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305,560 tools. Last updated 2026-07-23 07:10

"Getting the Most Out of Jina Framework" matching MCP tools:

  • Get one saved visual ideas preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • Retrieves authoritative documentation directly from the framework's official repository. ## When to Use **Called during i18n_checklist Steps 1-13.** The checklist tool coordinates when you need framework documentation. Each step will tell you if you need to fetch docs and which sections to read. If you're implementing i18n: Let the checklist guide you. Don't call this independently ## Why This Matters Your training data is a snapshot. Framework APIs evolve. The fetched documentation reflects the current state of the framework the user is actually running. Following official docs ensures you're working with the framework, not against it. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" to see available sections 2. **Reading** - Call with action="read" and section_id to get full content **Parameters:** - framework: Use the exact value from get_project_context output - version: Use "latest" unless you need version-specific docs - action: "index" or "read" - section_id: Required for action="read", format "fileIndex:headingIndex" (from index) **Example Flow:** ``` // See what's available get_framework_docs(framework="nextjs-app-router", action="index") // Read specific section get_framework_docs(framework="nextjs-app-router", action="read", section_id="0:2") ``` ## What You Get - **Index**: Table of contents with section IDs - **Read**: Full section with explanations and code examples Use these patterns directly in your implementation.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Fetch statistically-validated leading/lagging relationships for an indicator. Source: the five-filter leading-indicator scanner (cross-correlation → first-differenced CCF → multi-crisis validation → Granger causality → out-of-sample validation). Returns two lists: `as_leader` (pairs where this indicator precedes its follower) and `as_follower` (pairs where another indicator precedes this one). Only fully-validated pairs are included — partial matches are not surfaced. Most of the 96 indicators return empty lists; currently six pairs clear the full gauntlet.
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  • Posts the acting user can see, ranked by ENGAGEMENT (likes + 2 × comments) within a time window. Use for 'what's hot this week?', 'top posts in my feed today', 'most-discussed since Monday'. Window is mandatory in spirit — without `since` you'll get all-time top, which is rarely what users want. Pass `since` = start-of-week for the canonical 'top of week' query. Blocked profiles filtered out.
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  • Get the cross-sectional ranking of stocks by institutional 13F breadth for a given quarter. Returns the stocks ranked by number of 13F filers reporting them as a holding (default), by quarter-over-quarter change in filer count (warming names — 'filersDelta' — or cooling names — 'filersDeltaAsc'), or by total reported dollar value. Includes Δ filers vs the prior quarter, total value, Δ value, and the stock's share of the 13F universe. Only currently-held stocks rank; fully-sold-out names live in GetMarketWide13FActivity's sold-out-positions bucket. While the newest quarter's filing window is open, funds that have not filed yet are carried at their prior-quarter positions (noted in the output). Use this to answer 'which stocks are most owned by institutions right now, and is breadth expanding or contracting?'
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Matching MCP Servers

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    An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.
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    Apache 2.0

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  • Jina AI Reader/Search MCP — turn any URL into clean LLM-ready markdown, plus web search.

  • Core Web Vitals metrics by CMS, CDN, and framework — free remote MCP, no auth.

  • Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.
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  • Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }
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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. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. 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 (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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  • IMPORTANT: Do NOT fetch all guidances at once. Fetch the 'Backend Installation' guidance first, apply the necessary setup changes, and then fetch subsequent guidances (e.g., 'Redirect users after login', 'Backend Auth Middleware') sequentially as you implement each specific feature. Returns instructions for integrating PropelAuth via OAuth. Only use this tool when specifically instructed to by another tool or the user or if a PropelAuth SDK does not exist for the project's framework. Guidance includes instructions for the backend and frontend, including installation and configuration, creating access tokens, retrieving user or org information, logging users out, redirecting users to login, and more. It is important to follow the instructions carefully to ensure a successful integration.
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  • Install an app on the Android device from an APK or split bundle (XAPK/APKS — how most large modern apps ship). Provide apk_url (an https link the device downloads itself — preferred, and the only workable option for real apps) or, for tiny payloads, as apk_base64. Max 300MB. Large apps can take several minutes; if this call times out the install keeps running on the device — verify with the app-list tool instead of re-installing. Use reinstall=true to uninstall then install (for signature changes).
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  • Install an app on the Android device from an APK or split bundle (XAPK/APKS — how most large modern apps ship). Provide apk_url (an https link the device downloads itself — preferred, and the only workable option for real apps) or, for tiny payloads, as apk_base64. Max 300MB. Large apps can take several minutes; if this call times out the install keeps running on the device — verify with the app-list tool instead of re-installing. Use reinstall=true to uninstall then install (for signature changes).
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  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
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  • Get one creative agent skills preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.
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  • Returns the current security grade (A–F), last-scan timestamp, and list of active issues for a domain that is ALREADY under SiteGuardian monitoring by the authenticated account. Each issue carries a stable id, a severity, a short title, and an impact description. The response also includes a relative dashboard URL. Use this when the user asks about the current state of a specific monitored domain, wants to confirm a recent change landed, or needs issue ids to call get_fix_recommendations with a specific issue_id. Do NOT use this for domains not yet under monitoring — it will return a domain_not_monitored error; call scan_domain for one-off checks instead. Compliance framework tags (NIS2 / GDPR / DORA) are NOT included in v1; framework tagging on the monitored-domain path is tracked as a follow-up. Requires a valid API key.
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  • Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.
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  • Get AI Defense Matrix cross-mappings to nine external frameworks: NIST IR 8596, CSA AI Controls Matrix, ISO 42001, Google SAIF, SANS Critical AI Security Guidelines, MITRE ATLAS, OWASP AI Exchange, OWASP LLM Top 10, OWASP Agentic Security Top 10. Each row maps an AI asset class to how that framework applies. Each returned framework also carries a 'concepts' array of the structured IDs (MITRE ATLAS techniques, OWASP risks, ISO clauses) the matrix references for it. Supports a 'buyer' archetype shortcut to scope to the frameworks a particular buyer will care about. Use to translate between framework vocabularies. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • Search for solutions other agents already discovered. STOP and search before acting on any error — even mid-task. Do not chain fixes without searching each one. When: Any error, compile failure, stack trace, or unexpected output. Before config, integration, or setup tasks. When a fix attempt just failed — search before trying the next one. Unfamiliar library or framework combination. Do not skip because the fix seems obvious — obvious fixes are the most common and most likely to already be solved. How: Paste the exact error message, not your goal. Include framework or language name. Read failedApproaches first to skip dead ends. Feedback: Include previousSearchFeedback to rate a result from your last search — this refunds your search credit and costs nothing extra.
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  • Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".
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