"Learning Programming" matching MCP tools:

• sti_search

  Stimulsoft Documentation MCP Server

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

  Connector

• get_benchmark_history

  Silicon Analysts

  Historical Benchmarks — the bitemporal benchmark-observations ledger behind the Chip Cost Calculator: wafer cost by node/foundry (deflationary curves), defect-density (D0) learning curves per node, advanced-packaging costs incl. the broken-out CoWoS interposer entity, test cost, backend yield, and HBM $/GB. Each observation carries as_of (the date the reading reflects — curated backfill from dated public archives extends history), detected_at (capture time), and full sourcing metadata (source_type taxonomy: foundry_ir | wfe_vendor_earnings | government_filing | press_release | analyst_report | company_announcement | trade_press | public_web; source_url; confidence high/medium/low). grain=month|quarter returns median/min/max rollups per period; grain=raw returns per-source observations. Access tiers: free key → preview, Pro/Enterprise → full ledger, anonymous → none. USE THIS for: "how has TSMC N5 wafer pricing moved over 24 months?", "is our internal D0 ramp tracking the market's learning curve?", "CoWoS interposer cost trend", benchmarking product-lifecycle cost projections. DO NOT USE for: current point values (use get_wafer_pricing / get_packaging_costs); the daily PIT ledger replay (use /api/v1/snapshot-series); margin history (use /api/v1/margin-trends). Filters: benchmark_type (required: wafer_cost|defect_density|packaging_cost|interposer_cost|test_cost|backend_yield|hbm_cost_per_gb), entity_id, foundry, from/to (as_of bounds), grain (raw|month|quarter), limit. Access: a free API key returns a short preview (latest few observations); Pro/Enterprise unlock the full ledger; anonymous callers get none (empty + a get-a-key note). Cite as "Silicon Analysts — Historical Benchmarks".

  Connector

• list_courses

  Ripostiq

  List every available Ripostiq course (no login needed). Call this FIRST when a user wants to browse or start learning. Each course has a free Module 1 anyone can start immediately via begin_course(course) → teach_section. Returns the `course` id to pass to other tools. For a logged-in learner this also includes courses THEY authored (marked `mine`, with their `visibility`) — offer those alongside the catalog.

  Connector

• me.learning_path

  AI Design Blueprint

  Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.

  Connector

• search_open_grants

  foundation-discovery

  Search open grant opportunities from Kindora's active foundation-program corpus plus federal and state 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 government grant opportunities — federal (Grants.gov) plus state and district grant portals. 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)

  Connector

• read_datasheet

  sheetsdata-mcp

  Read from a component's datasheet. Two modes: **Section mode** (default): Returns a named section. Start with section='summary' to get an overview and a list of available_sections. Then request specific sections by name. Section names are dynamic — any heading in the actual datasheet works (e.g. 'register_map', 'i2c_interface', 'power_management'). If a section name isn't found, automatically falls back to search mode. **Search mode**: Semantic search within the part's datasheet. Best for targeted questions (register bit fields, I2C config, specific specs). Use when you need to find specific information rather than a whole section. First call for a new part triggers extraction (30s-2min). Subsequent calls are cached. **Datasheet vs Reference Manual**: Manufacturer datasheets cover high-level specs, pinout, absolute maximum ratings, and package info. For microcontrollers (STM32, nRF52, RP2040), register-level programming details (I2C CR1/CR2, DMA config, interrupt bits) are in a separate Reference Manual, not the datasheet. The summary's available_sections will show what's actually present. The part_number must be a specific manufacturer part number (e.g. 'TPS54302', 'STM32F446RCT6') or LCSC number (e.g. 'C2837938'). Do NOT pass bare component values ('100nF', '10K'), descriptions, or reference designators. DATASHEET STATUS VALUES: - 'ready' — extracted and indexed; call read_datasheet, search_datasheets, or analyze_image. - 'extracting' / 'in_progress' / 'queued' / 'pending' — extraction running or scheduled. Poll check_extraction_status every 5-10s until 'ready' or 'failed'. Typical time: 30s-2min. - 'not_extracted' — known part but datasheet hasn't been fetched yet. Trigger it via prefetch_datasheets (cheapest) or by calling read_datasheet (auto-triggers on first read). - 'no_source' — we couldn't find a public datasheet URL for this MPN. First, retry prefetch_datasheets in 10-30s (the URL resolver re-runs and often finds a source on the second pass). If still 'no_source', the agent can upload the PDF manually via request_datasheet_upload + confirm_datasheet_upload (see those tools). Org-uploaded datasheets are private to the org. - 'unsupported' — PDF exists but can't be extracted (scanned image-only, encrypted, or corrupted). Upload a clean text-based PDF via request_datasheet_upload to override. - 'failed' / 'error' — extraction errored. The response includes the error reason. Retry via prefetch_datasheets or escalate to support. - 'rejected' — input wasn't a real MPN (bare value like '100nF', description, or reference designator). Fix the input and re-call. - 'deduplicated' — another part in the family already has this datasheet; same content is returned under the primary MPN.

