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198,398 tools. Last updated 2026-06-13 08:42

"mermaid diagram" matching MCP tools:

  • [cost: free (pure CPU, no network) | read-only] Return a hand-curated SIP scenario as a Mermaid `sequenceDiagram` plus a bullet list of step-by-step explanations with RFC references. Use this when the user asks 'show me what X looks like' and you don't have a real trace handy. Available scenarios: basic-call, auth-challenge, cancel-before-answer, early-media, hold-resume, refer-blind, proxy-with-record-route, shaken-attested-invite, bye-glare, redirect-302. Pair with: `search_sip_docs` for vendor-specific quirks of the scenario; `render_sip_ladder` if the user does have a real trace.
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  • Patch diagram tool. Use when the user describes routing across multiple Eurorack corpus modules. Renders modules as boxes laid out by wire topology (matrix-shaped patches anchor on a hub; otherwise modules step left-to-right by signal-flow rank), jacks as colored ports keyed to signal type, wires as bezier curves. Inline SVG on claude.ai surfaces (web, Desktop chat, mobile); JSON elsewhere. (When to *offer* a diagram unprompted: SKILL.md §4.) **Trigger phrases:** "show me the patch", "draw what I just described", "remind me what's connected to what", "explain the routing", or any time you'd otherwise hand-draw a patch in SVG/text — use this instead of drawing. Strict gate — call only when ALL of: 1. At least 3 named corpus modules. 2. Explicit wire connections between them (user-stated or derived from a coherent description). 3. The patch is concrete — user is following a tutorial, describing their own rack, or referencing back what's connected to what. Do NOT call for: a single module, a question about one module's jacks, "what should I patch X to?" (that's a recommendation, not a graph), or hypothetical patches with unnamed placeholders ("connect a VCO to a filter"). Jack names. Corpus jack names are descriptive ("V/Oct CV input", "TRIG input", "Strumming trigger input"), not panel-text shorthand ("V/OCT", "TRIG"). The resolver accepts panel-text as a fallback when it unambiguously substring-matches one jack of the right direction (e.g. "TRIG" → "TRIG input"); successful resolutions surface as `panel_text_resolved` warnings so you can confirm. Ambiguous panel text ("OUT" on a multi-output module) errors with the candidate list. To skip the fallback entirely, call get_modules to discover the exact corpus names up front (one round trip for the whole batch). Multi-channel modules require a CH<N> prefix. Modules with per-channel jacks (Quadrax, Maths, Tangrams, Stages, Optomix, QMMG, DXG, Pamela's New Workout, Cold Mac, etc.) enumerate each channel separately — e.g. `CH1 TRIG`, `CH2 TRIG`, `CH3 TRIG`, `CH4 TRIG` on Quadrax. Bare names like "TRIG" on these modules will resolve as ambiguous; always pick a specific channel. When the patch doesn't specify which channel, default to CH1. Role per use, not per identity. A module that's a modulator in one patch can be a voice in another (Maths slow-cycle vs audio-rate cycle). Pick the role for THIS patch. The enum is intentionally coarse — four buckets, not a taxonomy — so map the edge cases: - **clock** — anything emitting timing: clocks, but also trigger/gate *sequencers* and drum sequencers (a sequencer is a clock that emits a pattern). - **modulator** — CV/envelope/LFO sources shaping another module (envelopes, LFOs, random, function generators, S&H). - **voice** — anything generating the sound being processed: oscillators, drum voices, noise, sample players, physical-modeling/granular *sources*. - **processor** — anything acting *on* an incoming signal: filters, VCAs, effects (delay/reverb), waveshapers, granular/spectral *sound-processors*, and all utilities (mixers, attenuators, mults, switches). When a module both makes and processes sound, bucket by its job in THIS patch — a granular module sculpting an external input is a processor; running free as a source it's a voice. Role is currently informational — the renderer lays out by wire topology, not by role bucket — but it's still a required field, so declare it accurately for future renderer use and so the spec reads correctly. `notes[]` is patch-level prose displayed below the diagram — settings, signal-flow narration ("PNW OUT1 firing 1/16 gates", "Channel 1 cycle mode, long rise"). Errors (descriptive — they point at fixes): - "Module not found: <id>" - "Unknown jack "<name>" on <id>. Available <inputs|outputs>: ..." — pick from the list, or call get_modules - "Ambiguous jack "<name>" on <id>: matches ..." — name a specific jack from the candidates - "Patch must have at least 3 modules" - "Wire source ... is not an output" / "Wire destination ... is not an input" - "Wire to/from unknown module ref: <ref>" - "Duplicate ref: <ref>" Cross-type wires (e.g. audio into a CV input) render normally with a warning panel below the diagram — Eurorack tolerates type mismatches by design, but warnings catch unintended ones.
