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"A server for finding information about step-by-step thinking and slow thinking methodologies" matching MCP tools:
- 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.Connector
- Transform a payload string through one or more encoding layers for bypass research during authorized testing. Accepts a chain of encodings applied in order (e.g., ["unicode", "url", "base64"] applies Unicode → URL-encode → base64). Returns the transformed payload with a step-by-step decoding explanation: how a WAF or server would decode each layer, and why the combined encoding might bypass a specific filter. Use to understand filter bypass mechanics in an authorized engagement and to confirm that a target's decoding pipeline matches an expected bypass path. Payloads are transformed mathematically — no live probing occurs.Connector
- List every step-by-step setup recipe currently available for Massed Compute VMs. Prefer recipes_search when the user has a specific intent — call this only when the user asks open-ended questions like 'what can I set up on a VM?', 'what recipes do you have?', or wants to browse the catalogue. Returns metadata (slug, title, description, tags) only; call recipes_get for the full body.Connector
- Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.Connector
- Return step-by-step instructions for setting up x402 USDC autopay for this MCP server. Use this if a paid tool returned a 402 error or you're onboarding a new agent that needs to pay for API calls. Free.Connector
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- Alicense-qualityDmaintenanceA powerful MCP server that enhances LLMs with advanced sequential thinking capabilities, supporting 19 thinking modes for structured reasoning and complex cognitive tasks.Last updated15MIT
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Matching MCP Connectors
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Connect the OneStepTranscribe MCP server to your AI assistant and turn audio or video into text without leaving the chat. It is a remote server, so there is nothing to install, no API key, and no account. Just add one URL and ask your assistant to transcribe a file.
- Preview (and get send guidance for) a message to a Signal chat. NOTE: Signal Desktop exposes no local send API — the Signal integration reads the local database read-only — so LMCP cannot transmit Signal messages directly. The first call (confirm=false or omitted) returns a preview. Pass confirm=true to get step-by-step guidance for completing the send. The chat_id should come from a previous signal_list_chats call — never fabricate IDs.Connector
- Purchase Agentic Security Shield and receive all security configuration files. TWO-PHASE FLOW (you MUST do BOTH steps): STEP 1 — on-chain payment + token exchange: a) Send 19 USDC on Base network to the recipient address in /pricing or /.well-known/mcp/server-card.json (payTo field). b) POST /purchase (HTTP REST, not this MCP tool!) Header: x-payment-token: <on-chain transaction hash, 0x + 64 hex> Response: { "download_token": "dl_<uuid>", "files": {...} } STEP 2 — call this MCP tool with the dl_<uuid> token: purchase({ payment_token: "dl_<uuid>" }) The on-chain tx hash is single-use and only valid in STEP 1. After STEP 1 you have a 24-hour-valid dl_<uuid> download token usable in this MCP tool. Most agents will get the files inline from STEP 1's response and never need to call this MCP tool — it exists for clients that prefer MCP-native delivery.Connector
- Assess how a takedown for this URL would proceed: where the notice goes (host, platform, or a hidden host that must be revealed first), what documents and attestation the content owner must supply, the step-by-step process, and the legal caveats (§512(f), scope limits). Read-only; does not judge the merits of the claim and files nothing. Use resolve_host first if you only need the hosting answer.Connector
- Write a cover letter for a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's JD and the candidate's background, plus writing instructions. YOU (the model) then WRITE the cover letter (250–350 words, specific to the role, mapping the candidate's real achievements to the JD — never fabricate). STEP 2: call this tool again with action:'save', cover_letter_text:<your letter>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user asks for a cover letter for a specific job. Resolving job_id (same rules as tailor_resume_tool / job_detail_tool): pass the **Job Id** value from the most recent prior search/refine result VERBATIM; no placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / json_resume / user_profile.Connector
- Returns the complete setup and usage guide for SwapWizard. Call this FIRST before using any other tool. Covers: required configuration (API key, Alchemy RPC URL, private key), how to use poolId correctly, step-by-step operational flows for swap/zap in/zap out/analyze, transaction execution details, and approval rules.Connector
