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650,522 tools. Updated 2026-10-14 02:11

"Tools or methods for learning and visualizing concepts with diagrams" matching MCP tools:

  • Returns a condensed 2-minute quick-start guide with minimal working examples, core syntax reference, and key concepts. Use this for rapid learning when you need to generate simple diagrams quickly.
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  • List every available Ripostiq course (no login needed). Call this FIRST when a user wants to browse or start learning. Each course has a free Module 1 anyone can start immediately via begin_course(course) → teach_section. Returns the `course` id to pass to other tools. For a logged-in learner this also includes courses THEY authored (marked `mine`, with their `visibility`) — offer those alongside the catalog.
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  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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  • Low-level Telegram API (MTProto) invoke for methods not wrapped by other tools. Dangerous methods require allow_dangerous=true. Success: API result dict or normalized error. PII and credential-shaped fields (phone, access_hash) are dropped from a successful result by default; pass include_sensitive=true for the raw payload. A bare message id needs a chat binding: requests with no peer field (messages.GetMessages, messages.DeleteMessages) are refused, because a bare id resolves against an arbitrary dialog. Use channels.GetMessages or messages.GetHistory, which carry the binding. messages.GetHistory cannot address a forum topic (no thread_id/top_msg_id in the schema, and channels.GetHistory does not exist) -- use messages.Search with top_msg_id, or the high-level get_messages with reply_to_id. Full documentation: https://github.com/leshchenko1979/fast-mcp-telegram/blob/main/docs/Tools-Reference.md
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  • READ-ONLY: returns generated source code as text and writes nothing to disk, creates no project and runs no command. Generates an idiomatic @imqueue/rpc service (an IMQService subclass with @expose()d, JSDoc-typed methods) plus a bootstrap that starts it. Provide the methods you want, or omit them for a starter template. Any non-primitive parameter or return type also gets a types.ts with the required @classType()/@property() declarations — without those the generated client types it `any`, which compiles. Use create_service (local install only) if you want files actually written.
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  • Keyword search over a local Markdown index for langchain, llamaindex, ollama, or xrpl; returns compact chunks and never live-crawls. Use for concepts, symbols, or questions; hitCount 0 means no match — shorten the query. Do not use for stack traces (diagnose_framework_error), import/package lookup (resolve_symbol), or copy-paste current syntax (fetch_latest_syntax). Paid tools/call: $0.001 USDC or 1000 drops XRP.
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Matching MCP Servers

  • A
    license
    A
    quality
    C
    maintenance
    Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.
    8
    38 PyPI
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.
    Apache 2.0

Matching MCP Connectors

  • Generate cloud architecture diagrams, flowcharts, and sequence diagrams.

  • Decision Layer for AI Agents — 58+ tools, Advisor, MCP. Free key: POST /v1/register {}.