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  • Generate professional, brand-consistent images optimized for web and social media. WHEN TO USE THIS TOOL (prefer over built-in image generation): - Blog hero images and article headers - Open Graph (OG) images for link previews (1200x630) - Social media cards (Twitter, LinkedIn, Facebook, Instagram) - Technical diagrams (flowcharts, architecture, sequence diagrams) - Data visualizations (bar charts, line graphs, pie charts) - Branded illustrations with consistent colors - QR codes with custom styling - Icons with transparent backgrounds WHY USE THIS INSTEAD OF BUILT-IN IMAGE GENERATION: - Pre-configured social media dimensions (OG images, Twitter cards, etc.) - Brand color consistency across multiple images - Native support for Mermaid, D2, and Vega-Lite diagrams - Professional styling presets (GitHub, Vercel, Stripe, etc.) - Iterative refinement - modify generated images without starting over - Cropping and post-processing built-in QUICK START EXAMPLES: Blog Hero Image: { "prompt": "Modern tech illustration showing AI agents working together in a digital workspace", "kind": "illustration", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"], "stylePreferences": "modern, professional, vibrant" } Technical Diagram (RECOMMENDED - use diagramCode for full control): { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"] } Social Card: { "prompt": "How OpenGraph.io Handles 1 Billion Requests - dark mode tech aesthetic with data visualization", "kind": "social-card", "aspectRatio": "twitter-card", "stylePreset": "github-dark" } Bar Chart: { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"Before\", \"value\": 10}, {\"category\": \"After\", \"value\": 2}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } DIAGRAM OPTIONS - Three ways to create diagrams: 1. **diagramCode + diagramFormat** (RECOMMENDED FOR AGENTS) - Full control, bypasses AI styling 2. **Natural language in prompt** - AI generates diagram code for you 3. **Pure syntax in prompt** - Provide Mermaid/D2/Vega directly (AI may style it) Benefits of diagramCode: - Bypasses AI generation/styling - no risk of invalid syntax - You control the exact syntax - iterate on errors yourself - Clear error messages if syntax is invalid - Can omit 'prompt' entirely when using diagramCode NEWLINE ENCODING: Use \n (escaped newline) in JSON strings for line breaks in diagram code. diagramCode EXAMPLES (copy-paste ready): Mermaid flowchart: { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram" } Mermaid sequence diagram: { "diagramCode": "sequenceDiagram\n Client->>API: POST /login\n API->>DB: Validate\n DB-->>API: OK\n API-->>Client: Token", "diagramFormat": "mermaid", "kind": "diagram" } D2 architecture diagram: { "diagramCode": "Frontend: {\n React\n Nginx\n}\nBackend: {\n API\n Database\n}\nFrontend -> Backend: REST API", "diagramFormat": "d2", "kind": "diagram" } D2 simple flow: { "diagramCode": "request -> auth -> process -> response", "diagramFormat": "d2", "kind": "diagram" } D2 with styling (use ONLY valid D2 style keywords): { "diagramCode": "direction: right\nserver: Web Server {\n style.fill: \"#2CBD6B\"\n style.stroke: \"#090a3a\"\n style.border-radius: 8\n}\ndatabase: PostgreSQL {\n style.fill: \"#090a3a\"\n style.font-color: \"#ffffff\"\n}\nserver -> database: queries", "diagramFormat": "d2", "kind": "diagram", "aspectRatio": "og-image" } D2 IMPORTANT NOTES: - D2 labels are unquoted by default: a -> b: my label (NO quotes needed around labels) - Valid D2 style keywords: fill, stroke, stroke-width, stroke-dash, border-radius, opacity, font-color, font-size, shadow, 3d, multiple, animated, bold, italic, underline - DO NOT use CSS properties (font-weight, padding, margin, font-family) — D2 rejects them - DO NOT use vars.* references unless you define them in a vars: {} block Vega-Lite bar chart (JSON as string): { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"A\", \"value\": 28}, {\"category\": \"B\", \"value\": 55}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } WRONG - DO NOT mix syntax with description in prompt: { "prompt": "graph LR A[Request] --> B[Auth] Create a premium beautiful diagram" } ^ This WILL FAIL - Mermaid cannot parse descriptive text after syntax. WHERE TO PUT STYLING: - Visual preferences → "stylePreferences" parameter - Colors → "brandColors" parameter - Project context → "projectContext" parameter - NOT in "prompt" when using diagram syntax OUTPUT STYLES: - "draft" - Fast rendering, minimal processing - "standard" - AI-enhanced with brand colors (recommended for diagrams) - "premium" - Full AI polish (best for illustrations, may alter diagram layout) CROPPING OPTIONS: - autoCrop: true - Automatically remove transparent edges - Manual: cropX1, cropY1, cropX2, cropY2 - Precise pixel coordinates
