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307,088 tools. Last updated 2026-07-27 21:33

"Resources and Tips for Writing Better R Code" matching MCP tools:

  • Compile TypeScript source (defineIntent() call) into native Swift App Intent code. Returns { swift, infoPlist?, entitlements? } as a string — no files written, no network requests. On validation failure, returns diagnostics (severity, AX error code, position, fix suggestion) instead of Swift. Use: use when TypeScript DSL source should become Swift; use validate for cheaper preflight only. Inputs: source is TypeScript DSL text; options add sandbox, format, plist, or entitlement proof without writing files. Effects: read-only generated Swift/diagnostics; writes no files and uses no network.
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  • Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.
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  • Amend an OPEN journal trade by trade_id: move your stop or target, fix size_usd / leverage / thesis. A stop MOVE changes only the current stop (what the watchdog and close-time touch scan use); realized R stays measured against your INITIAL stop, so trailing to breakeven can't inflate R. To fix a genuine fat-finger in the original entry or stop, also pass correct_entry=true — that resets the R basis (disclosed in the response). asset/side can't be amended — void and re-log for that. Pro.
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  • Get the realised track record: win-rate + R-multiple distribution. Aggregates actual closed outcomes (tp_hit, trail_stop_hit, stop_hit, position_closed) into an honest scorecard — win_rate, wins/losses, and the realised-R distribution (mean/median/min/max/total). Pass a ticker for its per-ticker record; omit it (or pass "") for the portfolio-wide record. HONESTY: small samples are flagged `illustrative: true` with an explanatory `note`, and `win_rate` is null when no closed P&L exists. Never present an illustrative record as a reliable hit-rate. Every number is grounded in stored outcome events — none are fabricated. This is the ground-truth check on signal quality: a high stated R:R only holds if the realised-R mean is positive and the stop-hit rate is low. Args: ticker: Stock ticker (e.g. "AAPL"), or "" for portfolio-wide. Returns a dict with scope, sample_size, win_rate, realised_r, and note.
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  • KILLER ANALYSIS: given a target KPI + multiple candidate indicators, rank which candidates best predict the target by correlation strength. Perfect for "what moves my KPI?" questions. Returns ranked list with r, p-value, R² for each candidate. Maximum 30 candidates per call.
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  • Return the kernelcad-authoring SKILL.md body — conventions for writing .kcad.ts scripts (imports, parameters, evaluation contract, common pitfalls). Use this tool BEFORE generating CAD code if your MCP client does not list resources. Clients that do list resources should instead read `kernelcad://skills/authoring` directly — the contents are identical. INPUT: none. OUTPUT: { uri, mimeType, text } where `text` is the SKILL.md body.
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Matching MCP Servers

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    Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.
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  • Corporate travel: search and book flights, hotels, rail and transfers, manage orders.

