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668,457 tools. Updated 2026-10-17 05:41

"Automatic context compression tools or methods" matching MCP tools:

  • 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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  • WHEN: object name is unknown, partial, or you need to find by concept/keyword. Search the D365 F&O knowledge base for X++ code, tables, classes, forms, views, enums, EDTs, security objects using natural language or partial names. Returns ALL chunks (metadata, Declaration, methods) for the top-scoring objects so the LLM has complete context on the first call. Lower-scoring results return a short preview. No follow-up get_object_details call is needed for top results. NOT for listing all objects in a model -- use list_objects for that. NOT when the exact name is known -- use get_object_details for that. NEVER call search_d365_code twice in the same conversation turn. If one search did not find the object, answer from what you have -- do not repeat the search. When you need context on MORE THAN ONE concept simultaneously, use batch_search instead -- it runs all queries in parallel and is faster. NEVER call for ADO items (FDD, RDD, IDD, Bug, Task, PR, WorkItem, sprint, #1234) -- use ado_* tools instead.
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  • Answers ONE question: is stress reaching US household credit? Use corporate_transmission_board for firms. Delinquency, charge-offs, revolving credit and unscored debt-service context; gaps stay in cannot_see. Display-only; no institution score or watchlist tier. full:true adds methods and histories.
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  • Store or update ONE durable memory entry (key → value) for this user so context survives across sessions — preferences, prior conclusions, working context. Replace semantics per key (reusing a key overwrites it). Do NOT store a number you would later cite as a fact: financial figures come from data tools and carry fact_ids; memory values are never treated as verified figures. Caps: 200 entries / 8000 chars per value. Tier: sp500+ (sample rejected).
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  • Read an existing USRProf or uploaded USRProf source artifact without reconstruction, scope replacement or model activation. For chat attachments without artifact IDs, first use import_profile_attachment with explicit upload permission if host file handoff is available; otherwise offer the browser upload page immediately. Do not substitute raw-file commentary for this canonical inspection. Required query: summary, quality, recency, course_fit, coverage or insights. Stored readiness is not current fitness or a new pacing gate. Course context is required for course_fit and optional for coverage only; selection is a preview, never applied. Paged queries default offset 0/limit 20, maximum 100. insights rejects offset, limit and course context and returns six bounded families: durability, uphill, GAP, run/hike, terrain mechanics and terrain-relative strength. Preserve their distinct age scopes, fitted/default provenance and extrapolation disclosures; defaults are not athlete limits. Run/hike above 1,000,000 eligible pairs is resource_limited without suppressing other families. Other rendered views are explicitly unavailable in MCP, not missing athlete evidence; experimental gates remain unchanged. Server owns as-of time. No raw source identities or full graph arrays; no Strava, writes, automatic upload or strategy application. Use goal/planning tools only when separately available, not these observational results as a finish-time prediction.
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Matching MCP Servers

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    Drives real COMSOL Multiphysics sessions through natural language over MCP, covering geometry, materials, physics fields, coupling, meshing, studies, solving, and result visualization via 99 tools with session protection, serial execution, and HTTP authentication. Also provides an original 3D heat-sink example and curated classic case recipes across physics domains such as AC/DC, acoustics, heat transfer, structural mechanics, and optics.
    MIT
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    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.
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    MIT

Matching MCP Connectors

  • Research methods for AI assistants: study design guidance, R and Python code, and citations

  • Oregon DMV MCP — live wait times at all 60 Oregon DMV field offices, plus the office

  • Groq-powered vault compression: 50 cold (least-read) memories → 5 dense summaries. Source memories are archived after compression. Net result: sharper vault, lower LLM token cost when injecting context. Automatically refunded if Groq fails. $0.05. Requires API key.
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  • Groq-powered vault compression: 50 cold (least-read) memories → 5 dense summaries. Source memories are archived after compression. Net result: sharper vault, lower LLM token cost when injecting context. Automatically refunded if Groq fails. $0.05. Requires API key.
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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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  • Answers ONE question: is US funding stress reaching nonfinancial firms? Use household_credit_board for households. CP, bank credit lines and real-economy confirmation; cannot_see preserves gaps. Seiche context never enters regime or transmission. Display-only; no institution score or tier. full:true adds methods.
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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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  • When to use standalone media tools (automatic model routing for a single asset) versus workflows (full editable professional video). Call when choosing between generate_* tools and workflow tools. Equivalent to reading the `guidance://tools-vs-workflows` MCP resource — provided as a tool for clients that don't read resources directly.
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  • WRITE (uses quota): ask TinyPNG (Tinify) to fetch a public image by URL and compress it (JPEG, PNG, WebP, AVIF or JPEG XL). Returns the output_url (Tinify's Location address of the compressed image), the compressed size and type, the savings when the input size is known, and the Compression-Count header (compressions used this month). The image is stored by Tinify for about 48 hours; output_url needs your API key, so use tinypng_download_image to get the bytes and tinypng_transform_image to resize, convert or keep metadata. Each compression counts against the monthly quota of your Tinify API key (the free plan includes 500 per month); resizing and converting count one extra compression each. POST /shrink.
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  • Check for additional tools whenever your task might benefit from specialized capabilities, even if existing tools could work as a fallback. Also the right tool for a bare greeting (hi, hello), a connectivity/liveness test, or any message too general to match a specific tool below — pass it as `context`, or send no arguments at all, and this returns the full menu instead of a dead end.
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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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  • Compress retrieved memories using a hybrid approach (extractive + LLM). First searches Hipocampo (SSC v1.0), then compresses the top-k results: - method="extractive": sentence-level keyword relevance (fast, no API cost) - method="llm": summarization via NVIDIA NIM (highest quality, API cost) - method="hybrid" (default): uses LLM for technical/code content, extractive for generic text Use this tool BEFORE sending context to another LLM to reduce prompt size while preserving critical information. Args: query: Natural language search query. k: Number of memories to retrieve (default 5, max 20). method: Compression method: "hybrid" (default), "extractive", or "llm". target_token: Target token count (-1 = auto, based on content). include_metadata: Include per-memory details in output. budget_ratio: Scale factor for auto-estimated tokens (default 1.0). Returns: Compressed context as plain text with compression statistics. Includes: compressed text, original/compressed char counts, ratio, latency.
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  • WRITE: change fields of an existing customer; send only the fields to change. Cannot touch payment methods, balances or status. Replaces the previous values of the fields you send. PUT /customer/{customer_id}.
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  • Read-only JSON-RPC pass-through for Solana mainnet. Allowed methods: getAccountInfo, getBalance, getTransaction, getSignaturesForAddress (limit <= 100), getTokenAccountsByOwner, getTokenSupply, getSlot, getBlockTime, getLatestBlockhash, getMultipleAccounts (<= 20). Write methods and getProgramAccounts refused. Price: $0.001 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.
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  • List all candidates in a hiring context. Returns an array of candidate objects, each with an 'id' field. Use candidate id as candidate_id in atlas_start_gem_analysis, atlas_fit_match, atlas_fit_rank, atlas_generate_interview, and batch tools. Requires context_id from atlas_create_context or atlas_list_contexts. Free.
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