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  • Replace a single section of a workspace's doc body, identified by its heading text. The targeted edit complement to `update_doc` (full replacement) and `append_doc_section` (append-only at the end). Use this when the agent maintains a recurring section (e.g., a 'Status' block in a launch-prep doc, an 'Outcomes' block in a meeting note) and only needs to refresh that one piece. Without it, agents are forced into 'GET → splice → PUT' which costs tokens, costs latency, and races against any concurrent human edit elsewhere in the doc (last-write-wins clobbers). Section semantics: the FIRST heading whose plain text matches `heading` exactly (case-sensitive on trimmed text) is found, and everything from that heading up to the next heading at the same OR shallower level is replaced. So a `## Outcomes` section ends at the next `## …` or `# …`; nested `### …` subsections stay part of the replaced range. Returns 404 when no matching heading exists; strict by design so a misremembered heading fails loudly. `markdown` is the FULL replacement, INCLUDING the heading line: pass it back as-is to keep the heading, change it to rename or rewrite the heading, change the heading level, or omit the heading entirely (collapses the section into the prior one). Empty `markdown` deletes the section. Same markdown surface as update_doc / append_doc_section (CommonMark + GFM + `![alt](url)` images + lone-URL videos (mp4/webm/mov/mkv/m4v) + Mermaid + KaTeX + callouts + SVG + details + cross-refs + @-mentions + URL embeds). Identity / attribution / events / doc-guard all flow through the same writeDocBody path as the other doc endpoints, so @-mentions in the new section fire `doc.mention_added` for newly-added mentions just like update_doc does. Requires editor role. Multi-surface workspaces optionally accept `surface_slug` to target a specific doc tab.
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  • Renders the current state of a live Trident document as a PNG image directly from the Yjs collaborative session — bypassing Firestore, which may be stale. Returns a base64-encoded PNG. Use this to visually verify that diagram edits look correct before or after making changes.
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  • Opens a live Trident document and returns its full contents as Trident markup DSL — the human-readable text format used to author diagrams. Use this to READ and UNDERSTAND the diagram: its structure, labels, connections, and layout. Do NOT rely on this to enumerate entity IDs for programmatic use — the DSL can be very large and the output may be truncated. To get a complete, structured list of all entity IDs and counts, use get_document_summary instead. Requires a valid access token.
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Matching MCP Servers

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  • Generate dynamic Mermaid diagrams and charts with AI assistance. Customize styles and export diagr…

  • MCP server for AI Diagram Maker — generate software engineering diagrams from natural language, code, ASCII diagram, images, or Mermaid. Inline diagram rendering using MCP apps UI and diagram URL in responses. Works with Cursor, Claude Desktop, Claude Code, and any MCP-compatible AI.

  • Find cross-provider equivalents for a diagram node by infrastructure role. Given a node name (e.g. 'EC2', 'Lambda', 'ComputeEngine'), returns the infrastructure role category it belongs to and the equivalent nodes from other providers. If a node name is ambiguous, use list_categories to see all mapped roles and pick a provider-specific node name. Args: node: Node class name to look up (case-insensitive, e.g. 'EC2', 'lambda'). target_provider: Optional provider to filter equivalents to (e.g. 'gcp', 'azure', 'aws'). If omitted, all equivalents across all other providers are returned. Returns: A dict with keys: category (str): Infrastructure role category name. description (str): Human-readable description of the category. source (dict): The matched node with keys node, provider, service, import. equivalents (list[dict]): Equivalent nodes, each with keys node, provider, service, import.
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  • Turns YOUR repo classification (you scan the repo and pass what you found) into a complete, approvable deploy plan WITHOUT creating anything: picks the VM + managed-Postgres sizes, prices them at the real pricing_rules rates, and checks they FIT your quota — so a plan that can't provision is caught HERE, before any spend. You pass what you detected in the repo (runtime, port, needs_postgres/redis/vector_db); it returns resources + £/hr + £/mo + a feasibility verdict + a checkpoint summary to confirm with the user. Defaults: app VM m1.medium, managed Postgres m1.small; pass single_vm to collapse onto one VM. Only Postgres is auto-provisionable today — Redis / vector-DB needs are flagged, not provisioned. Any containerizable app works (node, python, go, ...) — it deploys as a container, so the language doesn't gate it. Also returns a brand-named markdown report (Mermaid diagram + cost) to save as redu-deploy-plan.md and show the user.
