Skip to main content
Glama
194,718 tools. Last updated 2026-06-12 00:59

".NET" matching MCP tools:

  • Get comprehensive token screening data across multiple blockchain networks with advanced filtering. A maximum of 25 results are returned out of 1000s of tokens. Use the sorting and filtering options to narrow down the results. A maximum of 5 chains can be specified per request (excess chains are automatically trimmed). This tool helps with token discovery and finding trending tokens by combining different metrics: volume, liquidity, market cap, smart money activity, and token age. **IMPORTANT - Hyperliquid Special Case:** - Hyperliquid chain queries perpetual futures (perps), not spot tokens - When hyperliquid is mixed with other chains, two sections of up to 25 results each are returned - one for spot tokens and one for perps. - For perps, only these filters are supported: volume, buyVolume, sellVolume, openInterest, netflow, nofTraders, traderType - Additional orderBy fields for perps: openInterest, funding - Unsupported filters/orderBy will fallback to defaults INPUT EXAMPLES: # Find tokens which are going up in price. # Added some liquidity filter to remove spam and low quality tokens. ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "liquidity": {"from": 100000}, "nofTraders": {"from": 10}, "orderBy": "price_change", "orderByDirection": "desc" } ``` # Find top stablecoins by market cap ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "7d", "sectors": ["Stablecoin"], "orderBy": "market_cap_usd", "orderByDirection": "desc" } ``` # Find AI memecoins with high trading activity { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "7d", "sectors": ["AI Meme"], "liquidity": {"from": 100000}, "volume": {"from": 1000000} } # Find DeFi lending tokens { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "sectors": ["DeFi Lending (Money Markets)"], "netflow": {"from": 1000000} } # Find tokens which have a lot of buying activity (high nofBuyers and buyVolume) # Note that we added some filters to remove spam and low quality tokens. We added liquidity filter so that we only surface tokens which we can buy or sell. # We sort by `netflow` descending to get tokens with the most net buying activity. ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "liquidity": {"from": 100000}, "buyVolume": {"from": 1000000}, "marketCapUsd": {"from": 1000000}, "nofBuyers": {"from": 10}, "orderBy": "netflow", "orderByDirection": "desc" } ``` # Find Hyperliquid perps with high open interest and positive net flow ``` { "chains": ["hyperliquid"], "timeframe": "7d", "openInterest": {"from": 100000}, "volume": {"from": 1000000}, "netflow": {"from": 0}, "nofTraders": {"from": 10}, "orderBy": "netflow", "orderByDirection": "desc" } ``` WARNING: To avoid timeouts, it's recommended to: - Use 4 chains or less at a time (API tends to timeout with more chains) - Use shorter timeframes (e.g., 24h or 1h instead of 7d or 30d) Args: Returns: Comprehensive token metrics as markdown. Returns empty string if no tokens found. Columns returned: - **Token Address**: Token address (e.g., 0x1234567890123456789012345678901234567890) - **Symbol**: Token trading symbol (e.g., ETH, BTC, DOGE) - **Chain**: Blockchain network (ethereum, solana, polygon, etc.) - **Price USD**: Current token price in USD (currency formatted) - **Price Change**: Price change percentage over the date range (percentage, can be negative) - **Market Cap**: Current market capitalization (currency formatted) - **Fully Diluted Valuation (FDV)**: Market cap if all tokens were circulating (currency formatted) - **FDV/MC Ratio**: Ratio indicating how much supply is locked/vested (numeric, >1 means locked supply) - **USD Volume**: Total trading volume in USD (currency formatted) - **Buy USD Volume**: Total buy volume in USD (currency formatted) - **Sell USD Volume**: Total sell volume in USD (currency formatted) - **Net Flow USD**: Net flow (buys minus sells) in USD (currency formatted, can be negative) - **DEX Liquidity**: Available liquidity for trading (currency formatted) - **Inflow/FDV**: Inflow as percentage of FDV (percentage formatted) - **Outflow/FDV**: Outflow as percentage of FDV (percentage formatted) - **Token Age (Days)**: Days since token was first deployed - **Sectors**: List of token sectors/categories Hyperliquid perps columns (smart-money mode, when `onlySmartTradersAndFunds=true`): - **Net Position** (`LONG $X` / `SHORT $X` / `FLAT`): current net direction. Use this when answering long/short questions. - **Current Longs USD** / **Current Shorts USD**: gross notional on each side; sizing only, not direction. - **Net Position Change**: delta over the timeframe — can be positive while Net Position is still SHORT. Notes: - Positive Net Flow on spot tokens indicates more buying than selling - High FDV/MC Ratio suggests significant locked or vested tokens **Filtering Options** (filters parameter): - **Numeric Ranges**: volume, liquidity, marketCapUsd, netflow, tokenAgeDays, nofTraders, nofBuyers, nofSellers, nofBuys, nofSells, buyVolume, sellVolume, fdv, fdvMcRatio, inflowFdvRatio, outflowFdvRatio - **Categories**: sectors (e.g. ["AI", "Meme"]), includeSmartMoneyLabels - **Trader Type**: traderType (string: "all", "sm", "whale", "public_figure") - Use "sm" ONLY when user explicitly asks for "smart money". - Use "whale" ONLY when user specifically asks for whales or large holders. - Use "public_figure" ONLY when user asks for KOLs or popular figures. - Data with "sm", "whale", and "public_figure" is sparse — "whale" and "public_figure" are even sparser than "sm". Pairing any of these with other filters (volume, liquidity, netflow) is likely to return no results. - Only pair traderType="sm/whale/public_figure" with other filters (volume, liquidity, netflow) if the user request explicitly requires it. - Instead of pairing this with other filters, you can rely on orderBy to sort by netflow, volume, liquidity, etc. **CRITICAL WARNING:** 'priceChange' is NOT a valid filter. You cannot filter for "tokens up > 10%". Use `orderBy="priceChange"` instead. **Sorting Options** (orderBy field): Available fields (use with orderByDirection: "asc" or "desc"): - **priceUsd**: Sort by token price - **priceChange**: Sort by price change percentage - **marketCapUsd**: Sort by market capitalization - **volume**: Sort by total trading volume - **buyVolume**: Sort by buy volume - **sellVolume**: Sort by sell volume - **netflow**: Sort by net flow (buys - sells) - **liquidity**: Sort by DEX liquidity - **nofTraders**: Sort by number of traders (Note: Fields like `tokenAgeDays` or `outflowFdvRatio` are for FILTERING only, not sorting) Default: orderBy="netflow", orderByDirection="desc"
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • Get comprehensive token screening data across multiple blockchain networks with advanced filtering. A maximum of 25 results are returned out of 1000s of tokens. Use the sorting and filtering options to narrow down the results. A maximum of 5 chains can be specified per request (excess chains are automatically trimmed). This tool helps with token discovery and finding trending tokens by combining different metrics: volume, liquidity, market cap, smart money activity, and token age. **IMPORTANT - Hyperliquid Special Case:** - Hyperliquid chain queries perpetual futures (perps), not spot tokens - When hyperliquid is mixed with other chains, two sections of up to 25 results each are returned - one for spot tokens and one for perps. - For perps, only these filters are supported: volume, buyVolume, sellVolume, openInterest, netflow, nofTraders, traderType - Additional orderBy fields for perps: openInterest, funding - Unsupported filters/orderBy will fallback to defaults INPUT EXAMPLES: # Find tokens which are going up in price. # Added some liquidity filter to remove spam and low quality tokens. ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "liquidity": {"from": 100000}, "nofTraders": {"from": 10}, "orderBy": "price_change", "orderByDirection": "desc" } ``` # Find top stablecoins by market cap ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "7d", "sectors": ["Stablecoin"], "orderBy": "market_cap_usd", "orderByDirection": "desc" } ``` # Find AI memecoins with high trading activity { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "7d", "sectors": ["AI Meme"], "liquidity": {"from": 100000}, "volume": {"from": 1000000} } # Find DeFi lending tokens { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "sectors": ["DeFi Lending (Money Markets)"], "netflow": {"from": 1000000} } # Find tokens which have a lot of buying activity (high nofBuyers and buyVolume) # Note that we added some filters to remove spam and low quality tokens. We added liquidity filter so that we only surface tokens which we can buy or sell. # We sort by `netflow` descending to get tokens with the most net buying activity. ``` { "chains": ["ethereum", "solana", "bnb", "base"], "timeframe": "24h", "liquidity": {"from": 100000}, "buyVolume": {"from": 1000000}, "marketCapUsd": {"from": 1000000}, "nofBuyers": {"from": 10}, "orderBy": "netflow", "orderByDirection": "desc" } ``` # Find Hyperliquid perps with high open interest and positive net flow ``` { "chains": ["hyperliquid"], "timeframe": "7d", "openInterest": {"from": 100000}, "volume": {"from": 1000000}, "netflow": {"from": 