  Connector

• compare_analytical_vs_simulated

  QueueSim

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

  Connector

• getInstagramPostsByKeywords

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)

  Search Instagram posts by keywords with two response modes. Searches in both post captions and video subtitles. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: content analysis, hashtag trends, brand monitoring across thousands of posts. Returns by default: id, caption, username, createdAtDate. First searches database, then external API if data is stale or missing. NOT for URL lookups or post ID lookups - use getInstagramPostsByIds instead. Query must be plain text keywords/phrases, not URLs or IDs. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). Date filters: OMIT startDate/endDate parameters by default. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, caption, userId, username, fullName, createdAtDate), Engagement (likeCount, commentCount, reshareCount, videoPlayCount), Media (mediaType, imageUrl, videoUrl, subtitles, videoDuration). This is a safe, read-only tool for analyzing searchable information. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.

  Connector

• getRedditPostsByKeywords

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)

  Search Reddit posts by keywords with two response modes. Searches in post titles and selftext. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, subreddit trends, community discussions across thousands of posts. Returns by default: id, title, authorUsername, subredditName, createdAtDate. First searches database, then external API if data is stale or missing. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). Date filters: OMIT startDate/endDate parameters by default. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FILTERS: sort (relevance, hot, top, new, comments), time (hour, day, week, month, year, all), subreddit (limit to specific subreddit). FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, title, selftext, url, permalink), Author (authorId, authorUsername), Subreddit (subredditName, subredditId), Engagement (score, upvotes, downvotes, upvoteRatio, commentsCount, crosspostsCount), Flags (isSelf, isVideo, over18, spoiler, locked, stickied, archived). This is a safe, read-only tool for analyzing searchable information. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.

  Connector

• sti_search

  docs

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

  Connector

• me.learning_path

  AI Design Blueprint Doctrine

  Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.

  Connector

• get_capabilities

  Bullrun

  Discover what the connected Bullrun account can do BEFORE attempting an action, so you can plan instead of learning by hitting a 403. Reports whether you are authenticated and as WHICH identity (email + userId), whether the account has Bullrun Pro and why (subscription / trial / admin), the granted OAuth scopes, portfolio usage vs the free/max limits, and a per-tool entitlement map: create_portfolio_from_positions (free), create_portfolio_draft and create_position_draft (Pro-only), and whether another portfolio can be created now. Call this first when a draft/write tool might be gated, or to confirm which account a request will act on. Read-only.

  Connector

• find_xkcd

  xkcd Comic Suggester

  Find xkcd comics semantically relevant to the current conversation. Call this whenever an xkcd comic might enrich the conversation — when the discussion lands on a topic xkcd is famous for skewering (programming, science, statistics, relationships, the absurdity of standards, etc.). Pass a concise description of the current topic or theme as `context` (a phrase or sentence works better than a whole transcript), e.g. "spending hours automating a task that was faster to do by hand" or "code finally compiling". IMPORTANT — deciding whether to mention one. xkcd has a comic for almost every topic, so this tool will nearly always return something. A result being returned does NOT mean you should bring it up. Use the `score` as a signal and apply your own judgment about conversational fit: score >= 0.75 strong match — usually worth mentioning if it fits the moment 0.66 - 0.75 plausible — mention only if it genuinely lands < 0.66 weak/tangential — almost always better to stay silent Only one comic, at most, per topic — and only when it actually adds something. When you do share one, cite it by number and title with its `url`, and quote the `alt` (mouseover) text — it's half the joke. Returns a dict with a `results` list (num, title, score, url, image, alt, explain_url) and a `count`. An empty list means nothing cleared the floor.

