"How to break tasks into smaller steps" matching MCP tools:

• analyze_trades

  OneQAZ Trading Intelligence

  Purpose: Aggregate paper trades by day / pattern / symbol. Triggers (casual questions too): "how's the week been?", "이번 주 매매 성적 어때?", "which patterns are working?", "어떤 종목이 제일 잘 벌었어?", "break down the trades", "daily P&L summary?". When to call: pattern audits, period-over-period performance review. Prerequisites: get_trade_history recommended for raw rows first. Next steps: market://{market_id}/signals/feedback for the upstream signals. Caveats: max 30 days; empty result when no trades in the window. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) days: Analysis period in days (default 7, max 30) Disclaimer: Information only, not investment advice.

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• workflow_orchestrator

  gapup-mcp

  Meta-tool that CHAINS multiple MCP tools sequentially into a named workflow — delivering a composite output in a single call. 10 predefined workflows: compliance_full_audit (6 steps: KYC+sanctions+AI_gov+privacy+ESRS+CSRD), deal_due_diligence (7 steps: deep_dive+registry+court+patents+KYC+financials+M&A), market_entry_brief (6 steps: country_study+regulations+procurement+tax+AGOA+market_brief), competitor_intelligence_pack (5 steps: deep_dive+intel+patents+earnings+pitch_deck), esg_360 (5 steps: ESG_audit+carbon+CSRD+ESRS+supplier_esg), ip_freedom_to_operate (4 steps: patent_search+async_deep+IP_audit+competitive), climate_property_assessment (3 steps: climate_risk+real_estate+geo), pharma_target_screen (4 steps: trials+adverse_events+patents+meta_analysis), sanctions_360 (5 steps: KYC+Russian_sec+registry+crypto_wallet+court_filings), talent_market_brief (4 steps: salary+trends+adjacent_roles+skills_taxonomy). Returns steps_executed, consolidated P0/P1/P2 signals, overall_status, estimated_cost_usd, and raw outputs per step. Cache: 1h LRU per (workflow, target). Budget: 60s global timeout → partial if exceeded. Use when an agent needs a composite liverable without orchestrating tools manually.

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• get_resource_usage

  Dockhold

  Show your account's compute, database-RAM, and storage pools: how much you've bought, how much is used, and how much is free, plus every app's current size. Call this before any resize tool (the allowed sizes come from its steps fields), and to explain to the user why an app ran out of memory or a deploy was refused for capacity.

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• find_known_issues

  canton-ccpedia

  Surface known UNRESOLVED problems matching a free-text description: forum threads with zero replies but high views, plus open GitHub issues. Answers "is anyone else hitting this?". Canton-specific. Does NOT return fixes, solutions, config, or how-to steps, and returns nothing when no open issue matches; for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.

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• preview_qr_encoding

  qart.uk QR studio

  Work out how a payload will encode as a QR code, without creating anything. Returns the exact string the pixels would carry, its byte length, the QR version and module count required, the capacity remaining at that version, and any warnings. Costs nothing and mints nothing. Use this to answer questions like "will this URL fit at error correction level H" or "how much does a long Wi-Fi password cost me in QR density". Higher ECC survives damage better but needs more modules for the same data; more modules print smaller and get harder to scan. payloadKind 'qart_code' sizes the RETARGETABLE variant instead: the pixels carry a 16-byte qart.uk/_ link rather than your data, which is usually several versions smaller. Compare the two before committing to a print. This returns metadata only, never the module grid. For the actual black/white matrix, call get_qr_matrix.

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• create_workflow

  WebRun Browser Automation

  Create a new saved workflow. READINESS BAR: if you could not execute the prompt yourself as a human in a browser — which site, which records, which fields, delivered where, matched to each recipient how — it is not ready; first collect the missing business facts from the user (see the TASK INTERVIEW in the server instructions), then create. Requires title and promptTemplate — write the prompt as a browsing runbook following the Goal / Ground rules / Stages / Output contract in the promptTemplate field description. For tasks that must handle each new item exactly once (new messages, forward once, skip seen), set workListEnabled + listType 'monitor' AND author memoryContract {mode:'dedupe', key, emit} — the platform injects the tracking; never write memory steps into the prompt. Created in the connection's environment automatically — omit environmentId and never ask the user to choose one. Write the user's concrete values (names, URLs, numbers) directly into the prompt; use {{variables}} ONLY when the user explicitly wants a reusable template with per-run inputs. Everything else is optional and defaults sensibly — do not quiz the user about settings. A supplied schedule is stored as a setting only — the workflow does not run on a timer until deployed via create_agent; use trigger_workflow to run it now.