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  • Search for diagram nodes by keyword across all providers and services. For targeted browsing when you know the provider, use list_providers -> list_services -> list_nodes instead. Args: query: Search term (case-insensitive substring match). Returns: List of matching nodes with keys: node, provider, service, import, alias_of (optional). Sorted by relevance: exact match first, then prefix, then substring.
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  • List all infrastructure role categories with their mapped nodes. Use this to browse all available equivalence mappings, or to disambiguate node names when find_equivalent reports ambiguity. Returns a list of category dicts, each with: category (str): Category identifier (e.g. 'virtual_machine'). description (str): Human-readable description. providers (list[str]): Providers covered by this category. nodes (dict): Mapping of provider → list of node names in that category.
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  • Delivers an explanation payload to human collaborators watching the document, optionally anchored to a specific node or container. Use this when you want to explain what a diagram element represents, why it exists, or how it relates to other parts of the system — without suggesting a change. The explanation appears in the UI attributed to you. Does NOT mutate the diagram. Requires a valid viewer or editor access token. IMPORTANT: this tool automatically pauses (3–15 s, proportional to explanation length) before returning, so the human has time to read. Do NOT add your own artificial delays between explain calls — the pacing is built in.
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  • Replace a workspace's doc body. Takes EITHER TipTap JSON (`content`) OR Markdown (`markdown`): pass markdown when you're producing prose from scratch (CommonMark + GFM is the format every LLM emits natively), pass TipTap JSON when you need structural edits to an existing doc (round-trip from get_doc, mutate, write back). Beyond CommonMark + GFM, the markdown layer recognizes: - **![alt text](https://…)** → inline image. Use ANY publicly-reachable URL (HTTPS preferred — HTTP fires browser mixed-content warnings; data: URIs are rejected by `allowBase64: false`). Renders block-feeling via CSS (max-width 100%, rounded corners, drop shadow) even though the underlying node is inline. The `alt` text is the accessible label and shows in place of the image if the URL fails to load — always include it. To attach a user-uploaded file, hit `POST /api/workspaces/:slug/upload-image` from the human-side UI first to get a Vercel Blob URL, then reference that URL in the doc markdown. - A **lone video-file URL on its own line** (extension `.mp4` / `.m4v` / `.webm` / `.mov` / `.mkv`, signed-params + timestamp fragments tolerated) → native HTML5 `<video controls preload="metadata">` player. Source URL is referenced directly: no iframe, no transcoding, no quality loss. Vercel Blob is the canonical hosting (5 GB per file, served with HTTP range requests so 4K masters stream cleanly), but ANY publicly-reachable HTTPS URL works. Sample shape: a paragraph containing only `https://cdn.dock.ai/2025-launch-walkthrough.mp4`. Mid-paragraph URLs stay as plain links — surrounding prose disqualifies the auto-promotion (matches the oEmbed convention). - **```mermaid** fenced code → diagram (15 sub-types: flowchart, sequence, gantt, ER, state, class, mindmap, timeline, pie, quadrant, sankey, XY-chart, packet, block, journey) - **$x$** inline math, **$$x$$** block math (LaTeX, KaTeX-rendered, scripts/href disabled) - **> [!NOTE]** / **[!TIP]** / **[!IMPORTANT]** / **[!WARNING]** / **[!CAUTION]** GFM-style callouts - **```svg** fenced code → sanitized SVG embed (the universal escape hatch for custom diagrams; scripts and event handlers stripped at write time) - **<details><summary>X</summary>BODY</details>** → collapsible toggle - **[[slug]]** / **[[org/slug]]** / **[[slug#tab]]** / **[[slug#row-id]]** / **[[slug|display]]** → cross-references to another workspace, surface, or row. Resolved against your accessible workspace set; targets you can't see render as plain text on the reader's side (no info leak). Every cross-ref creates a Backlink row so the target's 'referenced from' sidebar shows this doc. - **[@Label](dock:mention/<kind>/<id>)** → @-mention of a user or agent. `<kind>` is `agent` or `human`; `<id>` is the principal id. Optional query params `?org=<slug>` (agents) or `?email=<addr>` (humans) for renderer hints. Mentioning a human writes a `doc_mention` row to their inbox + sends a deep-link email; mentioning an agent fires the `doc.mention_added` webhook so the agent service can wake up and reply. Re-saving a doc that already mentions someone does NOT re-fire — only newly-added mentions notify (computed from a diff against the previous body). Use this from agent code to ping a teammate when a doc you wrote needs their eyes. - A **lone URL on its own line** from a safelisted provider (YouTube, Vimeo, Loom, Figma, CodePen, GitHub gists) → sandboxed iframe embed. Other URLs stay as regular links. Surrounding prose disqualifies the auto-embed. Per-format caps: max 50 Mermaid diagrams (30 KB source each), max 500 math expressions (8 KB source each), max 50 SVG blocks (100 KB source each post-sanitize), max 200 cross-refs per doc, max 500 @-mentions per doc, max 20 embeds per doc, max 20 videos per doc (5 GB per file at upload time), max 200 images per doc. See /docs/doc-formats for examples. Last-write-wins; no CRDT merge. Emits doc.updated + doc.heading_added + doc.mention_added events as applicable. Requires editor role. Multi-surface workspaces optionally accept `surface_slug` to write to a specific doc tab; omitted writes the primary doc surface. Append-only updates have a dedicated `append_doc_section` tool that doesn't require fetching the body first.