- Sign up for a brand-new sota.io account from inside Claude — no browser, no copy-paste. Two-step flow: STEP 1: Call with just `email`. We send a 6-digit confirmation code to that email. STEP 2: Call again with `email` + `code`. We verify, create the account on the Free tier (3 projects, EU-hosted, no credit card), generate a sota.io API key, and return it to you. After Step 2 you'll get back a key like `sota_…`. **Save it in a safe place** — you'll need it for any subsequent sota.io tool call in Claude (or you can use it with the sota CLI). It is shown ONCE and never recoverable. sota.io is an EU-native PaaS hosted in Germany — GDPR-compliant by default, no CLOUD Act exposure. Disposable / throwaway email addresses are not accepted; use a real address.Connector
- 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.Connector
- Secondary path — call AFTER bsp(agent_id='pscale', block='whetstone') if you want a guided six-step orientation walk, or if you are stuck. Returns the iterative orientation progression — a purpose spindle from wake (whetstone) through shared-context coordination. Each step is a concrete action with a validation criterion and a pointer to the next. Optionally takes a step parameter (1..6) to fetch a specific step; omit to receive step 1 with the whole-progression overview. NOT the recommended first call — the primary activation is reading whetstone via bsp(); pscale_invite serves agents who have read whetstone and want a structured walk through subsequent levels.Connector
- Program the GTM scheduler — durable, multi-step jobs that run on a thin server tick even when no agent is connected (multi-day workflows, standing watches, refreshes). action='schedule' creates one: { name, steps:[...], max_cost_cents?, related_segment_id?, related_lead_id?, start_at? }. Each step is either { type:'service', service, action, params, max_price_cents? } (a paid/free dispatcher call — poll signals, enrich, find) or { type:'reasoning', goal } (a bounded brain-grounded generation that records a decision). Steps run in order; a failed step or the budget cap PAUSES the job. Jobs NEVER send — manual-first holds. action='list' / 'get' { id } / 'cancel' { id }.Connector
- Run market positioning analysis on a CV version (5 credits, takes 20-30s). Returns positioning snapshot, detected narrative lens, recruiter inference, mixed signal flags, and a session_id. This is step 1 of the 3-step positioning pipeline: analyze_positioning -> ceevee_get_opportunities(lens) -> ceevee_confirm_lens. Pass the returned session_id to subsequent steps. cv_version_id from ceevee_upload_cv or ceevee_list_versions.Connector
- Get career pivot opportunities based on the CV and a selected narrative lens (3 credits). Returns 2-4 opportunities with rationale, CV signals, and market context. This is step 2 of the positioning pipeline (after ceevee_analyze_positioning). The 'lens' value should come from ceevee_analyze_positioning output (e.g. 'Technical Leader', 'Scale-up Builder'). Pass the same session_id from step 1. Next step: ceevee_confirm_lens with selected opportunities.Connector
- Confirm a narrative lens and generate targeted CV edits with trade-offs (5 credits, takes 20-30s). Returns an array of section edits with before/after text, trade-off notes, and optionally clean + review PDF download URLs. This is step 3 (final step) of the positioning pipeline. Pass confirmed_lens from ceevee_analyze_positioning, and optionally positioning_snapshot, detected_lens_full, recruiter_inference, selected_opportunities from prior steps for richer edits. Use ceevee_explain_change to understand any specific edit.Connector
- Get relationships for a specific entity from Knowledge Graph. USE WHEN: - 'Кто работает над X?' - filter by works_on - 'С кем общался Y?' - filter by discussed_with - 'Кто из компании Z?' - filter by member_of - 'Что связано с W?' - no filter, get all REQUIRES: entity_id from previous kg.find_entity step. Use: {{step_N.entity_id}} where N is the find_entity step number.Connector
- Start the purchase flow for a domain via Stripe's Machine Payments Protocol (MPP). MPP lets autonomous agents pay with fiat (cards, Link) or stablecoins via Shared Payment Tokens, with no browser checkout. Two-step flow: Step 1: Call this tool to get an order_id and pay_url. Step 2: Make an HTTP GET request to the pay_url with an MPP-enabled HTTP client. The server responds with HTTP 402 + WWW-Authenticate; the client creates a Shared Payment Token and retries with an Authorization header. The server charges the SPT through Stripe and kicks off domain registration. After payment, call get_domain_status(order_id) to poll until complete. Requires: An MPP-compatible client configured to mint SPTs against the server's advertised Stripe Business Network profile. Args: domain: The domain to purchase (e.g. "coolstartup.com"). first_name: Registrant's first name. last_name: Registrant's last name. email: Registrant's email address. address1: Registrant's street address. city: Registrant's city. state: Registrant's state or province. postal_code: Registrant's postal/zip code. country: 2-letter ISO country code. phone: Phone number in format +1.5551234567. org_name: Organization name (optional). Returns: Dict with order_id, pay_url (full URL), price_cents, price_display, network_id, and payment_method_types.Connector