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271,031 tools. Last updated 2026-07-08 01:05

"Slow thinking, distributed thinking, and reasoning abilities research" matching MCP tools:

  • Batch version of colour_passport. Submit up to 20 hex values in one call. Returns a full Colour Passport for each unique hex: colour science, archive anchor, evidence grade, do_not_say constraints, hex provenance, accessibility, and physics. Deduplicates hex values automatically. Use for multi-colour workflows, Figma palette analysis, or any case where calling colour_passport separately for each colour would be slow.
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  • Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).
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  • Tell support something went wrong, file a bug, request a feature, or flag a slow query. Zero LLM cost, zero credits. Call this when: - A `query_data` result looks wrong or misleading (pass the `query_id` from that response — support uses it to investigate the issue). - You hit a confusing error or the same query keeps failing. - A query took unreasonably long (still pass `query_id` if available). - You wish mrmarket.ai supported something it currently doesn't. This is the PREFERRED support channel because it auto-links to the query trace; email is slower and requires the user to copy/paste the query_id manually. Categories: - wrong_data → result was incorrect / numbers look off - bug → something crashed or the response was malformed - slow → query took too long or timed out - feature_request → "I wish it could do X" - general → catch-all if none of the above fits
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  • ONE-CALL attested company/crypto deep research. Pass ?q=<company, domain, or topic> (and optional ?domain=, ?num=, ?receipt=1). LION runs web search -> scrapes the top source -> firmographics enrich (Wikidata + SEC) -> domain trust, and merges them into one Ed25519-attested JSON — replacing StableEnrich's 3-4 call research loop (~$0.08) with a single $0.012 call (~85% cheaper). For company research, vendor due diligence, business intelligence, SEC financials, and crypto/token research. Keyless, no account, no PII. For people/email/LinkedIn/maps use stableenrich.dev — LION proves companies. Volume: ?volume=100 -> $0.010, ?volume=1000 -> $0.008. [x402 paid tool: GET /api/x402/deep-research-json?src=mcp returns the 402 challenge with the canonical payTo; price 0.012 USDC on Base eip155:8453.]
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  • Batch version of colour_passport. Submit up to 20 hex values in one call. Returns a full Colour Passport for each unique hex: colour science, archive anchor, evidence grade, do_not_say constraints, hex provenance, accessibility, and physics. Deduplicates hex values automatically. Use for multi-colour workflows, Figma palette analysis, or any case where calling colour_passport separately for each colour would be slow.
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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…

  • AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.