  Connector

• search_jobs

  Magnificent Jobs

  Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings). YOU write the expanded query — it is embedded as-is, with no server-side rewriting — so always send `query` in this shape: "<Full job title>. <One sentence of what the role does; 3-5 key skills/tools>." NO ABBREVIATIONS anywhere in the query — spell everything out (ML → machine learning, AI → artificial intelligence, RN → registered nurse, SWE → software engineer, QA → quality assurance, PM → product manager, CDL → commercial driver's license, EMT → emergency medical technician, etc.) and keep the user's qualifiers (seniority, shift, domain). Example: user says 'ML eng jobs' → query 'Machine Learning Engineer. Builds, trains and deploys machine learning models; Python, PyTorch, MLOps, data pipelines.' Optionally add `city` (results within radius_miles of that city, ranked by relevance) and/or `state`. Without a city, ranks across the state or nationwide. Returns job cards with a `url` to show the user; call get_job for details.

  Connector

• lookup_native

  RedM Mcp

  Resolve a RedM/RDR3 SCRIPT native by hash or name — O(1), exact. Use whenever you see `Citizen.InvokeNative(0x...)`, `Citizen.invokeNative('0x...')`, `GetHashKey('NAME')`, or a SCREAMING_SNAKE_CASE native name (e.g. `SET_ENTITY_COORDS`, `GetPedHealth`) in Lua/JS/TS. NOT for game-data hashes (weapon/ped/animation names) — use `grep_docs`. Pass `hash` (0x… optional, case-insensitive) or `name` (exact first, ILIKE substring fallback). Returns name, hash, namespace, return type, params, description, full content, plus `findings[]` — community gotchas linked to that native. Inspect `findings[].id` and call `get_document({path: 'learning:<id>'})` for full body. Also returns `refDocs[]` — enum/flag value tables for that native (the constants to pass for params like flagId/attributeIndex/eventType). When `refDocs[].content` is set, it's the inline enum table — use those values directly. When `content` is null but `refDocs[].fetch` is present, the table was too large to inline — run that exact call (e.g. `get_document({ path: "refdoc:eEventType" })`) to get the full table; `refDocs[].preview` shows the first lines. github entries (no `fetch`) are url-only.

  Connector

• handoffs.agency

  AI Design Blueprint

  Authenticated — submit an agency engagement enquiry on behalf of the caller for a founder-led discovery call. Persists an AgencyHandoff row routed to the agency inbox; the user is contacted by the team for a scoped proposal. Engagement scopes: workflow sprint (rapid agentic workflow implementation), proof-of-concept (validate a specific agent design in a bounded timeframe), pilot support (co-design and validate a production-ready pilot), advisory (ongoing architectural guidance across a product team). WHEN TO CALL: the user has identified a paid hands-on expert engagement need beyond self-service learning, and explicitly asks to talk to the team or book a discovery call. ALWAYS confirm with the user before firing — this creates a sales-visible record. WHEN NOT TO CALL: for free training / partnerships discussion (use handoffs.partnership); for support / billing / access (use handoffs.operator); proactively or as a sales push. BEHAVIOR: write-only, single insert, side-effecting. Auth: Bearer <token> (Firebase ID token, any plan). UK/EU residency. Response confirms the ticket id + scope so the user can reference it.