0}, "nofTraders": {"from": 10}, "orderBy": "netflow", "orderByDirection": "desc" } ``` WARNING: To avoid timeouts, it's recommended to: - Use 4 chains or less at a time (API tends to timeout with more chains) - Use shorter timeframes (e.g., 24h or 1h instead of 7d or 30d) Args: Returns: Comprehensive token metrics as markdown. Returns empty string if no tokens found. Columns returned: - **Token Address**: Token address (e.g., 0x1234567890123456789012345678901234567890) - **Symbol**: Token trading symbol (e.g., ETH, BTC, DOGE) - **Chain**: Blockchain network (ethereum, solana, polygon, etc.) - **Price USD**: Current token price in USD (currency formatted) - **Price Change**: Price change percentage over the date range (percentage, can be negative) - **Market Cap**: Current market capitalization (currency formatted) - **Fully Diluted Valuation (FDV)**: Market cap if all tokens were circulating (currency formatted) - **FDV/MC Ratio**: Ratio indicating how much supply is locked/vested (numeric, >1 means locked supply) - **USD Volume**: Total trading volume in USD (currency formatted) - **Buy USD Volume**: Total buy volume in USD (currency formatted) - **Sell USD Volume**: Total sell volume in USD (currency formatted) - **Net Flow USD**: Net flow (buys minus sells) in USD (currency formatted, can be negative) - **DEX Liquidity**: Available liquidity for trading (currency formatted) - **Inflow/FDV**: Inflow as percentage of FDV (percentage formatted) - **Outflow/FDV**: Outflow as percentage of FDV (percentage formatted) - **Token Age (Days)**: Days since token was first deployed - **Sectors**: List of token sectors/categories Hyperliquid perps columns (smart-money mode, when `onlySmartTradersAndFunds=true`): - **Net Position** (`LONG $X` / `SHORT $X` / `FLAT`): current net direction. Use this when answering long/short questions. - **Current Longs USD** / **Current Shorts USD**: gross notional on each side; sizing only, not direction. - **Net Position Change**: delta over the timeframe — can be positive while Net Position is still SHORT. Notes: - Positive Net Flow on spot tokens indicates more buying than selling - High FDV/MC Ratio suggests significant locked or vested tokens **Filtering Options** (filters parameter): - **Numeric Ranges**: volume, liquidity, marketCapUsd, netflow, tokenAgeDays, nofTraders, nofBuyers, nofSellers, nofBuys, nofSells, buyVolume, sellVolume, fdv, fdvMcRatio, inflowFdvRatio, outflowFdvRatio - **Categories**: sectors (e.g. ["AI", "Meme"]), includeSmartMoneyLabels - **Trader Type**: traderType (string: "all", "sm", "whale", "public_figure") - Use "sm" ONLY when user explicitly asks for "smart money". - Use "whale" ONLY when user specifically asks for whales or large holders. - Use "public_figure" ONLY when user asks for KOLs or popular figures. - Data with "sm", "whale", and "public_figure" is sparse — "whale" and "public_figure" are even sparser than "sm". Pairing any of these with other filters (volume, liquidity, netflow) is likely to return no results. - Only pair traderType="sm/whale/public_figure" with other filters (volume, liquidity, netflow) if the user request explicitly requires it. - Instead of pairing this with other filters, you can rely on orderBy to sort by netflow, volume, liquidity, etc. **CRITICAL WARNING:** 'priceChange' is NOT a valid filter. You cannot filter for "tokens up > 10%". Use `orderBy="priceChange"` instead. **Sorting Options** (orderBy field): Available fields (use with orderByDirection: "asc" or "desc"): - **priceUsd**: Sort by token price - **priceChange**: Sort by price change percentage - **marketCapUsd**: Sort by market capitalization - **volume**: Sort by total trading volume - **buyVolume**: Sort by buy volume - **sellVolume**: Sort by sell volume - **netflow**: Sort by net flow (buys - sells) - **liquidity**: Sort by DEX liquidity - **nofTraders**: Sort by number of traders (Note: Fields like `tokenAgeDays` or `outflowFdvRatio` are for FILTERING only, not sorting) Default: orderBy="netflow", orderByDirection="desc"
    Connector
  • Search the 96-indicator registry by keyword. Returns ranked matches (up to `limit`, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling `get_indicator`.
    Connector
  • Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.5 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy.
    Connector
  • One SEC Form 4 filing by accession number: the reporting insider, issuer, and every transaction in it (non-derivative + derivative), with net open-market flow and a link to the original SEC document. The full-filing follow-up when insider.transactions or events.latest hands you an accession_number.
    Connector

