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136,781 tools. Last updated 2026-05-21 23:59

"Managing and Processing Jira Tickets" matching MCP tools:

  • Cut and assemble a clip from any prior video job (find_clips, summarize, or video transcribe). Operates on a parent job — possessing the parent `source_job_id` is the capability, no upload step. Pass one segment for a simple cut, or multiple non-contiguous segments to compose a single mp4 highlight reel — same flat $0.50 either way. Two-call flow: (1) call with `source_job_id` + `segments` (ordered array of `{start, end, label?}` in source seconds, total duration capped at 30 minutes) to receive {job_id, payment_challenge}; (2) pay via MPP and call with `job_id` + `payment_credential` to start processing. No upload step. Poll get_job_status(job_id) for completion; outputs are role `clip-video` (the assembled .mp4, frame-accurate boundaries with 15ms audio fades at segment joins) and — when `include_transcript: true` (default) — roles `clip-srt` + `clip-words` (transcripts stitched and time-shifted to match the assembled video). Set `include_transcript: false` to skip transcript outputs. Payment: MPP — accepts Tempo USDC and Stripe SPT. The challenge's WWW-Authenticate header and /.well-known/mpp.json are authoritative for which methods are offered. Source must still be in storage (72h TTL for find_clips parents, 24h elsewhere — check `expires_at` from get_job_status on the parent). Multiple extract_clip calls against one parent are independent paid jobs. Failed jobs auto-refund.
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  • Chat with the Roboflow AI agent. Use this tool for: - **Roboflow Q&A** — the agent has the full Roboflow documentation indexed (SDKs, REST API, deployment options, training, batch processing, Universe, blocks, pricing, etc.). Ask it anything about how Roboflow works. - **Advanced workflow building** — workflows complex enough that direct block composition via ``workflow_blocks_*`` is impractical. The agent knows every block and connection pattern. - **Solution planning** — pass ``mode="plan"`` and the user's problem; the agent uses a stronger planning model to scope a CV solution end-to-end before any building happens. For straightforward workflows you can construct yourself, the direct ``workflow_*`` tools are fine — you don't have to route every workflow through the agent. ## Conversation flow The agent runs a multi-step conversation. It may ask clarifying questions, recommend a model, or (in plan mode) produce a plan for confirmation. Pass the returned ``conversation_id`` back on follow-up calls to keep context. Use ``agent_conversations_list`` and ``agent_conversation_get`` to find and resume past conversations. ## CRITICAL: the agent NEVER publishes workflows Every workflow the agent creates or edits is saved as a **draft**. The published version that callers using the workflow by id will hit is unchanged until you explicitly publish. To make agent edits live, call ``agent_workflow_publish`` with the workflow ``url`` returned in the chat response. ## Running an agent-built workflow Two options: 1. **Run the draft directly without publishing** — pass the ``specification`` returned in the chat response to ``workflow_specs_run``. Best for testing the draft, or for one-off runs where you don't want to disturb the currently-published version. 2. **Publish, then run by id** — call ``agent_workflow_publish(workflow_url=...)`` then ``workflows_run(workflow_id=..., images=...)``. Use this when you want the change to go live for everyone using the workflow by id. ## Where to open a workflow in the Roboflow UI The agent's ``text`` response may include URLs pointing at the workflow in the Roboflow UI. **Ignore those URLs** — the agent sometimes picks the wrong host or path. Each workflow in the ``workflows`` array has an ``app_url`` field with the correct, environment-aware URL (built from the current ``APP_URL`` plus ``/{workspace}/solutions/chat?workflowUrl=...``) — show that one to the user instead. ## Response shape - ``text`` — the agent's reply. - ``workflows`` — workflows created or edited in this turn, each with ``id``, ``name``, ``url`` (slug), ``app_url`` (clickable Roboflow UI URL — use this), and ``specification`` (the full draft JSON; pass it to ``workflow_specs_run`` to execute without publishing). - ``conversation_id`` — pass back to continue the conversation.
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  • Recover detail from camera-shake and accidental motion blur. NAFNet (ECCV 2022, SOTA on GoPro/SIDD benchmarks). Best for: handheld shake, bumped camera, whole-frame uniform blur. NOT effective for: intentional panning blur, bokeh/depth-of-field, or artistic motion effects. Also supports denoising (grainy/noisy photos). 20 sats per image (~2 min processing), pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='deblur_image'.