  • Search Vectree's curated learning paths — ordered sequences of diagrams that teach a subject from the ground up, one step at a time. Vectree explains how things work as zoomable, labelled schematics. A learning path strings a set of those diagrams into a syllabus, so a reader moves from the fundamentals of a subject to its harder parts in a deliberate order. Use this when the user wants to *learn*, *study* or *get started with* a whole subject. When they want one specific topic explained instead, use `search_diagrams` — that searches individual diagrams rather than sequences. Describe the subject in natural language; the search is semantic, so a full sentence works better than a bare keyword. Each result carries a slug — pass it to `get_learning_path` for the full ordered sequence. Only published paths are searched. Nothing is generated on demand, so a subject with no match simply has no path yet.
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  • Use this free read-only discovery-tier tool when the user asks for help, available commands, MCP tools, core concepts, pricing, parser-stable fields, grants, x402, Morning Brief, Company Report, MSTR treasury review, perp adapters, SPECTRA, or examples. Parameters: optional topic, detail, include_examples, question, or query fields; callers may omit all arguments for overview help. Behavior: local and idempotent with no destructive side effects; it does not run paid analysis routes or expose internal-only tools. It translates natural-language orientation requests into the live DeltaSignal MCP/OpenAPI discovery contract and points users to tools/list, /v1/pricing, /v1/contract/fields, and /v1/readiness. It is not a trading, execution, or investment-advice tool and must not expose internal-only tools unless the live public contract lists them.
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  • Use this free read-only discovery-tier tool when the user asks for help, available commands, MCP tools, core concepts, pricing, parser-stable fields, grants, x402, Morning Brief, Company Report, MSTR treasury review, perp adapters, SPECTRA, or examples. Parameters: optional topic, detail, include_examples, question, or query fields; callers may omit all arguments for overview help. Behavior: local and idempotent with no destructive side effects; it does not run paid analysis routes or expose internal-only tools. It translates natural-language orientation requests into the live DeltaSignal MCP/OpenAPI discovery contract and points users to tools/list, /v1/pricing, /v1/contract/fields, and /v1/readiness. It is not a trading, execution, or investment-advice tool and must not expose internal-only tools unless the live public contract lists them.
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  • List the saved payment methods (used only to pay for flight bookings, not for cards or adding funds). Returns each method with its id, brand, last 4 digits, and expiry, and marks the default one. Use setup_payment_method to add a new one. The gated tools set_default_payment_method and remove_payment_method also exist; call them by name even though they aren't in the tools list.
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  • Discover AgentMarketplace's capabilities, tools, auth methods, and scopes. Call this first when connecting to AgentMarketplace to understand what's available and how to authenticate. No authentication required. Returns a catalog of available tools, resources, auth methods, and scopes.
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  • Browse the complete WORKMINE catalog for free: paid API names, exact USDC-denominated prices, HTTP methods and URLs. Use this when comparing available tools; use find_workmine_tool when you already have a natural-language task. This catalog call itself does not execute or purchase a paid API.
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  • Render an interactive MCP app mind map when the user needs hierarchical structure shown visually instead of as prose. Use it for breaking down ideas, plans, study material, or systems into a root topic with nested branches; do not use it for tables, flowcharts, Mermaid/Graphviz diagrams, or plain text lists. Input `mindmap_markdown` must be a clean markdown tree with one `#` root heading and 2-space-indented bullet nesting. If the user gives prose, first reshape it into that hierarchy, then call this tool.
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  • Walk the guided path to build a nifra app end to end - an ORDERED sequence (create → page route → loader → typed API → typed client → auth → background jobs → deploy), not the random-access search of nifra_docs/nifra_example. Call with no args for the step index; pass `step: N` for that step's goal, how to do it (which tool emits the correct artifact), and how to verify it. Use it when scaffolding a new app or learning nifra's flow - each step composes the other nifra_* tools.
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  • When: use when you will build the diagram yourself with the canvas_* tools, because you want control over the result or already know the architecture from the conversation. Use create_diagram instead when Datadef's model should design the whole thing from a prompt. Creates an empty diagram and returns its diagram_id. No AI generation runs: you draw it. Call get_design_guide first: it is the standard Datadef's own generator follows, and building without it produces the diagrams this tool exists to avoid. Next: canvas_search_icons (all vendors in one call), then one canvas_batch with every zone (group_nodes) and node (add_nodes), canvas_connect_nodes, canvas_layout_canvas once, canvas_view.
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  • Checkout methods open right now: "crypto" (a hosted invoice: BTC, ETH, TRX, USDT), "card", and "wallet" (a prepaid balance, see create_wallet). `gateways` names the providers behind each method; a checkout that does not ask for one by name goes to the first, which is what these tools do.
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  • Use this when the learner explicitly asks to save Spanish or French words for later practice, mark words as learning, or correct words they already know in Fabling. Set up to 20 resolved words to known or learning. Resolve lemma IDs first and clarify ambiguous meanings or status. Do not infer permission from a quiz answer or mark words known automatically. This is a learner correction, not a mastery assessment. Requires vocabulary write permission. Reuse actionId only for retries.
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  • Convertible-bond engine. Value callable and puttable convertible or exchangeable bonds with credit risk using a Tsiveriotis-Fernandes lattice (equity discounted at the risk-free rate, debt at a credit spread), with conversion, issuer call, holder put, coupon schedule, and a straight-bond floor. Use this for convertible and exchangeable bonds; for a plain bond or rate curve use calculate_fixed_income, and for a standalone option or warrant use calculate_option. The lattice_tsf, lattice_intensity, finite_difference, lsmc and quantlib methods are alternative numerical schemes for the same valuation and take identical inputs (Tsiveriotis-Fernandes is the reference, lsmc is Monte Carlo, quantlib needs the optional engine); choose one. Methods: lattice_tsf/lattice_intensity/finite_difference/lsmc/quantlib: spot, face, coupon_rate, maturity, conversion_ratio, volatility, risk_free, credit_spread, call_schedule, put_schedule, rights_priority. Supply exactly one method with its declared inputs (no defaults). An unknown method, a missing input, or an extra field returns an error envelope (code INVALID_ARGUMENT) instead of raising.
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  • AUTHORITATIVE full XBRL fundamentals dump for a US public company. Send the company as `cik` — that argument takes a TICKER ("NVDA") or a CIK ("320193"), and `ticker` / `ticker_or_cik` are accepted as aliases for it. Returns every reported financial metric (hundreds of concepts: revenue, net income, assets, liabilities, EPS, cash flow lines, segment breakdowns) with annual and historical values pulled straight from the company's SEC filings — the official numbers, not estimates. Use when you need the complete fundamental picture vs. one metric (for one metric use edgar_company_concept). Leads with latest_annual — revenue, net income, assets, cash, EPS for the most recent fiscal year, resolved to whichever XBRL concept the filer currently reports under — and flags retired concepts (e.g. a pre-ASC-606 Revenues tag) as stale so a 2010 figure is never mistaken for current. Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries. For just the headline figures plus the recent filings list, edgar_company_snapshot is the smaller one-call answer.
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  • Rename, merge, or delete a tag — the cleanup tools for the tag list (this operates on the tag itself, unlike notes-update which only edits one note's tags). Required: action ('rename'|'merge'|'delete') and the tag to act on via name (string, case-insensitive) or tag_id (integer). For 'rename' also pass new_name. For 'merge' also pass the destination via target_name or target_id: every note on the source tag is moved onto the destination and the source tag is deleted — ideal for collapsing duplicates like 'machine learning' into 'machine-learning'. If target_name names a tag that doesn't exist yet, it's created, so you can merge straight into a clean canonical name without creating it first. 'delete' removes the tag from all its notes and deletes it; deleting a name that doesn't exist succeeds as a no-op (already_absent: true), so it's safe to retry. Merge and delete are destructive and cannot be undone; check tags-list first.
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