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305,560 tools. Last updated 2026-07-23 07:10

"Understanding Boss Mode" matching MCP tools:

  • Update LLM instructions at the specified level. Required: level ('brain'|'personal_root'|'container'|'team'), instructions (string). Optional: id (integer, required for 'container' and 'team'), mode ('replace' default|'append'). The 'container' level updates personal containers only; to set instructions for a team, use level 'team' (team owners only). In 'replace' mode (default), the provided text overwrites existing instructions. In 'append' mode, the text is appended to existing instructions with a newline separator. Always read current instructions first before replacing to avoid losing existing content.
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  • Use this when you need to encode or decode text and want multi-byte and entity edge cases handled correctly rather than doing it by hand. Deterministic: same input, same output. The mode selects the operation: url-encode/url-decode (percent-encoding), html-encode/html-decode (entity table plus numeric character references), base64-encode/base64-decode (UTF-8 safe; decode tolerates URL-safe alphabet, whitespace, and missing padding), and unicode-encode/unicode-decode (\uXXXX and \u{...} escapes for non-ASCII). Every mode returns the same shape: {mode, output}. Example: mode base64-encode, text "héllo" -> output "aMOpbGxv".
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  • Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)
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  • Summarize document text into a prose summary and key points with citations. Use after extract_text or extract_url when you need a condensed understanding of a long document. For single-sentence Q&A, use qa_url instead. For extracting specific fields, use extract_structured. Typical workflow: extract_text/extract_url → summarize_document. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."
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  • Summarize document text into a prose summary and key points with citations. Use after extract_text or extract_url when you need a condensed understanding of a long document. For single-sentence Q&A, use qa_url instead. For extracting specific fields, use extract_structured. Typical workflow: extract_text/extract_url → summarize_document. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."
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  • Get upto 25 (per page) top holders information for a specific token. **Note:** Using `labelType: smart_money` is not a good proxy for an overall market view. Use it only if user explicitly requests it, or to combine it with other non smart money data. **Modes:** - `onchain_tokens` (default): Analyze on-chain tokens by contract address - `perps`: Analyze Hyperliquid perpetual futures by symbol (chain auto-set to "hyperliquid") Columns returned (onchain_tokens mode): - **Address**: Wallet/contract address of the token holder - **Label**: Nansen label (e.g., exchange, whale, etc.) - **Balance**: Current balance held (numeric with K/M/B formatting) - **Balance USD**: USD value of token holdings (currency formatted) - **Ownership %**: Percentage of total token supply owned (percentage, 2 decimal places) - **Sent**: Total tokens sent from this address historically (numeric) - **Received**: Total tokens received by this address historically (numeric) - **24h Change**: Balance change in last 24 hours (numeric, can be negative) - **7d Change**: Balance change in last 7 days (numeric, can be negative) - **30d Change**: Balance change in last 30 days (numeric, can be negative) Columns returned (perps mode): - **Trader Address**: Address of the trader - **Trader Label**: Nansen label for the trader - **Side**: Position direction (Long/Short) - **Position Value USD**: Total USD value of the position (currency formatted) - **Position Size**: Size of the position in tokens (numeric) - **Leverage**: Leverage multiplier (e.g., "20X") - **Leverage Type**: Type of leverage (cross/isolated) - **Entry Price**: Average entry price (price formatted) - **Mark Price**: Current mark price (price formatted) - **Liquidation Price**: Liquidation price (price formatted) - **Funding USD**: Cumulative funding payments (currency formatted) - **Unrealized PnL USD**: Unrealized profit/loss (currency formatted) Sorting Options (default: holding_size desc): onchain_tokens mode: holding_size, total_outflow, total_inflow, balance_change_24h, balance_change_7d, balance_change_30d perps mode: holding_size, side, entry_price, leverage, liquidation_price, funding_usd, upnl_usd Examples: # On-chain tokens (default mode) ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "top_100_holders" } ``` # Hyperliquid perpetual futures ``` { "mode": "perps", "token_address": "PENGU", "label_type": "smart_money" } ``` # Find most active senders using filters ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "smart_money", "includeSmartMoneyLabels": ["All Time Smart Trader", "Fund"], "orderBy": "total_outflow", "order_by_direction": "desc" } ``` # Find biggest accumulators (who received most tokens) ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "whale", "orderBy": "total_inflow", "order_by_direction": "desc" } ``` # Perps mode with filters ``` { "mode": "perps", "token_address": "ETH", "label_type": "smart_money", "side": "Long", "upnlUsd": {"from": 10000}, "positionValueUsd": {"from": 100000}, "orderBy": "holding_size", "order_by_direction": "desc" } ``` **Restrictions exclusively when querying for native tokens (ETH, BNB, etc.):** - Only supports sorting by `orderBy='holding_size'` (others will fail) - With `label_type='top_100_holders'`: limited filters (holding_size, total_outflow, total_inflow, address, smart money labels) - For advanced filters, use different`label_type` or set `aggregate_by_entity=true` **orderBy Restrictions (use 'holding_size' to avoid API errors):** - Token address: 0xa0b86a33e6b6c4b3add000b44b3a1234567890ab **Does not** work for SOL in onchain_tokens mode (tokenAddress So11111111111111111111111111111111111111112). For SOL analysis, use perps mode instead.
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  • Get token information — spot on-chain details or Hyperliquid perpetual futures stats. On-chain tokens mode (default): Returns token details (name, symbol, market cap, FDV, supply, deployment date, socials) and spot trading metrics (volume, buys/sells, buyers/sellers, holders, liquidity). Perps mode: Returns Hyperliquid perp stats — mark price, funding, open interest, buy/sell pressure, trader participation. Returns: Token information as markdown. On-chain tokens fields: - **Market Cap / FDV**: Market capitalization and fully diluted valuation - **Circulating / Total Supply**: Token supply metrics - **Deployed**: When the token was deployed - **Volume (Total / Buy / Sell)**: Trading volume in USD - **Buys / Sells**: Number of buy/sell transactions - **Unique Buyers / Sellers**: Distinct trading addresses - **Total Holders**: Number of token holders - **Liquidity**: Available liquidity in USD Perps fields: - **Mark Price**: Current perp mark price - **Price Change**: Change vs previous price - **Max Leverage**: Maximum leverage offered for the perp on Hyperliquid (e.g. "40x") - **Funding Rate (hourly/annualized)**: Current funding rate - **Open Interest**: Total current open interest in USD - **Volume (Total / Buy / Sell)**: Perp volume in USD - **Net Flow (Buy - Sell)**: Buy/sell pressure in USD - **Traders**: Number of traders Example: On-chain tokens (default mode): ``` { "mode": "onchain_tokens", "chain": "ethereum", "tokenAddress": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "timeframe": "1d" } ``` Hyperliquid perps: ``` { "mode": "perps", "tokenAddress": "BTC", "timeframe": "7d" } ``` Notes: - On-chain tokens mode uses contract addresses - Perps mode uses token symbols (e.g. BTC, ETH, HYPE) - Both modes use the same `timeframe` parameter
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  • Calculate percentages three ways: what's X% of Y, what % is X of Y, and what's the % change from X to Y. Use mode='of' for the first form, mode='ratio' for the second, mode='change' for the third.
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  • Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.
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  • Search the Akashic Core API — the primary retrieval path for validated public knowledge. Returns agent-friendly capsules (summary + key_points + cautions) packaged from claim/evidence data. Use this FIRST for factual/conceptual questions. For your own working notes use search_notes. - mode='compact' → 1-sentence summary per capsule (smallest, best for small models) - mode='standard' → full capsule without metadata (default) - mode='full' → everything including metadata and timestamps - fields=['summary','key_points'] → custom projection overriding mode
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  • Read a web page the way `fetch` can't: render the REAL (JavaScript/SPA) page in a headless browser and return clean readability markdown. Free. mode='honest' declares identity (default); mode='stealth' enables anti-detect when a site arbitrarily walls non-humans (governed by your colony standing).