  • Tell support something went wrong, file a bug, request a feature, or flag a slow query. Zero LLM cost, zero credits. Call this when: - A `query_data` result looks wrong or misleading (pass the `query_id` from that response — support uses it to investigate the issue). - You hit a confusing error or the same query keeps failing. - A query took unreasonably long (still pass `query_id` if available). - You wish mrmarket.ai supported something it currently doesn't. This is the PREFERRED support channel because it auto-links to the query trace; email is slower and requires the user to copy/paste the query_id manually. Categories: - wrong_data → result was incorrect / numbers look off - bug → something crashed or the response was malformed - slow → query took too long or timed out - feature_request → "I wish it could do X" - general → catch-all if none of the above fits
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  • Find your worst queries by TOTAL time — no connection needed. Paste a MySQL slow query log or a PostgreSQL pg_stat_statements export and get a ranked top-N: each query shape with calls, total/mean time, and (slow log) the rows-examined-to-sent ratio, fingerprinted so thousands of log lines collapse into a few classes. Flags the dominant query, N+1 patterns, and full-scan ratios, reports how concentrated the load is (what share of total time the top shapes own), and hands the worst offenders to sixta_analyze_query. Call this whenever the user shares a slow query log or pg_stat_statements export — even a long one — or asks which queries are slowest: summing time across thousands of log lines is arithmetic a model cannot do reliably by eye. Input is analyzed in memory and never stored.
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  • Multi-call reasoning scaffold for AI coding agents — NOT Anthropic's single-call think tool, NOT extended thinking. Tracks hypotheses, observations, conclusions, and assumptions across iterative tool-call chains. Detects circular debugging, repeated failed approaches, and dangerous operations. Returns: shouldContinue, riskLevel (high/critical blocks continuation), repetitionWarning, reflectionPrompt (recovery questions on loop), boredLoopDetected (same tool called twice), approachingLimit (2 thoughts before cap). Call when: (1) high-blast-radius edit — schema, auth, billing, multi-file refactor, production deploy. (2) Debugging after 2+ failed attempts. (3) Task spans 3+ files. (4) Ambiguous requirements — surface assumptions first. DO NOT call when: (1) you already know the answer — act. (2) Single-step task — rename, typo, file read. (3) You're calling again without new evidence — that's a loop, stop. (4) Session is closed (nextThoughtNeeded:false was set). Pass lastActions (last 2-5 tool calls) to enable boredom detection. Set actionReady:true to exit early when planning is done. Set nextThoughtNeeded:false to close the session and write a Supabase checkpoint. Pass sessionId to resume — previously rejected approaches are injected so you don't repeat them. Hard cap: 10 thoughts per session.
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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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  • Generate a personalized cold email sequence for ONE lead. This is SYNCHRONOUS — the request takes 3-10 minutes because MachFive researches the prospect and crafts unique emails. Do NOT retry if it seems slow; wait for the response. You must have a campaign_id first. Call list_campaigns if you don't have one. If the request times out, use the returned list_id with get_list_status and export_list to recover results.
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  • Read-only. Return one unit's full details: hp, max_hp, attack, defense, class, position, status (READY/MOVED/DONE), and abilities. Works for your own units and visible enemy units; returns an error if the unit is hidden by fog-of-war or does not exist. unit_id is the string identifier shown in get_state output (e.g. 'blue_archer_1'). Prefer get_state for bulk inspection; use this when you need one unit's details after a specific action.
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  • Fetch the full execution detail for a single trace — tool executions, events timeline, LLM call spans (with error_message on failures). Use after `agents.traces_list` identifies a specific trace of interest (failed run, slow run, unexpected outcome). By default LLM `system_prompt` and `prompt_messages` are stripped — set `include_llm_bodies=true` to fetch them when diagnosing prompt engineering issues (emits a WARNING audit log). Set `full=true` to disable all field truncation. `completion_text` on failed LLM calls is always returned (capped at 8 KB).
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  • Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources. Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers. Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant. NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search.
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  • Get a fully denormalized Pokémon dossier in a single call — base stats, types, abilities (with full English effect text), height/weight, resolved evolution chain, sprite URLs including official artwork, species flavor text, variety list, capture rate, growth rate, gender rate, legendary/mythical flags, egg groups, and (optionally) a summarized learnable-move list. Accepts a name (lowercase, hyphens for spaces, e.g. "bulbasaur", "mr-mime") or Pokédex number. Set include_moves=true to include the move summary (large); defaults to false. Use game_version to select flavor text from a specific game (e.g. "sword", "red"); falls back to the most recent English entry when the version is not found. Use pokeapi_find_pokemon to discover Pokémon by type, generation, or egg group before calling this tool.
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  • Submit a public product URL for price tracking. Waits up to ~25s server-side; fast shops return status "completed" with product in one call. Slow jobs return status "running" with job_id — poll get_job_status. On failure, returns a structured error object with fields error.code, error.message, error.http_status, error.retry_recommended, and error.retry_after_seconds.
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  • Describe one Sugra API endpoint by operation_id. Includes agent_hints (duration_class fast/slow/heavy, max_concurrency, bulk billing) so you can budget timeouts and parallelism before calling. POST endpoints with a JSON body also carry request_body_schema (the resolved JSON schema) - construct the `body` argument from it instead of guessing key names.
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  • Batch version of colour_passport. Submit up to 20 hex values in one call. Returns a full Colour Passport for each unique hex: colour science, archive anchor, evidence grade, do_not_say constraints, hex provenance, accessibility, and physics. Deduplicates hex values automatically. Use for multi-colour workflows, Figma palette analysis, or any case where calling colour_passport separately for each colour would be slow.
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  • Lists the full folder (mailbox) tree for Apple Mail (Mail.app) accounts, including nested subfolders. Use this to discover the exact folder names that move_email(target_mailbox=...) and list_emails(mailbox=...) expect. Outlook.com, Exchange, Gmail, iCloud and IMAP accounts added to Mail.app are all included. For a Graph-only Microsoft 365 mailbox not added to Mail.app, use m365_list_emails instead. Pass account=<name> (from list_email_accounts) to enumerate one account fully; without it, every account is walked which can be slow on macOS 15+. Message counts are off by default (slow on IMAP) — pass include_counts=true to add unread/total per folder.
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  • Return the connected OpenClip account: the user's id and email, the current team and plan, the number of processing credits remaining, and the granted scopes/abilities of the current connection (the OAuth scopes for the primary connection, or the personal access token abilities for a key-based connection). Use this first to confirm the connection works and the user has credits before submitting videos.
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