  Connector

• lookup_native

  redm-mcp

  Resolve a RedM/RDR3 SCRIPT native by hash or name — O(1), exact. Use whenever you see `Citizen.InvokeNative(0x...)`, `Citizen.invokeNative('0x...')`, `GetHashKey('NAME')`, or a SCREAMING_SNAKE_CASE native name (e.g. `SET_ENTITY_COORDS`, `GetPedHealth`) in Lua/JS/TS. NOT for game-data hashes (weapon/ped/animation names) — use `grep_docs`. Pass `hash` (0x… optional, case-insensitive) or `name` (exact first, ILIKE substring fallback). Returns name, hash, namespace, return type, params, description, full content, plus `findings[]` — community gotchas linked to that native. Inspect `findings[].id` and call `get_document({path: 'learning:<id>'})` for full body. Also returns `refDocs[]` — enum/flag value tables for that native (the constants to pass for params like flagId/attributeIndex/eventType). When `refDocs[].content` is set, it's the inline enum table — use those values directly. When `content` is null but `refDocs[].fetch` is present, the table was too large to inline — run that exact call (e.g. `get_document({ path: "refdoc:eEventType" })`) to get the full table; `refDocs[].preview` shows the first lines. github entries (no `fetch`) are url-only.

  Connector

• getTiktokPostsByKeywords

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)

  Search Tiktok posts by keywords with three response modes. Searches post descriptions and video transcripts. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: trend analysis, hashtag tracking, viral content discovery across Tiktok. Returns by default: id, description, username, createdAtDate. NOT for URL lookups or post ID lookups - use getTiktokPostsByIds instead. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). Date filters: OMIT startDate/endDate parameters by default to retrieve all posts. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, description, userId, username, nickname, createdAtDate), Engagement (likeCount, commentCount, playCount, forwardCount, collectCount, downloadCount), Media (videoThumbnail, videoUrl, duration, postType), Content (hashtags, transcriptsJson). This is a safe, read-only tool for analyzing searchable information. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.

  Connector

• get_free_resources

  Makuri Showcase (CogniLedger)

  Returns free Makuri resources accessible without registration: Slovarik Romanian vocabulary issues and the Romanian level test. Use this when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools. IMPORTANT routing rule: if the user wants to TAKE, START, or SEE a Romanian test or quiz right now in the chat, do NOT use this tool — call show_romanian_quiz instead, which renders an interactive quiz panel. Use this tool only for questions ABOUT what free resources exist.

  Connector

• query_dataset

  tw-market-data

  Query rows with built-in look-ahead protection. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD) for backtesting/agent-learning. For non-point-in-time-safe datasets (fundamentals, monthly_revenue, dividend_policy…) rows are filtered by DISCLOSURE date <= as_of, so the agent only sees what was public at that moment. Omit as_of only for present-day lookups (warned). ★ IF A VALUE IS IN `coverage.missing`, IT IS NOT AVAILABLE. Say it is not available. **Never estimate it, interpolate it, infer it from a neighbouring period, or carry the last known value forward.** `coverage.missing` lists exactly what was requested and not returned, with a reason (e.g. "9999 在 as_of 當日未上市"). An empty `data` array alongside a populated `missing` list is a complete and correct answer to "what do you have" — not an invitation to fill the gap. ★ EVERY VALUE IS ATTRIBUTABLE. `provenance` carries {source, source_role, ingested_at, revision, provenance_uri}; `meta.query_id` names this exact question. Quote the query_id when reporting a number — `replay_query(query_id)` returns the bytes that were served, so the claim can be checked later. `freshness.is_stale` is computed server-side against the dataset's own cadence budget; `null` means it could not be determined, which is NOT the same as fresh. Args: dataset_id: see list_datasets. tickers: e.g. ['2330','2317']. start/end: 'YYYY-MM-DD' range. as_of: knowledge-time cutoff 'YYYY-MM-DD' (use for backtests). limit: <=5000. Returns: {meta:{table,coverage,row_count,as_of_applied,point_in_time_safe,warnings,query_id}, data:[...], provenance:{...}, coverage:{requested,returned,missing,reason}, freshness:{status,latest_available,expected_lag,is_stale}} Example: query_dataset('fundamental_income', tickers=['2330'], as_of='2023-06-30')

  Connector