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  • Generate a CeeVee career-intel report asynchronously (15 credits, takes 2-3 min). Returns report_id and status. POLLING: Call ceevee_get_report(report_id) every 30 seconds, up to 40 times (20 min max). If status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay the error_message to the user. If still processing after 40 polls, stop and inform the user with the report_id so they can check later. Call ceevee_list_report_types first to discover valid report_type values and required inputs. Report categories: Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, Fractional Leadership.
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  • Check multiple URLs in a single batch. Returns results for all URLs, handling async processing automatically. Each URL is analysed across seven dimensions: redirect behaviour, brand impersonation, domain intelligence (age, registrar, expiration, status codes, nameservers via RDAP), SSL/TLS validity, parked domain detection, URL structural analysis, and DNS enrichment. Known and cached URLs return results immediately. Unknown URLs are queued for pipeline processing. This tool automatically polls for results until all URLs are complete or the 5-minute timeout is reached. You don't need to manage polling or job tracking. If the timeout is reached before all results are complete, returns whatever is available with a clear message indicating which URLs are still processing. The user can check results later via check_history. Maximum 500 URLs per call. For larger datasets, call this tool multiple times with chunks of up to 500 URLs. Billing: Same as check_url. Known and cached domains are free. Only unknown domains running through the full pipeline cost 1 credit each. The summary shows pipeline_checks_charged (the actual number of credits consumed). If you don't have enough credits for the unknowns in the batch, the entire batch is rejected with a 402 error telling you exactly how many credits are needed. Duplicate URLs in the list are automatically deduplicated (processed once, charged once). Invalid URLs get individual error status without rejecting the batch. Use the "profile" parameter to score all results with custom weights.
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  • Evolutionary Symbolic Regression (PySR). Discovers algebraic equations y = f(x1, x2, ...) from feature/target data. Returns a Pareto front ranked by the complexity/accuracy tradeoff. Slower than SINDy (10-60s); searches often terminate early on convergence. For differential equations from time series, use sindy_run instead. Pricing: free tier up to 100 rows × 8 features, 60s timeout. Beyond that, $0.25 + $0.03 per 100 extra rows + $0.01 per extra feature squared, timeout up to 300s (5 min), via x402 (USDC on Base) or MPP/Stripe. MPP/Stripe adds a flat $0.35 per-transaction fee (Stripe processing), so the MPP challenge amount in a `payment_required` response is $0.35 higher than the x402 amount for the same base price; x402 gets the lower rate. Omit `payment` for free-tier requests; paid requests without a valid credential receive a `payment_required` result with pricing and accepted schemes. Full pricing: occam://pricing Advisory limits: jobs over 50,000 rows or 20 features are accepted but may not converge; response carries a top-level `warning`. Operators: fixed supported set only — custom operators (e.g. 'inv(x) = 1/x') are rejected. Unary: sin, cos, tan, exp, log, log2, log10, sqrt, abs, sinh, cosh, tanh. Binary: +, -, *, /, ^. See also prompt `supported_operators`. Loss metric: `loss` (in `pareto_front[].loss` and `best_loss`) is mean squared error between model prediction and `y` on the full training set — not RMSE, and not normalized by Var(y). A threshold appropriate for one dataset scales with y's magnitude, so set `loss_threshold` with that in mind (e.g. for y values near 1.0, 1e-6 is a tight fit; for y near 1000, the equivalent is 1.0). Early termination: set `loss_threshold` to stop at your noise floor. The server also stops when the search stalls (<1% improvement in the last third of the budget); disable with `stall_detection=false`. Response `stop_reason` is one of: loss_threshold, stall, timeout, natural. If `feature_names` is supplied, its length must equal the number of columns in `X`; a mismatch is rejected with a validation error. Follow-up: call `pysr_uncertainty` with a chosen expression and the same dataset for bootstrap confidence intervals on its fit constants and optional prediction bands. Rate limit: 10 requests/hour per IP, 200/hour global, max queue depth 20 (shared with sindy_run and pysr_uncertainty). Response (success) includes `pareto_front[]` (each with `complexity`, `loss`, `expression`, `expression_latex`), `best_expression`, `best_expression_latex`, `best_loss`, `best_complexity`, `stop_reason`, `elapsed_seconds`, `queue_seconds` (>0 = server saturated; use as backoff signal), optional `warning`, optional `_meta` (MPP receipt). Full response and payment-required schemas: occam://tool-schemas Example request: X=[[0.0], [1.0], [2.0], [3.0]], y=[1.0, 3.0, 5.0, 7.0], feature_names=["x"], max_complexity=10, timeout_seconds=15 Policy: occam://privacy-policy — Citation: occam://citation-info
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  • Jira MCP Pack

  • Image processing for AI agents. Resize, convert, compress, and pipeline images.