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  • FDIC BankFind Suite: US bank financial health, regulatory capital ratios, and historical bank failure data. search mode: find institutions by name with asset size and state. profile mode: quarterly Call Report data — ROA, ROE, risk-based capital ratio, net income, loans, deposits (input: bank name or FDIC certificate number). failures mode: recent/historical bank closures with fail date, assets, resolution type. Covers 10,000+ institutions and 4,100+ failures since 1934. No API key.
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  • Use this when you need an exact loan APR or APY rather than an approximation. Two modes. mode="loan" (default): solve the true APR of an installment loan from amount financed, monthly payment, term, and upfront fees — Reg-Z style, fees discounted against the amount received, solved by Newton-Raphson. mode="rate": convert a nominal annual rate to APY for a given compounding frequency. Deterministic: same input, same output. Example: mode="loan", loanAmount=20000, monthlyPayment=450, termMonths=60, fees=500 -> apr=13.6301, totalInterest=7000. Prefer this over estimating APR/APY by hand.
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  • Calculate IPv4 subnet details from CIDR notation. Parses a CIDR block (e.g. 192.168.1.0/24) and returns the network address, broadcast address, subnet mask, wildcard mask, first and last usable host addresses, total and usable host counts, prefix length, and classful IP class (A/B/C/D/E). Essential for homelab network planning, VLAN segmentation, firewall rule design, and understanding address space allocation. Handles special cases for /31 point-to-point links (RFC 3021) and /32 host routes.
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  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • Get upto 25 (per page) top holders information for a specific token. **Note:** Using `labelType: smart_money` is not a good proxy for an overall market view. Use it only if user explicitly requests it, or to combine it with other non smart money data. **Modes:** - `onchain_tokens` (default): Analyze on-chain tokens by contract address - `perps`: Analyze Hyperliquid perpetual futures by symbol (chain auto-set to "hyperliquid") Columns returned (onchain_tokens mode): - **Address**: Wallet/contract address of the token holder - **Label**: Nansen label (e.g., exchange, whale, etc.) - **Balance**: Current balance held (numeric with K/M/B formatting) - **Balance USD**: USD value of token holdings (currency formatted) - **Ownership %**: Percentage of total token supply owned (percentage, 2 decimal places) - **Sent**: Total tokens sent from this address historically (numeric) - **Received**: Total tokens received by this address historically (numeric) - **24h Change**: Balance change in last 24 hours (numeric, can be negative) - **7d Change**: Balance change in last 7 days (numeric, can be negative) - **30d Change**: Balance change in last 30 days (numeric, can be negative) Columns returned (perps mode): - **Trader Address**: Address of the trader - **Trader Label**: Nansen label for the trader - **Side**: Position direction (Long/Short) - **Position Value USD**: Total USD value of the position (currency formatted) - **Position Size**: Size of the position in tokens (numeric) - **Leverage**: Leverage multiplier (e.g., "20X") - **Leverage Type**: Type of leverage (cross/isolated) - **Entry Price**: Average entry price (price formatted) - **Mark Price**: Current mark price (price formatted) - **Liquidation Price**: Liquidation price (price formatted) - **Funding USD**: Cumulative funding payments (currency formatted) - **Unrealized PnL USD**: Unrealized profit/loss (currency formatted) Sorting Options (default: holding_size desc): onchain_tokens mode: holding_size, total_outflow, total_inflow, balance_change_24h, balance_change_7d, balance_change_30d perps mode: holding_size, side, entry_price, leverage, liquidation_price, funding_usd, upnl_usd Examples: # On-chain tokens (default mode) ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "top_100_holders" } ``` # Hyperliquid perpetual futures ``` { "mode": "perps", "token_address": "PENGU", "label_type": "smart_money" } ``` # Find most active senders using filters ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "smart_money", "includeSmartMoneyLabels": ["All Time Smart Trader", "Fund"], "orderBy": "total_outflow", "order_by_direction": "desc" } ``` # Find biggest accumulators (who received most