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• submit_workflow

  Botverse

  Submit a multi-step workflow to the Botverse workflow engine. Steps execute in dependency order; parallel branches (multiple steps with the same depends_on) run simultaneously. Returns a workflow_id immediately — poll get_workflow_status every 5–10 seconds until terminal. INTER-STEP REFERENCES: pass a prior step's output into a later step with the string "$.steps.<step_id>.output_key" (e.g. a docx→pdf chain: step to_pdf has depends_on: ["to_docx"] and inputs {"source_url": "$.steps.to_docx.output_key", "input_format": "docx", "output_format": "pdf"} using tool convert_from_url). Workflow params are referenced as "$.params.<name>". No other template syntax (${...} etc.) is supported. BILLING: convert-only workflows run on wallet balance ($0.05/step). Workflows containing transcode or transcribe steps require auto-refill to be enabled at botverse.cloud/dashboard/billing (their cost scales with source duration). Workflow definition uses BWDL (Botverse Workflow Definition Language) — schema at botverse.cloud/schemas/workflow/v1.json.

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• plan_and_execute

  ALM X++ MCP Server

  CHAIN-OF-TOOLS PLANNER -- Execute a multi-step JSON plan in ONE call by chaining tools. Pass an EXPLICIT JSON array of steps (NL parsing disabled to prevent ambiguity). Maximum 5 steps per plan. The chain stops on any critical failure (ado_analyze_workitem, get_object_details). ## Required Format (JSON array) `[{"tool":"get_object_details","args":{"objectName":"SalesTable"}},{"tool":"validate_best_practices","args":{"objectName":"SalesTable"}}]` ## When to use - You already know the exact sequence of 2-5 tool calls needed - The output of step N is independent of step N+1 (or matches the carried context: analysis_output) - You want SSE progress events between steps ## When NOT to use - Single tool call (use the tool directly) - Plan depends on the user's intermediate decision (call tools one at a time) - More than 5 steps (split into separate calls) ## Cost: $0 -- pure in-process execution.

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• simulation.step

  Cloud World Model

  Advance a demo simulation by one time step and return updated metrics — CPU, latency, throughput, error rate, cost (max 20 steps per session). Use it to observe how the architecture behaves over time, typically right after simulation.create or simulation.inject_traffic. Do not use it to read current state without advancing time — that is simulation.metrics. simulationId is optional: omit it to step this session's current simulation (the one from simulation.create or the last explicit successful use). The likely next tool is simulation.step again (to keep observing) or simulation.inject_traffic (to change load first). Responses are compact by default: principal metrics plus per-resource status (id, name, status, cpuPercent, routedRps) and this step's events. Pass responseMode: 'full' to get the complete simulation state instead. Authenticate with an API key for unlimited steps.

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• gdelt_get_coverage_breakdown

  gdelt-mcp-server

  Break down news coverage volume over time by source language or source country, returning a multi-series time series (one series per language or country). Shows which countries or languages drove early vs. late coverage — useful for tracing how a story propagated geographically or across language communities. Returns up to 10 series by total volume and aggregates the rest into an "Other" bucket, naming every series it folded in there under otherSeriesLabels — pass any of those labels back as the series input to get that series complete, ranked or not. Values are normalized: each point is the topic's share of media output, not an absolute article count. Small media markets with concentrated coverage therefore rank above large markets with diverse output — a high value means the topic dominated that source's coverage, not that it published the most articles. Use breakdownBy "country" with the signal-detection chain to map geographic attention, or "language" to detect non-English media surges.

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• list_scheduled_tasks

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Paginated newest-first listing of the caller's own scheduled (deferred) tasks — transparency into what an agent has queued for the future. Filter by `status` (pending/completed/cancelled/cancelled_owner_inactive/all). Tier: sp500+ (sample rejected).

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• get_ai_development_loop_info

  Fractera

  Explains the Fractera development loop: how one admin request becomes tested, deployed, recorded code with no human writing it — Hermes orchestrates and loads its identity + project context, picks a ready coding agent (Claude Code, Codex, Gemini, Qwen, Kimi), the agent is enriched (SOUL.md / AGENTS.md / GLOSSARY.md / completed steps), generates a task then code, it is built and deployed, and the result branches (error feeds back; success updates the completed steps and the deployments tab) — all grounded by LightRAG memory at every step. RETURNS A DIAGRAM IMAGE URL you can show the user when they ask how Fractera builds software or how its agents work. Call with NO arguments to get the diagram URL + the "how the loop works" overview + the section list; call again with a single `section` id to read one stage in depth.

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• create_tasks

  Plate

  Create multiple tasks in a project in one action. Use this instead of calling create_task multiple times when the user asks to create several tasks at once. All tasks are created atomically — if validation fails for any item, nothing is created.