  • Check the status of a Disco run. Returns current status and progress details: - status: "pending" | "processing" | "completed" | "failed" - job_status: underlying job queue status - queue_position: position in queue when pending (1 = next up) - current_step: active pipeline step (preprocessing, training, interpreting, reporting) - estimated_wait_seconds: estimated queue wait time in seconds (pending only) Poll this after calling discovery_analyze. Use discovery_get_results to fetch full results once status is "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.
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  • Returns file metadata (content_type, download_url, download_size, expires_at) for the report or zip artifact. Use artifact='report' (default) for the interactive HTML report (~700KB, self-contained with embedded JS for collapsible sections and interactive Gantt charts — open in a browser). Use artifact='zip' for the full pipeline output bundle (md, json, csv intermediary files that fed the report). While the task is still pending or processing, returns {ready:false,reason:"processing"}. Check readiness by testing whether download_url is present in the response. Once ready, present download_url to the user or fetch and save the file locally. Download URLs expire after 15 minutes (see expires_at); call plan_file_info again to get a fresh URL if needed. Terminal error codes: generation_failed (plan failed), content_unavailable (artifact missing). Unknown plan_id returns error code PLAN_NOT_FOUND.
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  • Validates the format of a Colombian Cédula de Ciudadanía (CC) — the national identity document for Colombian citizens. Verifies that the number is between 6 and 10 digits as required by the Registraduría Nacional. Returns { valid: boolean, cc: string } or { valid: false, reason: string }. Use when processing Colombian individual tax filings, employment contracts, or KYC onboarding flows requiring a verified Colombian citizen ID. Note: checksum validation is not publicly available for CC numbers.
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  • Validates a Brazilian CNPJ (Cadastro Nacional da Pessoa Jurídica) — the 14-digit company registration number issued by the Receita Federal. Applies the official two-pass weighted checksum algorithm. Returns { valid: boolean, cnpj: string } for valid CNPJs, or { valid: false, reason: string } for invalid format or failed checksum. Use when processing Brazilian B2B invoices (Notas Fiscais), supplier onboarding, or any compliance workflow requiring a verified Brazilian company tax ID. Offline validation only.
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  • Use this whenever a user asks how many posts were published today, yesterday, this week, or in another date range, or asks what is queued/processing after publishing. This counts actual published delivery receipts separately from queued or processing posts, so do not describe queued posts as published.
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  • Start an async rank of multiple candidates against a job description (8 credits). Returns task_id and analysis_id. Poll with careerproof_task_status, then fetch result with careerproof_task_result (result_type='fit_rank'). Requires context_id from atlas_list_contexts, candidate_ids from atlas_list_candidates (minimum 2), and jd_text. For async batch processing with more detail, use atlas_start_jd_fit_batch instead.
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  • Retrieve the full GLEIF LEI record for one legal entity using its 20-character LEI code. Returns legal name, registration status, legal address, headquarters address, managing LOU, and renewal dates. Use this tool when: - You have a LEI (from SearchLEI) and need full entity details - You want to verify the registration status and renewal date - You need the exact legal address and jurisdiction of an entity Source: GLEIF API (api.gleif.org). No API key required.