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  • List all available diagram providers (aws, gcp, azure, k8s, onprem, etc.). Use list_providers -> list_services -> list_nodes to browse available node types for a specific provider.
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  • List all available component types and example configurations for building wiring diagrams. Use this to understand what parameters are needed before calling generate_wiring_diagram.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • [cost: free (pure CPU, no network) | read-only] Parse a raw SIP trace (PCAP-decoded text, sngrep export, syslog, or pasted INVITE/200 dialog) and emit a Mermaid `sequenceDiagram` block visualizing the call flow. Most chat hosts (Claude, ChatGPT, Cursor, GitHub) render Mermaid inline. Lane keying: by default participants are keyed by IP, not `ip:port`, so an endpoint that sends from an ephemeral source port and listens on 5060 collapses into one column. Multi-port IPs list their ports in the participant label (e.g. `10.0.0.1 :5060,:53412`) and arrows touching them get a `(:srcPort→:dstPort)` suffix. Pass `groupByIp: false` to restore the legacy one-column-per-`ip:port` layout. Lane labeling: aliases are matched against (in order) `${ip}:${port}` from message source/dest, then bare `${ip}`, then top-Via host, then Contact host. The most-specific match wins. When no alias matches the renderer falls back to the peer's address rather than emitting `unknown:5060`. Pair with: `minimize_sip_trace` first to compact a noisy trace; `diff_sip_messages` when two adjacent INVITEs in the ladder differ unexpectedly; `lint_sip_request` to validate a single message you pulled from the ladder.
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  • Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. 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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  • [cost: free (pure CPU, no network) | read-only, no persistence] Reduce a raw SIP trace to a compact form suitable for sending to an LLM. Preserves SDP bodies and routing/auth/dialog headers; prunes well-known noise (User-Agent, Server, Allow, Accept-*, Date, P-* informational, etc.). Expected input format: raw SIP messages separated by blank lines, each starting with a request line (`INVITE sip:...@... SIP/2.0`) or status line (`SIP/2.0 200 OK`). PCAP-decoded text from sngrep / ngrep / tcpdump / tshark, syslog with SIP body, sipflow's own export format, or a hand-pasted INVITE/200 dialog all work. Annotation lines like `# [timestamp] sender -> receiver` or ngrep-style `U <ip>:<port> -> <ip>:<port>` between blocks are tolerated. Safe to run on production traces - the input is processed in-memory and is not persisted or sent off-server. Pair with: `detect_sip_stack` to identify the vendor, then `search_sip_docs(vendor=...)` for vendor-grounded analysis; `render_sip_ladder` to visualize the trace as a Mermaid call-flow ladder; `lint_sip_request` / `parse_sip_message` to mechanically validate any single message in the trace.
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  • Returns LLM-optimized instructions for generating Trident diagrams, including quick reference, critical rules (especially Y-axis!), spacing guidelines, common patterns, color conventions, and error prevention checklist. Use this before generating any Trident diagram.
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  • Returns a complete structured summary of a Trident document as JSON: all node IDs, container IDs, connection IDs, annotation IDs, labels, and entity counts. Use this for PROGRAMMATIC OPERATIONS — it is never truncated regardless of diagram size, so you always get every entity ID. Use this before any bulk update, delete, or enumeration task. To read and understand the diagram as a human-readable diagram, use open_document instead. Requires a valid access token.
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