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• add_contacts_to_campaign

  LinkedIn MCP Server (Salesbot)

  Add selected contacts (by ID) to an existing campaign. Blacklist of domains/companies, duplicate checks and manual exclusion are applied automatically. Campaign steps (visit → connect → message) are then scheduled per template and daily limits. Returns how many were added vs filtered.

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• cc.twap_executor

  Central Command

  Call cc.twap_executor — Time-Weighted Average Price execution engine — splits large orders into smaller randomized slices over configurable time windows to minimize market impact. Purpose: Time-Weighted Average Price execution engine — splits large orders into smaller randomized slices over configurable time windows to minimize market impact. Behavior: DESTRUCTIVE. Splits and submits real exchange orders over time. Irreversible fills once slices execute. Not a simulation. Auth: X-Api-Key required (and linked exchange credentials for execution actions). Cost: $0.015 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 10/min (per API key). Tier: premium. Returns: Execution status: filled slices, average fill price, remaining quantity, estimated completion time. Guidelines: Prefer paper/simulation paths. For live money require explicit human confirmation (confirm_live / action=execute). Report real HTTP errors; never invent proxy failures. Tags: execution, twap, smart-order, market-impact, slippage.

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• robyn_mandate_check

  Robyn — Gasless Cross-Chain for AI Agents

  Check a proposed spend against a signed spending mandate BEFORE making it. Returns WITHIN_MANDATE, EXCEEDS_MANDATE or EXPIRED plus the remaining budget. If EXCEEDS_MANDATE, do NOT split the spend into smaller pieces to get under the limit — that defeats the control your principal set. Ask them to raise the budget. NOTE THE SCOPE: a mandate covers spending THROUGH ROBYN only. It is not custody and does not stop spending elsewhere with the same key.

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• estimate_claims

  Fidenta Verify

  FREE. Break text into checkable claims and report how many there are and how many credits verifying them would cost. Spends nothing and verifies nothing. Always call this before verify_claims on anything longer than a sentence or two — an article can contain 20 claims, and each claim costs one credit, so the user should see the price before agreeing to it.

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• get_signal_detail

  OneQAZ Trading Intelligence

  Purpose: Per-symbol signal deep-dive — latest signal + history + feedback. Triggers (casual questions too): "why is BTC a buy?", "그 시그널 근거가 뭐야?", "signal history for AAPL?", "이 종목 시그널 자세히 보여줘", "how has this signal performed before?". When to call: drilling into a single ticker's signal context. Prerequisites: confirm existence via get_signals first. Next steps: get_role_analysis, get_position_detail. Caveats: queries both the per-symbol signal store and the paper-trading store. Disclaimer: Information only, not investment advice. Args: market_id: Market ID (crypto, kr_stock, us_stock) symbol: Asset identifier (preferred; e.g., BTC, AAPL) coin: Legacy alias of symbol (kept for backward compatibility) interval: Timeframe (default: combined)

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• workflow_orchestrator

  gapup-mcp

  Meta-tool that CHAINS multiple MCP tools sequentially into a named workflow — delivering a composite output in a single call. 10 predefined workflows: compliance_full_audit (6 steps: KYC+sanctions+AI_gov+privacy+ESRS+CSRD), deal_due_diligence (7 steps: deep_dive+registry+court+patents+KYC+financials+M&A), market_entry_brief (6 steps: country_study+regulations+procurement+tax+AGOA+market_brief), competitor_intelligence_pack (5 steps: deep_dive+intel+patents+earnings+pitch_deck), esg_360 (5 steps: ESG_audit+carbon+CSRD+ESRS+supplier_esg), ip_freedom_to_operate (4 steps: patent_search+async_deep+IP_audit+competitive), climate_property_assessment (3 steps: climate_risk+real_estate+geo), pharma_target_screen (4 steps: trials+adverse_events+patents+meta_analysis), sanctions_360 (5 steps: KYC+Russian_sec+registry+crypto_wallet+court_filings), talent_market_brief (4 steps: salary+trends+adjacent_roles+skills_taxonomy). Returns steps_executed, consolidated P0/P1/P2 signals, overall_status, estimated_cost_usd, and raw outputs per step. Cache: 1h LRU per (workflow, target). Budget: 60s global timeout → partial if exceeded. Use when an agent needs a composite liverable without orchestrating tools manually.

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• get_orientation

  The Latent Space

  START HERE on a first visit. One call returns everything a new agent needs: what The Latent Space is, which rooms are open and how busy each is, total registered agents, three suggested first actions, and key endpoints. Pass your agent_name if already registered to include your profile and tailored next steps. No authentication required.

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