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

"Improved thinking frameworks and methodologies beyond sequential thinking" matching MCP tools:

  • Create a new always-on database owned by the Pro account. Beyond the included allotment this costs extra per month — the tool then returns confirmation_required with the exact price; relay it to the user and only retry with accept_overage_usd after their explicit approval.
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  • Discovery meta-tool. Lists ALL available Nordic Data API data endpoints (HTTP method, path, short description) by reading the backend's live OpenAPI spec at runtime — far beyond the curated high-level tools. Use this to discover capabilities the dedicated tools do not cover, then call get_endpoint_schema for parameter details and call_endpoint to execute one. Admin endpoints are never returned. Supports an optional `search` keyword filter. The catalog has 230+ endpoints.
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  • Update a forked agent's instructions (prompt) to the latest version of the system template it was created from. Use when the platform has improved a template and the user wants their forked agent to pick up the new prompt. This OVERWRITES the agent's prompt_text with the template's current prompt — any customizations to the prompt are replaced (recoverable via prompt history). Tool/model/execution settings are NOT changed. Only works on agents forked from a template (not from-scratch agents or templates themselves).
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  • Return the latest competitor SEO snapshot for the site (FD-041): which keywords each tracked competitor DOMAIN ranks for on Google (Japan/ja), at what position, with monthly search_volume, cpc and etv (estimated monthly traffic — a visit estimate, not a monetary value), plus how each rank moved vs the previous snapshot. READ-ONLY — this tool never runs a research (that costs money and is triggered separately from the dashboard, the competitor-research Edge Function); it only reads what was already fetched. The response is summary-first (token-aware): each domain carries a constant-size `summary` (total_keywords, total_etv, volume_bands and rank_bands histograms, and vs_previous new/lost/improved/declined/same counts) that always reflects the FULL keyword set, while `keywords` returns only the top rows ranked by `sort` (etv default | volume | rank; default limit 10 per domain, max 100) with a `truncated` block (shown/matching_total/lost_total). rank is a POSITION: smaller is better, so a NEGATIVE rank_delta means the competitor's ranking IMPROVED (change ∈ new/improved/declined/same/unknown). Keywords the competitor ranked for before but lost are disclosed in `lost_keywords` (top 10 by previous etv), never dropped silently. Pass `domain` to focus one competitor, `min_volume` to drop low-volume keywords. When the site has NO completed research yet the response is { researched:false } with a `guidance` string explaining a research must be triggered from the dashboard first — this tool cannot start one. site_id is OPTIONAL when OAuth-authenticated. This is the external competitor lens (third-party SERP data); for YOUR OWN search performance use get_keyword_performance, and for your content playbook use get_content_actions.
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  • Run Core-OCR extraction on a previously uploaded file slot. The ``file_id`` comes from a prior ``files_create_upload`` call (upload the bytes to the returned signed URL first; each slot is single-use). Choose one or more ``extraction_types``: 'ocr' (full text), 'invoice_headers', 'invoice_line_items', 'document_details_hebrew', 'document_line_items_hebrew', or 'custom_template' (requires a READY ``template_id``). Extraction is asynchronous: the call returns within seconds with either the completed result (fast documents) or an IN_PROCESS status carrying ``meta.correlationId`` — poll ``get_extraction_result`` with that id until complete. Duplicate protection is opt-in: pass an ``external_id`` of your own to enable it (re-submitting the same external_id within the dedup window idempotently returns the prior result, or errors if the file or extraction types differ; ``allow_duplicate=true`` overrides). Without an external_id every extraction is billed as new. Optional advanced knobs mirror the REST API: ``model_type`` selects the extraction model, and ``thinking`` / ``evaluation`` / ``correction`` / ``include_coordinates`` toggle accuracy passes and coordinate output.
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  • Capture a Texas homeowner's interest in rooftop solar and route to a licensed installer — use when the user owns (or is buying) a Texas home and mentions solar panels, solar quotes, solar savings, or reducing their bill through solar. Use when the user says 'I just bought a house in Austin and want solar quotes', 'how much could solar save on my Houston electric bill', or 'connect me with a solar installer for my new home'. Returns a lead ID and confirms next steps; Utilify routes the lead to installer partners (SunPower, Sunrun, Palmetto, and independent TX installers). Caveats: (1) only call when the user has explicitly opted in and confirmed homeownership — this is not for renters, and Utilify may earn a referral fee. (2) Texas-only — for non-TX addresses, decline and explain. (3) Don't double-call for the same address in one conversation; one lead per opt-in. If the user has only expressed mild curiosity ('I'm thinking about solar someday'), answer the question first and only call this tool once they confirm 'yes, connect me'.
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  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • Claims-based knowledge base for no/low ABV specialty beverages (producers, beverages, people).

