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574,485 tools. Updated 2026-09-15 20:38

"Guide for Updating Code Version from Java 17 to 18 or Python 2 to 3" matching MCP tools:

  • Find working SOURCE CODE examples from 42 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs, .ts, .js) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C# (official SDKs) plus Rust and TypeScript/Node.js (community-maintained wrappers, not official) SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.
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  • Fetch PyPI download breakdown for a package by Python major version (2 vs 3) from pypistats.org. Returns per-version download counts.
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  • Support and end-of-life dates for a runtime, OS, database or framework version from endoflife.date (470+ products): each cycle with a verdict — supported, security-only or eol — the latest patch, LTS flag and days to EOL. Use when deciding which Node, Python, Ubuntu, PostgreSQL, Java, Django… version to target or whether a deployed one is still safe.
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  • 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.
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  • Decode one or more US medical codes to their official descriptions across ICD-10-CM (diagnoses), ICD-10-PCS (inpatient procedures), HCPCS Level II (supplies/drugs/services), and RxNorm (drugs, by RXCUI). Also decodes a National Drug Code (NDC) directly to its RxNorm product offline, tagged `source: "NDC"` — hyphenated in an FDA segment configuration (4-4-2, 5-3-2, 5-4-1, or the 11-digit 5-4-2) or as bare 10/11 digits; any other segment widths are malformed and stay unresolved. Auto-detects the system from each code's shape; pass an explicit `system` only when a value is genuinely ambiguous. Accepts 1–50 codes and returns partial success: resolved codes in `found`, unresolved in `notFound` with a per-code reason, so one bad code never fails the batch. Set `includeHierarchy` to attach each code's parent and immediate children (with a `childrenTruncated` flag when a code has more children than the cap returns — walk the full set via medcode_browse_hierarchy or medcode_map_codes). The resolved `system` is echoed on every result for chaining into medcode_map_codes or a billability check; a code string that also exists in another bundled system carries `alsoInSystems` naming it, so a single answer to a colliding code is never mistaken for the only one.
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  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • Transform any blog post or article URL into ready-to-post social media content for Twitter/X threads, LinkedIn posts, Instagram captions, Facebook posts, and email newsletters. Pay-per-event: $0.07 for all 5 platforms, $0.03 for single platform.

  • WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents
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  • 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.
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  • Resolve a postal/ZIP code to its place name(s), state/region, and coordinates. `country_code` is a 2-letter ISO code (US, GB, DE, ...); `postal_code` format varies by country (e.g. "90210" for the US, "SW1A 1AA" style outward codes for the UK). Use for "what city is ZIP 90210 in", "where is postal code X in country Y", or any question that needs a place name/region/lat-lon from a postal code -- not for the reverse (place name to postal code) or for full street address lookup. Some postal codes span multiple places, in which case all of them are returned. Returns an error dict (never raises) if the code isn't recognized for that country.
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  • Use this when the user asks about a prior decision, preference, person, project, or other fact that may be in their connected MemoryRouter vault. Semantically searches only the OAuth-selected vault and does not modify it. Tier guide: tier 3 holds the highest-level consolidated reflections (identity, principles, what matters most), tier 2 holds reflections of specific meaningful events, tier 1 holds raw verbatim memories. Match filters to the question. Broad 'most important things we have done' questions: tiers [3], importance 8 or higher, large limit such as 250. Big-picture overviews like 'what do you know about this area or period': tiers [3] with no importance filter. Specific lookups like 'find the decision about X': omit tiers to search all tiers blended and leave importance unset; a high importance filter hides the specific detail you are looking for. Specific meaningful events without raw noise: tiers [2] or [2, 3]. Exact verbatim detail: if the detail is not already in context, drill down by lineage: search tiers [3] to find the relevant thread, then use inspect_memory on the result to see the tier 2 reflections it consolidated, then inspect those to reach the raw memories underneath. Direct tiers [1] search works when you know exact wording to match. Mixing tiers is legitimate; adjust the importance threshold to the question rather than always setting it high. Only reflections (tiers 2 and 3) carry importance ratings. For time-window questions like 'what happened last week' or 'lately' use date_search_memories instead.
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  • Analyze a negative Amazon review for root cause and a suggested response (async; 2 credits). Extracts the underlying issue from a critical review and drafts a brand-appropriate response angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only; deducts 2 credits; runs asynchronously, poll for the result. Args: text: the negative review text (required). marketplace: marketplace code (default US). lang: zh or en (default en).
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  • Load full details for one product by its `productRef` (from search_products or browse_products): description, price, media, the brand's real product videos, size guide, per-variant stock, and buy links. Returns each variant's id and options (size/color). Use it to resolve the exact `variantId` the shopper wants before calling build_cart, and to answer fit/sizing questions from the size guide. The response includes the product image so it can be seen directly.
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  • Generates one or more images from a text prompt (T2I) or a text prompt + reference image(s) (I2I). Submits the job, polls until terminal, and returns the final image URLs. Default model is 'grok-imagine-t2i' (fast, 6 images per generation, 5 credits). Use list_image_models to see the full lineup with pricing. For I2I, pass `referenceImages` as an array of public image URLs and pick a model with I2I support (e.g. 'grok-imagine-i2i', 'wan-2.5-spicy-i2i'). ## Model selection guide (when the user does not specify a model) Default: `grok-imagine-t2i` (5 cr, 6 outputs per call, fast, general purpose). **Strong recommendation: when a single high-quality output is what's wanted** (most agent / one-shot workflows), prefer `gpt-image-2-t2i` (9 cr @ 1K / higher @ 2K, single deterministic image, best general quality across realism, illustration, typography, and composition; supports up to 2K resolution and most aspect ratios including auto). This is the front-runner for serious creative output where you don't need to pick from 6 variations. Pick a different model when the prompt has these signals: - "single best result" / "one image" / production / no time to pick from variations -> `gpt-image-2-t2i` (9 cr, 1 output, top general quality) - "photoreal" / "photo of" / "realistic" -> `gpt-image-2-t2i` (9 cr, best general realism) or `imagen-4` (12 cr, very high quality) or `z-image-turbo` (3 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K, or `grok-imagine-quality-t2i` (16 cr @ 1K, 22 cr @ 2K), or `imagen-4-ultra` - Text inside the image (signs, posters, typography) -> `ideogram-v3-t2i` (best in class) or `gpt-image-2-t2i` (also strong) - Artistic / painterly / stylized -> `midjourney-t2i` - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 6 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `ideogram-v3-t2i` - NSFW / adult / explicit -> `wan-2.5-spicy-t2i` (auto-tags creation as 18+; routes to adult gallery) - Cheapest possible / quick test -> `z-image-turbo` (3 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (6 outputs default) or use `numImages` on a multi-output model For I2I (reference image provided): prefer the dedicated `aetherwave_edit_image` tool for "change something in this image" intent. Use `aetherwave_generate_image` with I2I models only when you specifically want style transfer (`midjourney-i2i`), premium quality (`grok-imagine-quality-i2i`), or adult content (`wan-2.5-spicy-i2i`). Always pass an explicit `aspectRatio` (e.g. "1:1" for square album art, "16:9" for video thumbnails, "9:16" for shorts/reels). Some upstream providers reject submissions with no aspect ratio. Ask the user only when: - The prompt contradicts itself (e.g., "highest quality but cheapest") - The user requested "the best model" with no context, surface 2-3 options with tradeoffs - A single generation would cost more than 20 credits and the user has not confirmed
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  • WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • WORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NOT call this while convoreply is still running or before terraform_ready is confirmed! If you get 'session has not reached terraform-ready state', wait for convoreply to complete first. 🎯 USE THIS TOOL WHEN: convoreply has returned with terraform_ready=true, OR the user asks to 'see the terraforms', 'generate terraform', 'show me the code', etc. **DEFAULT RESPONSE**: Returns summary table + download URL (keeps code out of LLM context). **FALLBACK**: Set `include_code: true` to get full code inline if curl/unzip fails. **CRITICAL WORKFLOW** (default mode): 1. Call this tool to get file summary and download URL 2. ASK the user: 'Where would you like me to save the Terraform files? Default: ./insideout-infra/' 3. WAIT for user confirmation before running the download command 4. Run the curl/unzip command with the user's chosen directory 5. If curl/unzip FAILS (sandbox, security, platform issues), retry with `include_code: true` **AFTER GENERATION**: Ask user if they want to review the files and then deploy with tfdeploy REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: include_code (boolean) - set true to return full code inline as fallback. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.
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  • Generate direct-response video ad scripts by fusing a proven structural source (decoded ad or formula) with a brand's PowerSource. Output is feed-native ad copy for paid social (Meta, TikTok, Reels) in the brand's voice — hook, beat-by-beat body, CTA close, plus visual direction per beat. Takes source_id (from adformula_intelligence, decoder_intelligence, or decode_ad), source_type ("formula" or "decode"), powersource_id (from any create_powersource_*), and tunable params: count (1-5 variants, tensions and selling points auto-rotated across variants), script_mode ("blueprint" preserves source structure exactly, "remix" preserves psychology but writes original copy), duration (target seconds), audience, tension override, selling_points override, voice_mode ("creator" for UGC default, "brand" for owned channels), and idempotency_key. Use this when the user says "write me a script", "I need a TikTok script", "write an ad based on this", or wants shell-faithful replication of a proven winner in their own brand voice. REQUIRES both a structural source AND a powersource — guide the user through creating either if missing. Metered pricing — typically 2-5 credits per script (~2 credits for 15s, ~5 credits for 60s). Pre-flight reserves a 17-credit ceiling and refunds the difference after measurement. Do NOT use to discover sources — use decoder_intelligence or adformula_intelligence first. Do NOT use to extract brand intel — use create_powersource_url first.
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  • Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.
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  • Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.
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  • Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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  • Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.
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