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458,064 tools. Updated 2026-08-14 22:15

"Understanding the Basics of Computer Use" matching MCP tools:

  • Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Honeydew Documentation documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.
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  • Query verified U.S. private construction spending ($ millions) for data centers and semiconductor/computer-electronics manufacturing plants, from the U.S. Census Bureau's Value of Construction Put in Place (C30). Use this for "how much is being spent BUILDING data centers (or chip fabs) in the US" questions — the construction buildout in dollars, not capacity or investment. Filter by `category` ("data_center" — Census's named subcategory under Office; or "computer_electronic_electrical" — the semiconductor/computer-electronics manufacturing line under Manufacturing), `basis` ("seasonally_adjusted" = a seasonally-adjusted ANNUAL RATE, or "not_seasonally_adjusted" = the NOT-adjusted MONTHLY LEVEL), `data_month` (one month, ISO first-of-month e.g. "2026-04-01") or the `data_month_from`/`data_month_to` range, `year`, and `revision_status` ("preliminary", "revised", or "final"). Group by any of `category`, `basis`, `data_month`, `year`, or `revision_status`. Pass each parameter as a top-level key of `params` (flat — not nested under a `filter`, `filters`, or `where` key). Example: `{"category": "data_center", "basis": "seasonally_adjusted", "data_month": "2026-04-01"}` for one month; add `"group_by": ["data_month"]` over a `data_month_from`/`data_month_to` range for a series. Returns JSON aggregates with citations and optional row-level records when `include_records` is true — every value cites the exact Census workbook, sheet, row, and column. The two categories are DISTINCT series and are never conflated: `data_center` is data-center buildings; `computer_electronic_electrical` is the chip/electronics-manufacturing (fab) line — the CHIPS-Act build-out. `basis` is the other fork: the seasonally-adjusted series is an ANNUAL RATE (what the current monthly pace annualizes to), while the not-seasonally-adjusted series is the actual MONTHLY LEVEL. `revision_status` carries Census's own preliminary/revised/final marking verbatim. Data is monthly; the data-center series begins 2014-01. The response `as_of` is the release vintage (Census revises monthly); pin `as_of` to an earlier vintage to reproduce what was served then. NOT additive: `construction_spending_musd` is a published per-(category, basis, month) reading, so a total that mixes the two bases (an annual rate + a monthly level), or that sums the seasonally-adjusted ANNUAL-RATE series across months, is not a real figure — such a result carries a `construction_aggregation` scope note and ranking remainders omit the metric. Filter to one `basis` and `group_by data_month` for a series over time. Does not determine total data-center INVESTMENT (servers, chips, cooling, equipment — this is construction put-in-place only; Census does not publish an investment total), data-center MW capacity, count, square footage, or location (use the power.* capabilities for capacity and the interconnection queue), which company or project is building (Census C30 has no operator breakdown), public or government construction (this is PRIVATE construction only), or construction outside these two categories.
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  • Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. 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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  • Institutional-grade BUY/SELL/HOLD directive for US equity symbols — the production-grade upgrade from demo_council (which is IWM-only, 5-min cached, free). Aggregates 8 proprietary engines — gamma-flow + flip detection, VPIN order-flow toxicity, fractal anchor confluence, regime classifier, dark-pool axis tracking, options sweep intelligence, mean-reversion regime, and Battle Computer consensus — into one tradeable verdict: directive, confidence 0-100, regime label (ALPHA_EXPANSION / MACRO_COLLAPSE / NEUTRAL / SHIELD), price targets (tp1/tp2/stop), and a per-engine breakdown explaining the score. Call this when you need a high-conviction directional read before sizing or executing a position — this is the same verdict institutional desks subscribe to at $1,000/mo via the Leviathan tier. Cost: 0.10 RLUSD per call (~$0.10). 60-second per-symbol cache, so back-to-back queries on the same ticker are effectively free. Pass payment_token from verify_payment plus your agent_wallet. Coverage: US equities; crypto coverage in roadmap. Typical response time: <2s cached, ~4s fresh compute.
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  • Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.
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  • Full Cook County / Chicago property dossier (parcel basics, recorded sales & deed history, building permits, property-tax assessment history, location context) for a PIN — the one-call due-diligence record. Permits are LINKED to the PIN here (the raw permit open-data has no PIN; links are derived by address + geo match), so this is a finished record you can't reproduce with raw open-data queries. Costs $0.03 per call (x402, USDC).
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Matching MCP Servers

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    license
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    quality
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    maintenance
    Exposes Anthropic's computer-use action surface (screenshot, click, move, keyboard, clipboard, batch) against a persistent desktop display via MCP stdio protocol. Enables AI agents to control a virtual desktop environment through natural language instructions.
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    MIT

Matching MCP Connectors

  • Run and manage H Company's Computer-Use Agents from any MCP client.

  • Honest library picks for coding agents in 25-360 tokens. Tells your agent what NOT to install.

  • Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. 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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  • Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the nTop documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.
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  • The Memory pages for this product: the shared record the growth marketing engine drafts from (business basics, audience, competitors, the voice samples, recent observations). Returns summaries with a size hint; fetch one page body with get_kb_page. Read-only, free.
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  • Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the Political Comms documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.
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  • Runs a Monte Carlo simulation over a portfolio balance and returns the distribution of possible end values: percentiles, mean, median, and if you give a goal amount, the share of simulated paths that reached it. Returns are drawn from a normal distribution using the expected return and volatility you supply. Use when the user asks about the range or probability of outcomes rather than a single projected number, for example the odds of reaching a target, or how much volatility widens the spread. Do not use it to value a company (use calculate_dcf_value or get_stock_valuation), and do not use it for retirement withdrawal, tax, or drawdown-sequencing questions, which it does not model. The simulation propagates the assumptions you give it. Normally distributed returns understate real market tail risk, and the output is a property of the inputs, not a prediction about any real portfolio.
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  • Call this when the user asks how much Bitcoin is vulnerable to a quantum computer, about quantum-exposed supply, P2PK coins, or Satoshi-era exposure. Returns the latest daily measurement from ByKaranteli's own Bitcoin Core node: exposed BTC and its share of held value and UTXO count, composition by script family, dormancy cohorts, the dormant-P2PK watch set, and provenance hashes (base_height, base_hash, txoutset_hash) so any figure can be re-verified against any node.
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  • USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"
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  • Get the basics for a match in ONE call: the score, whether it's live, when it kicks off, and who's favored. No betting knowledge needed — this answers "who's winning?", "what's the score?", "what time does Brazil play (in my timezone)?", "who's the favorite?". Returns the live score + match clock, the status, the kickoff time (in ``timezone`` if you pass an IANA name like "America/New_York"), the favored team with a plain win probability (de-vigged from the 1x2 line), and a ready-to-read ``summary`` you can quote directly. Args: query: natural-language fixture or team, e.g. "Brazil vs Argentina" or just "Brazil". timezone: optional IANA timezone (e.g. "America/New_York", "Asia/Shanghai") for the kickoff time; default UTC. sport: optional filter — "football" or "basketball". date: optional UTC date "YYYY-MM-DD" to disambiguate same-name fixtures. On an ambiguous query, ``status`` is "ambiguous" and ``ask_user`` carries a prompt — do not guess. ``favorite`` is best-effort (null when no 1x2 is on file for the fixture).
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  • Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.
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  • Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.
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  • Live CHP traffic incidents statewide, optionally filtered. Data: the California Highway Patrol statewide computer-aided dispatch feed - collisions, traffic hazards, disabled vehicles, closures as CHP logs them. Refreshes about once a minute; incidents disappear when CHP closes the log. Fetched live on every call. Filters (combinable): - highway: a route like "I-80", "US 50", "17", "Hwy 99". Matches incidents whose location text mentions that route. - center: "lat,lon" with radius_km - incidents within that circle. THIS IS THE RIGHT FILTER FOR A TOWN OR PLACE NAME: use your knowledge of where the place is (e.g. Coyote, CA -> "37.22,-121.74") with radius_km 15-30. A circle catches every road around the place, not just one highway. - area: substring match on the CHP dispatch-area name. These are CHP communication-center names ("Hollister Gilroy", "East Sac", "Golden Gate"), NOT town names - do not pass a town here. There is no county filter because CHP's feed carries no county field; for a county, use center on the county seat with a radius covering the county. Limits: locations are free-text from dispatchers; a few incidents lack usable coordinates and are omitted. No history - current logs only.
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  • Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. FOR-PROFIT APPLICANTS: pass for_profit_applicant=true to search capital a for-profit can take (PRIs, loans, revenue-based financing, patient equity) from CDFIs, impact investors, and PRI-active foundations. The default pool is 501(c)(3)-shaped and will NOT contain those programs. 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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  • Get comprehensive RDF data for any entity in the DanNet database. Supports both DanNet entities and external vocabulary entities loaded into the triplestore from various schemas and datasets. UNDERSTANDING THE DATA MODEL: The DanNet database contains entities from multiple sources: - DanNet entities (namespace="dn"): synsets, words, senses, and other resources - External entities (other namespaces): OntoLex vocabulary, Inter-Lingual Index, etc. All entities follow RDF patterns with namespace prefixes for properties and relationships. NAVIGATION TIPS: - DanNet synsets have rich semantic relationships (wn:hypernym, wn:hyponym, etc.) - External entities provide vocabulary definitions and cross-references - Use parse_resource_id() on URI references to get clean IDs - Check @type to understand what kind of entity you're working with Args: identifier: Entity identifier (e.g., "synset-3047", "word-11021628", "LexicalConcept", "i76470") namespace: Namespace for the entity (default: "dn" for DanNet entities) - "dn": DanNet entities via /dannet/data/ endpoint - Other values: External entities via /dannet/external/{namespace}/ endpoint - Common external namespaces: "ontolex", "ili", "wn", "lexinfo", etc. Returns: Dict containing JSON-LD format with: - @context → namespace mappings (if applicable) - @id → entity identifier - @type → entity type - All RDF properties with namespace prefixes (e.g., wn:hypernym, ontolex:evokes) - For DanNet synsets: dns:ontologicalType and dns:sentiment (if applicable) - Entity-specific convenience fields (synset_id, resource_id, etc.) Examples: # DanNet entities get_entity_info("synset-3047") # DanNet synset get_entity_info("word-11021628") # DanNet word get_entity_info("sense-21033604") # DanNet sense # External vocabulary entities get_entity_info("LexicalConcept", namespace="ontolex") # OntoLex class definition get_entity_info("i76470", namespace="ili") # Inter-Lingual Index entry get_entity_info("noun", namespace="lexinfo") # Lexinfo part-of-speech
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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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