Matching MCP Servers

  • A
    license
    -
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that provides detailed type information from .NET projects for AI coding agents. The .NET Types Explorer MCP Server is a powerful tool designed to help AI coding agents understand and work with .NET codebases. It provides a structured way to explore assemblies,
    Last updated
    26
    Apache 2.0
  • A
    license
    A
    quality
    C
    maintenance
    MCP server for .NET assembly analysis. Vendor-neutral wrapper around AsmResolver and ILSpy for parsing and decompiling .NET assemblies.
    Last updated
    11
    MIT

Matching MCP Connectors

  • Source-bounded product, offer, and price-history intelligence for developers and AI agents.

  • Drop-in daily content for AI briefing agents. 10 channels, 100 free calls on signup.

  • Concise profile of one city: currency, tax shape (bracket count + top rate + payroll/national insurance), headline costs (rent / groceries / transit / childcare), safety-net values (parental leave, vacation, universal healthcare), and data freshness. Lighter than compare_cities; use when the user is asking about one place rather than a comparison. Read-only, no side effects; returns a text summary plus structured JSON.
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • Groq-powered vault compression: 50 cold (least-read) memories → 5 dense summaries. Source memories are archived after compression. Net result: sharper vault, lower LLM token cost when injecting context. Automatically refunded if Groq fails. $0.05. Requires API key.
    Connector
  • <tool_description> Settle pending payments for media buys. Supports manual CSV export, Stripe invoice (Phase 2 stub), and x402 micropayments (Phase 2 stub). </tool_description> <when_to_use> When a publisher wants to collect earned revenue or an advertiser needs to settle outstanding charges. Use method='manual' for CSV export. Stripe and x402 are stubs (Phase 2). </when_to_use> <combination_hints> get_campaign_report → settle (after verifying amounts). Filter by media_buy_id, publisher_id, or period. </combination_hints> <output_format> Settlement totals (gross, platform fee, net), entry count, and method-specific data (CSV for manual). </output_format>
    Connector
  • List every audience-specific privacy guide Default Privacy publishes — currently 22 (doctors, accountants, realtors, content creators, high-net-worth individuals, OnlyFans creators, etc.). Each entry returns a slug, audience label, one-line headline, intent ("business" | "asset" | "emergency"), and the recommended LLC structure shape ("single" | "bundle") + state. Call `get_audience` next for the full FAQ + risks + structure rationale on a chosen slug. When to call: when the user describes their profession or situation ("I'm a doctor", "real estate agent", "OnlyFans creator", "I have a lot of assets") and you want to find a matching audience-specific guide. Also call when the user asks "what kinds of clients do you serve" or "who uses this". PREFER `get_audience` directly when the user has already named a specific audience slug. Input Requirements: none. Output: `{ audiences: [{ slug, audience, headline, intent, structureType, state }], total, citation }`. The list is sorted by slug. `structureType` is "single" for one-LLC recommendations and "bundle" for multi-entity stacks (typically high-net-worth or heavy asset-protection scenarios). PREFER quoting the matching audience's `headline` to the user and then chaining `get_audience(slug)` to retrieve the full guidance before recommending a structure.
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector
  • Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.
    Connector
  • Check whether a UK mortgage applicant qualifies as a high net worth mortgage customer under FCA MCOB 3A. The test passes if annual net income is at least GBP 300,000 OR net assets are at least GBP 3,000,000. The net assets test INCLUDES primary residence equity (per the literal FCA glossary G2953 and UK lender practice) and INCLUDES pension by default. Supports single applicant or joint application. Returns verdict, per-applicant test breakdown, joint household aggregate (if joint), and a routing recommendation including the relevant UK private bank list. Calculated by Fox Davidson, FCA-authorised UK mortgage brokers (FRN 600427). Use when a user asks whether they qualify for a high net worth mortgage, about MCOB 3A, the GBP 300k income or GBP 3m net assets test, private bank mortgages, or large loans against assets.
    Connector
  • Get Australian PBS (Pharmaceutical Benefits Scheme) pricing for a medication. Returns the government-subsidised benefit price AND what the patient actually pays (general and concessional rates). Works for generic names and brand names. Args: drug_name: Generic or brand name of the medication (e.g. "metformin", "Lipitor", "paracetamol"). Returns: JSON with PBS benefit price, patient copayment cost, all available forms/brands, originator vs generic breakdown, and current safety net thresholds. Example: get_pbs_drug_price("metformin") Example: get_pbs_drug_price("Lipitor")
    Connector
  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
    Connector