  • Get build and runtime logs for a deployment. If no deployment_id is provided, returns logs for the latest deployment. Use this after calling deploy to monitor build progress and diagnose failures. Logs include: framework detection output, dependency installation, build steps, container startup, and health check results. If a deployment fails, check the logs for error details — common issues include missing dependencies, build errors, or the app not listening on the correct PORT (check the PORT env var — 8080 for auto-detected frameworks, or the EXPOSE value from Dockerfile).
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  • CALL FORMAT: zambot_swarm_debate({goal: 'your strategic question or decision'}). Required param is `goal` (not topic, not question — exactly `goal`). Example: zambot_swarm_debate({goal: 'scale my SaaS from $10K to $100K MRR in 90 days'}). Runs a 5-domain strategic debate — simultaneously analyzed by Evolutionary Biology, Game Theory, Military Strategy, Behavioral Economics, and Systems Complexity. Returns 5 distinct framings + a cross-domain consensus and dominant domain. No two domains ever agree on the same approach — this surfaces blindspots and asymmetric advantages invisible to single-domain thinking. 3 free debates/day. Unlimited with Zambo Pass. Use when: exploring a hard strategic decision, diagnosing why a plan keeps failing, or before committing to a major direction.
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  • Structure multi-step debugging and planning across tool calls — not a one-shot think. Tracks hypotheses, observations, plans; detects loops via lastActions; riskLevel high/critical blocks dangerous edits (drop table, prod deploy). Loads projectBrief (stack, key_paths, project_memory recall) on local project. On close, suggestedRemember → call project_memory remember. 4 credits hosted. Hard cap 10 thoughts/session. Call when: stuck after 2+ failed debug attempts, auth/billing/schema change spans 3+ files, flaky test you cannot explain, or you need a plan before editing. Pass lastActions (2–5 recent tool calls), goalAnchor after thought 2, sessionId to resume, area for subsystem. NOT when fix is known, single typo, repeating without new evidence, or session ended (nextThoughtNeeded:false). Read thoughtConfirmed and shouldContinue first. Legacy alias: thinking. Example: keep_thinking({ thought: 'Hypothesis: refresh token not rotated in middleware', thoughtType: 'hypothesis', thoughtNumber: 1, totalThoughts: 5, nextThoughtNeeded: true, confidence: 0.6, goalAnchor: 'Fix auth logout loop', lastActions: ['find_code(query=refreshToken)', 'read_code(target=authMiddleware)'], area: 'auth' }). Read-only.
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  • List members (Relay connection): each edge has a cursor + node (profile, Stripe customer id, unrestricted-access flag, total spend, and their subscriptions). Paginate with `first` + `after` (pass the last edge's `cursor`); `first` defaults to 50. Set `activeOnly` to return only members with an ACTIVE membership state. For member-state filters beyond active, use memberful_query. Memberful GraphQL: members connection.
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  • Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January
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  • Detect website technology stack: CMS, frameworks, CDN, analytics tools, web servers, languages (via HTTP headers + HTML analysis). Use for passive reconnaissance; for full audit use audit_domain. Free: 30/hr, Pro: 500/hr. Returns {technologies: [{name, category, confidence%, version}]}.
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  • Search academic papers, books, and datasets via Crossref. For "search works titled X" / finding a SPECIFIC paper by its title, pass `title` (title-scoped — ranks the exact paper first). Beyond keyword search you can SORT by citation count or publication date and FILTER by date range, work type, and author — e.g. "most-cited papers on transformers" (sort=citations), "papers on LLMs since 2024" (from_date=2024-01-01), "recent journal articles by an author". Returns titles, authors, journal, DOIs, citation counts, and dates.
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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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  • Get detailed gateway status including treasury address, subsystem latencies, and agent count. Use for deeper diagnostics beyond basic health checks. FREE — rate-limited only. [pricing: {"cost":"0","currency":"FREE","type":"free","network":"eip155:8453"}]
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  • Score a TikTok, Reels, or Shorts hook against proven viral patterns. Returns a 0-100 score, percentile rank, matched pattern, strengths, weaknesses, and three improved hook variations. Use when the user has a draft hook to validate, wants to compare alternatives, or needs feedback before publishing.
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  • P87 — list the specialist agents ChiefLab can delegate to (design / video / research / outreach / seo / analytics). USE WHEN the user asks 'what can ChiefLab do beyond launch posts?' or before calling chieflab_request_specialist. Returns the kind + label for each so the caller can pick the right one.
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  • P87 — list the specialist agents ChiefLab can delegate to (design / video / research / outreach / seo / analytics). USE WHEN the user asks 'what can ChiefLab do beyond launch posts?' or before calling chieflab_request_specialist. Returns the kind + label for each so the caller can pick the right one.
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  • Get Lenny Zeltser's Malware frameworks (primary frameworks the brief structurally derives from) plus optional sibling frames (adjacent frameworks that aren't the structural backbone). Pass `include_siblings: false` to skip sibling blocks. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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  • Update a forked agent's instructions (prompt) to the latest version of the system template it was created from. Use when the platform has improved a template and the user wants their forked agent to pick up the new prompt. This OVERWRITES the agent's prompt_text with the template's current prompt — any customizations to the prompt are replaced (recoverable via prompt history). Tool/model/execution settings are NOT changed. Only works on agents forked from a template (not from-scratch agents or templates themselves).
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