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  • Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in milliseconds
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  • Scrape content from a single URL with advanced options. This is the most powerful, fastest and most reliable scraper tool, if available you should always default to using this tool for any web scraping needs. **Best for:** Single page content extraction, when you know exactly which page contains the information. **Not recommended for:** Multiple pages (call scrape multiple times or use crawl), unknown page location (use search). **Common mistakes:** Using markdown format when extracting specific data points (use JSON instead). **Other Features:** Use 'branding' format to extract brand identity (colors, fonts, typography, spacing, UI components) for design analysis or style replication. **CRITICAL - Format Selection (you MUST follow this):** When the user asks for SPECIFIC data points, you MUST use JSON format with a schema. Only use markdown when the user needs the ENTIRE page content. **Use JSON format when user asks for:** - Parameters, fields, or specifications (e.g., "get the header parameters", "what are the required fields") - Prices, numbers, or structured data (e.g., "extract the pricing", "get the product details") - API details, endpoints, or technical specs (e.g., "find the authentication endpoint") - Lists of items or properties (e.g., "list the features", "get all the options") - Any specific piece of information from a page **Use markdown format ONLY when:** - User wants to read/summarize an entire article or blog post - User needs to see all content on a page without specific extraction - User explicitly asks for the full page content **Handling JavaScript-rendered pages (SPAs):** If JSON extraction returns empty, minimal, or just navigation content, the page is likely JavaScript-rendered or the content is on a different URL. Try these steps IN ORDER: 1. **Add waitFor parameter:** Set `waitFor: 5000` to `waitFor: 10000` to allow JavaScript to render before extraction 2. **Try a different URL:** If the URL has a hash fragment (#section), try the base URL or look for a direct page URL 3. **Use firecrawl_map to find the correct page:** Large documentation sites or SPAs often spread content across multiple URLs. Use `firecrawl_map` with a `search` parameter to discover the specific page containing your target content, then scrape that URL directly. Example: If scraping "https://docs.example.com/reference" fails to find webhook parameters, use `firecrawl_map` with `{"url": "https://docs.example.com/reference", "search": "webhook"}` to find URLs like "/reference/webhook-events", then scrape that specific page. 4. **Use firecrawl_agent:** As a last resort for heavily dynamic pages where map+scrape still fails, use the agent which can autonomously navigate and research **Usage Example (JSON format - REQUIRED for specific data extraction):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/api-docs", "formats": ["json"], "jsonOptions": { "prompt": "Extract the header parameters for the authentication endpoint", "schema": { "type": "object", "properties": { "parameters": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "type": { "type": "string" }, "required": { "type": "boolean" }, "description": { "type": "string" } } } } } } } } } ``` **Prefer markdown format by default.** You can read and reason over the full page content directly — no need for an intermediate query step. Use markdown for questions about page content, factual lookups, and any task where you need to understand the page. **Use JSON format when user needs:** - Structured data with specific fields (extract all products with name, price, description) - Data in a specific schema for downstream processing **Use query format only when:** - The page is extremely long and you need a single targeted answer without processing the full content - You want a quick factual answer and don't need to retain the page content **Usage Example (markdown format - default for most tasks):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com/article", "formats": ["markdown"], "onlyMainContent": true } } ``` **Usage Example (branding format - extract brand identity):** ```json { "name": "firecrawl_scrape", "arguments": { "url": "https://example.com", "formats": ["branding"] } } ``` **Branding format:** Extracts comprehensive brand identity (colors, fonts, typography, spacing, logo, UI components) for design analysis or style replication. **Performance:** Add maxAge parameter for 500% faster scrapes using cached data. **Returns:** JSON structured data, markdown, branding profile, or other formats as specified. **Safe Mode:** Read-only content extraction. Interactive actions (click, write, executeJavascript) are disabled for security.
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  • Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. Required for any framework analysis flow. The companion tool `recommend_framework` returns framework_ids that are valid here. Pre-discovered framework_ids (SWOT = "swot-analysis", PESTEL = "pestel-analysis", Porter's 5 Forces = "porters-five-forces", etc.) work without a recommendation step. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (slug) + user_inputs object. The user_inputs schema is per-framework; call `recommend_framework` first if you don't know the field set, OR send a `raw_prompt` as a free-text fallback. Example: { "framework_id": "swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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  • Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.
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  • Poll an async task by id. Poll every 5 seconds; processing may take up to 30 seconds to start. Returns `{ taskId, status, progress, result?, error? }`. `status` is one of `created`, `running`, `completed`, `failed`.
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  • Enqueue an async fork of a public Universe project into your workspace. Provide either a Universe `url` or `source_project`. Returns the platform's 202 payload verbatim — at minimum `{ taskId, url }`, where `url` points at the async-task polling endpoint. Poll completion with `async_tasks_get(task_id=taskId)` every 5 seconds until `status` is terminal (`completed` or `failed`). Processing may take up to 30 seconds to start.
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  • Get report status and metadata (without PDF). Returns status (pending/processing/completed/failed), title, type, inputs, and summary. This is the polling tool for ceevee_generate_report — call every 30 seconds, up to 40 times (20 min max). When status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay error_message. If still processing after 40 polls, stop and give the user the report_id to check later. Free.
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