tokens) ``` { "mode": "onchain_tokens", "chain": "ethereum", "token_address": "0xa0b86a33e6b6c4b3add000b44b3a1234567890ab", "label_type": "whale", "orderBy": "total_inflow", "order_by_direction": "desc" } ``` # Perps mode with filters ``` { "mode": "perps", "token_address": "ETH", "label_type": "smart_money", "side": "Long", "upnlUsd": {"from": 10000}, "positionValueUsd": {"from": 100000}, "orderBy": "holding_size", "order_by_direction": "desc" } ``` **Restrictions exclusively when querying for native tokens (ETH, BNB, etc.):** - Only supports sorting by `orderBy='holding_size'` (others will fail) - With `label_type='top_100_holders'`: limited filters (holding_size, total_outflow, total_inflow, address, smart money labels) - For advanced filters, use different`label_type` or set `aggregate_by_entity=true` **orderBy Restrictions (use 'holding_size' to avoid API errors):** - Token address: 0xa0b86a33e6b6c4b3add000b44b3a1234567890ab **Does not** work for SOL in onchain_tokens mode (tokenAddress So11111111111111111111111111111111111111112). For SOL analysis, use perps mode instead.
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  • List every error code in the Trillboards API error catalog. WHEN TO USE: - Understanding what error codes the API can return. - Building a client-side error handler that covers all cases. - Looking up error types, HTTP statuses, and documentation URLs. RETURNS: - object: "list" - data: Array of { code, type, http_status, description, doc_url } - total: Total number of error codes. Equivalent to GET /v1/errors but executed in-process (no HTTP round-trip). EXAMPLE: Agent: "What error codes can the API return?" list_error_codes()
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  • Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.
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  • Module visualization tool. Use when the user wants to understand how a module's modes work, how parameters change between modes, or what a specific mode does — a visualization communicates the per-mode behavior better than prose. The host renders the result inline in the chat as an interactive visualization (mode buttons, per-mode descriptions, schematic curves); you do not need to build an artifact yourself — just call this tool. Do not use for general module specs (HP, jacks, capabilities) — call get_module instead. After calling, your prose can reference what the user is seeing in the visualization (e.g. "in formant mode, all three outputs become bandpass filters") rather than describing the visualization itself. Currently supported viz families: - filter_response — filters with characterized response curves (e.g. Three Sisters, Ripples, Belgrad, A-124, Filter 8, QPAS, SVF 1U, Cinnamon, C4RBN, Ikarie) - oscillator_morph — multi-mode oscillators and excited resonators (e.g. Rings, Loquelic Iteritas, Plaits) A module is supported when every one of its modes has a behavior_model_id the renderer knows. If you're unsure whether a given module qualifies, just call this tool — the error names the gap. Errors: - "Module not found: <id>" if no module with that id exists. - "Module not yet supported by visualize_module: <id>" when one or more modes lack a renderer-known behavior_model_id, or when the module mixes incompatible viz families. Suggest get_module for the underlying spec. The returned spec is a JSON object with: module_id, module_name, manufacturer, viz_type, params[], modes[], response_model_id, presets[]. Each mode has a behavior_model_id that the renderer uses to pick the curve set (e.g. crossover_lp_bp_hp vs formant_three_bp for filter_response). `response_model_id` (top-level) vs per-mode `behavior_model_id`: for multi-mode modules the top-level field is intentionally null — each mode carries its own behavior_model_id since the modes use different curve sets (e.g. Three Sisters' crossover vs formant). Read the per-mode values from `modes[].behavior_model_id`. The top-level is populated only for single-curve modules where one model applies across the whole module. `null` at top-level + populated per-mode = "modes carry distinct models," not a bug.
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  • Assigns displays to categories. mode 'replace' (default) sets the full category list of each display in display_ids to category_ids; mode 'add' or 'remove' adds/removes one category (category_ids[0]) across many displays without touching other assignments. Discover IDs with list_display_categories; create categories with manage_display_category.
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