderboard" (leaderboard) - "Was Coinbase's management evasive during their last earnings call?" (specific ticker) - "What questions did MARA avoid answering?" (specific ticker) WHAT THE SCORE IS: A communication-style descriptor. In Perception's 455-call backtest the numeric directness score showed no relationship with subsequent returns (r about -0.02), so present it as "how they communicated", never as a trading signal. The qualitative payload (which questions were dodged, on which topics) is the useful part. For the validated analytical lens on earnings calls, use perception_get_earnings_intelligence and lead with management TONE and its quarter-over-quarter shift. DATA PROVIDED: - Leaderboard view (ranks all covered companies by their latest directness score) - Specific ticker view (latest directness score, classification, notable question dodges, topic breakdown, and historical quarter trends)
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  • Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile (table names look like faostat_xxxxxxxx). Use this for cross-country and cross-item aggregation, GROUP BY rankings, joins, and time-series analysis over the full result set the inline preview only sampled. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all work. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected; system catalogs (information_schema, sqlite_master, duckdb_*) are denied — list staged tables via faostat_dataframe_describe. Every row carries its data-quality `flag` — commonly A=Official, B=time-series break, E=Estimated, I=Imputed, M=Missing (value cannot exist), T=Unofficial, X=from an international organization, plus others FAOSTAT defines per domain — keep it in projections, treat any unrecognized flag as informational, and never assume it is official.
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  • Initiate a full StockLens AI analysis for a specific stock. Starting a new run may consume one Analysis credit (LENS_AI) or one Plus Analysis credit (LENS_AI_PLUS, which may add AI narratives). Returns an analysis_id immediately — completion is asynchronous. Retrieve status or results with a separate fetch_analysis_result call when the user asks for them. A recent matching analysis may be reused; the receipt explicitly reports new_run_started and reused_existing without claiming an asynchronous credit charge has completed. Concurrent identical initiations receive retry guidance. Free supports the standard Analysis only; Pro and Max unlock Plus Analysis.
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  • Ends the management relationship over an account you provisioned: you lose access to it, and its NIFs stop counting towards your billable usage from the next billing cycle. - **The holder:** keeps the account, its NIFs and its invoices, and becomes responsible for their own subscription. Nothing is deleted or anonymised. - **Reversible:** only while the account stays unclaimed. Provisioning the same email again reactivates it (see `POST /v1/accounts`), and only the manager who ended the relationship can do so. Once the holder claims the account it is theirs, and getting the management back needs their consent, not just their email address. - **Entitlement:** requires `manage_accounts`. Endpoint: DELETE /v1/accounts/{account_id}/management
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