  • Cloudflare Workers MCP server: code-explainer

  • Full metadata for one dataset (CKAN package_show) including its resources/distributions with download URLs. Use a dataset `name` (slug) or id from search_datasets. There is no datastore, so fetch `resources[].download_url`/`url` for the underlying data.
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  • Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.
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  • Given the file paths an agent is about to change (and optionally a subset being deleted), return the conventions, documentation gaps, and existing/related docs whose evidence overlaps those paths, plus a net-new/undocumented analysis and any removal candidates. Read-only; no side effects. Returns a Markdown report. Call this BEFORE writing code so doc updates land in the same PR; then use propose_doc_update to write a doc, or propose_doc_removal for an orphaned one.
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  • Use to access the Hugging Face Hub. Navigate resources with ls, cat, find, stat, and search over hf:// URIs. Roots: hf://models, hf://datasets, hf://spaces, hf://buckets, hf://collections, hf://papers, hf://docs. For papers, ls hf://papers/ARXIV_ID to discover related resources; cat hf://papers/ARXIV_ID/paper.md or metadata.json. Documentation paths include the current version from each product's llms.txt manifest. Grammar; each token below is one args array element: ls URI [(-R|-r|-lR|-laR|--recursive)] [(-l|-a|-la|-al|--long)] [--glob GLOB] [(-type|--type|--entry-type) TYPE] [--sort SORT] [(-limit|--limit) N] cat URI [RELATIVE_PATH] [(-offset|--offset) N] [(-max-bytes|--max-bytes) N] stat URI [RELATIVE_PATH] find URI [(-R|-r|--recursive)] [(-name|--name|--glob) GLOB] [(-path|--path) GLOB] [(-type|--type|--entry-type) TYPE] [(-limit|--limit) N] search URI [QUERY...] [(-type|--type|--entry-type) TYPE] [--sort SORT] [--tag TAG] [--kind mcp] [(-limit|--limit) N] TYPE = file|dir|repo|bucket|collection|paper|link. Type aliases: f=file, d=dir, l=link, model|dataset|space=repo. SORT = createdAt|downloads|likes|lastModified|likes30d|trendingScore|mainSize|id|trending|upvotes. URI starts with hf://. QUERY and GLOB are each one string token. Search URI: hf://models|datasets|spaces[/OWNER], hf://collections[/OWNER], any hf://docs scope, or exactly hf://papers; not hf://. Repository and collection searches may omit QUERY to browse or filter; documentation and paper searches require it. Search joins multiple positional QUERY tokens with spaces. Cat and stat join one RELATIVE_PATH token to URI. Long-list flags are accepted for compatibility; hf_fs listings are already structured, so they do not alter output. Find is already recursive, so recursive flags are accepted without altering behavior. Space search: hf://spaces uses semantic search; repeat --tag to require tags, or use --kind mcp for --tag mcp-server. hf://spaces/OWNER uses owner-scoped keyword search. Documentation: ls hf://docs for products; search any docs scope; use returned hf:// URIs verbatim. Trending listings: ls hf://models/trending, hf://datasets/trending, or hf://spaces/trending. They return up to 20 entries. Trending paths imply trending order; --sort trending|trendingScore is redundant but valid. Trending papers: ls hf://papers/trending. TYPE filters mixed results; omit it when the URI already fixes the result type. Limits and path-specific behavior are documented at hf://README.md. Omit --limit and --sort unless the request asks for a cap, ordering, or exhaustive results. No pipes, redirects, shell expansion, or multiple commands.
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  • MANDATORY FIRST CALL before writing any @marmoui/ui code in this session. Returns a step-by-step generation checklist (which tools to call, in what order), critical rules (no namespace sub-components, PageSection is self-closing, no Sidebar export), component patterns, and ICON LIBRARY RULES. Pass iconLibrary (default "phosphor"; also "material" | "lucide" | "tabler" | "heroicons" | "feather") to get that library's import source, icon name map, and weight/style mapping — and pass the SAME value to review_generated_code so it enforces it. Ask the user which icon library they want before writing UI code. Call topic="patterns" to get the generation checklist specifically.
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  • Returns a 0-100 frontier-hardware research-momentum score (Semantic Scholar publication counts for quantum computing, solid-state batteries, and neuromorphic computing — 90-day windows vs. baseline, summed) with trend, z_score, per_variant_w0, and window_counts. Call when the user asks about emerging-tech R&D acceleration, quantum/battery/neuromorphic research trends, or pre-patent signals, or when timing deep-tech investment, corporate R&D strategy, or technology-scouting decisions. Updates: daily.
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  • Calculates timing parameters for the ubiquitous NE555 / LM555 timer IC in astable (free-running oscillator) and monostable (one-shot pulse) modes. In astable mode, computes frequency, period, duty cycle, and HIGH/LOW durations from R1, R2, and C using f = 1.44 / ((R1 + 2·R2) · C). In monostable mode, computes pulse width from R and C using t = 1.1 · R · C. Standard 555 astable duty cycle is always >50%; for 50% duty cycle use a diode across R2. Useful for generating clock signals, PWM, delays, and debounce circuits. Chain from ohms_law for power calculations or resistor_color_code for component selection.
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  • Fetch simplified GeoJSON for a boundary by its ONS census code. Safe to embed directly in generated HTML map files. At the default tolerance (0.0001°) a constituency polygon shrinks from ~4,000 vertices to ~200–400 with no visible difference at normal map zoom levels. Prefer this over get_boundary_geojson_by_code() when writing Leaflet map pages — the full geometry is large enough to exhaust your context window before you can finish writing the HTML.
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  • Load Lenny Zeltser's IR report writing context for local analysis. Returns expert guidelines for field completeness, incident identification, notification triggers, and writing quality. Includes rating-sheet items (lens taxonomy plus the IR-specific Information sheet) as concrete reference points for grounded feedback. This server never requests your incident notes and instructs your AI to keep them local. Use detail_level to control response size: "minimal" (~2k tokens), "standard" (~5k tokens), or "comprehensive" (~11k tokens).
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  • Calculate compound interest for investments. Formula: A = P(1 + r/n)^(nt) Where: - P = principal amount - r = annual interest rate (as decimal) - n = number of times interest compounds per year - t = time in years Examples: compound_interest(10000, 0.05, 5) # $10,000 at 5% for 5 years → $12,762.82 compound_interest(5000, 0.03, 10, 12) # $5,000 at 3% compounded monthly → $6,744.25
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Search the ChangeGamer corpus by keyword. Ranks resources by relevance across title, description, tags, category, and body, and returns metadata plus HTML/Markdown/JSON URLs (no body content). Use this to find resources before fetching them with get_resource.
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  • Search Netherlands Open Data (Netherlands) for datasets by keyword. Returns each dataset's id/name, title, organization, and its resources (each with a resource_id for query_resource).
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