"Finding tasks for large language model agents" matching MCP tools:

• browse_tasks

  AgentMarketplace

  Browse tasks on the marketplace. Defaults to open (``posted``) tasks. Filters are plain-column matches — to filter by requirements (capabilities, min_trust), use ``find_agents_for_task`` for ranked, requirement-aware matching; this tool's own filters stay plain-column. Args: access_token: AgentAuth bearer token (requires ``market.read``). status: Task status to filter on. Defaults to ``"posted"`` (open tasks). Pass any valid status to see tasks in other states. task_type: Optional exact-match task type filter. limit: Maximum results, 1-100. Default 20. Returns: ``tasks`` (list, newest first), ``total`` (count returned), and the applied ``filters``. ``{"error_code": "invalid_input", ...}`` listing the valid values if ``status`` is not a real task status.

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• publish_task

  Molt2Meet

  Publish a task to make it visible to operators. Works for both settlementMode='escrow' and 'direct' tasks. The task must be in Draft or Funded status. For escrow Draft tasks: funds are automatically reserved and locked from your wallet (requires sufficient balance). For direct-settlement Draft tasks: no funding happens — the task goes directly from Draft to Published because the client pays the operator on-site (no escrow). This is the intended shortcut for direct-settlement. For Funded tasks (after escrow Quote → Fund flow): the funds are already locked, the task is simply made visible. After publishing, operators can accept the task. Requires authentication. Next: wait for task.accepted via get_task_events or webhook.

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• remediate_findings

  quantakrypto pqc-tools (hosted)

  Produce a deterministic remediation REQUEST bundle (rubric + fix schema + per-finding metadata + fingerprints) for YOU (the host agent) to fix. This tool calls no model and needs no key. For each finding, propose the corrected FULL file content, then VERIFY with verify_fix and keep only fixes that clear the finding. Never touch files with secrets; never auto-merge. Pass 'findings' from scan_path --format json.

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• noaa_spaceweather_get_aurora_forecast

  noaa-spaceweather-mcp-server

  OVATION model aurora forecast for the next ~30–60 min: global grid of aurora probability percentages by latitude/longitude (1° resolution). With optional coordinates, returns the local aurora probability at the nearest grid point, the minimum Kp needed for aurora at that latitude, and a plain-language go/no-go verdict. Without coordinates, returns only global metadata. Data updates every ~5 minutes. Coordinates are geographic (WGS84), not geomagnetic.

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• tf_harnesses

  terminalfeed

  Returns a snapshot of public agentic-coding benchmark scores across SWE-bench Verified, Terminal-Bench, Aider Polyglot, and METR HCAST. Each row pairs a harness with a model. Same model can score very differently on different harnesses; that gap is the value-add. Pass ?view=summary for top 10 combined leaderboard plus biggest harness gaps; ?view=gaps for full per-model harness deltas; ?view=combined for normalized cross-benchmark ranking; ?view=raw (default) for the full benchmark/result graph. Source: hand-curated from upstream leaderboards (swebench.com, terminal-bench.org, aider.chat, metr.org). Cache TTL 12h. Use when the agent needs to recommend a harness/model combo or explain why two agents using the same model perform differently.

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• resolve_topic

  Pine Script

  USE WHEN looking up an exact Pine Script API term or known concept keyword. Returns the best-matching doc paths with matched keywords and a retrieval suggestion (get_doc or list_sections + get_section). AFTER calling this tool, follow the suggestion: call get_doc() for small files or list_sections() + get_section() for large files. For natural language questions use search_docs() instead. Data sourced from bundled TOPIC_MAP and doc file content scan.

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• mio_ai_document_translator

  MiOffice — AI-Powered Workspace Studio

  AI Document Translator — Translate text between 16 languages using AI. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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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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• check_local_viability

  Local Model Suitability MCP

  Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.

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• search_agent_directory

  TensorFeed

  Search the TensorFeed Agent Self-Directory for hireable AI agents. Filter by skill (from a controlled vocab including research, data-analysis, coding, content-writing, voice-acting, image-generation, etc), service_area (research/data/coding/writing/voice/image/video/other), language (BCP 47), availability, hourly rate cap, minimum years of experience, or verified-hireable status. Verified-hireable members (operators paying $5 USDC/30 days for top-tier visibility) sort first. Free tier capped at 25 results. Returns wallet, display_name, operator_url, skills, rates, languages, years_experience, composite reputation rank, trust grade. TF publishes self-descriptions; TF takes no fee from off-platform transactions between operators and the agents who contact them.

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• edgar_company_facts

  Edgar

  AUTHORITATIVE full XBRL fundamentals dump for a US public company by CIK. 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). Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries.

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• company_search

  Drillr — The financial MCP for AI agents

  Use for qualitative company discovery (industry, business model, supply chain, competitors, management background). For numerical screening (revenue, margins, ratios, growth rates) use run_sql on company_snapshot instead. Drillr's company knowledge base — searchable across industry classification, product offerings, business model, segment structure, competitive landscape, supply chain, management background, and customer profile. Coverage: US, Japan, Hong Kong, China A-shares, and Korea. `market` accepts one lowercase value or a list from `us | jp | hk | cn | kr`; omit it or pass `[]` for all five. List order does not set priority. Pass a natural-language description (for example, "Hong Kong and China EV battery suppliers"). Returns a structured list of matching companies with context snippets. ONLY for finding a LIST of companies by description.

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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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• find_agent

  Zambo AI Tools

  Search the Zambo Agent Registry — the public phone book for AI agents. Find agents by capability, name, or description and get back their handle, endpoint URL, wallet address (for x402 payments), and online status. Use this to discover agents that can perform specific tasks, then connect directly or route x402 payments via axis_tool_dispatch (x711 execution layer). Free, no auth, unlimited searches.

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• list_objects

  ALM X++ MCP Server

  WHEN: you need ALL objects of a given type or in a given model. Triggers: 'list all tables in ALM', 'show all classes', 'quels objets dans le modèle', 'give me all forms'. Full index scan -- returns EVERY matching object, not just top search results. Use to discover what tables, classes, forms, enums, etc. exist in a specific model. When no filters are given and a custom model is configured, defaults to listing that model. NOT for a single object -- use get_object_details. NOT for natural language search -- use search_d365_code.

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• list_tasks

  mcp

  List the account's past generation tasks, newest first, with pagination. Filter by status (processing/completed/failed), model id, category (video/image/audio/text/llm), provider, or a created_after/created_before time window. Use it to find an earlier generation's task_id (for get_task, extend_video, or the veo/grok upscalers) or to review recent spend. Within one filter, comma-separated values are OR'd; different filters are AND'd.

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• search_datasheets

  sheetsdata-mcp

  Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.

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• route_tasks

  glimind

  Batch version of route_task: give an ARRAY of plain-language sub-tasks and get the best live tool for EACH in one call — so a multi-step plan collapses N round-trips into one. Each result carries the tool's endpoint, input schema, example args, free/paid + how-to-pay, and fallbacks (compact by default to save tokens). Use this at the start of a multi-step task to plan every step at once.

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• get_cost_decoder

  The Front Desk Review

  The REAL all-in monthly cost per vendor for a decoder topic (slug from list_cost_decoders, e.g. 'ai-customer-support-cost'), computed in deterministic code from sourced, dated inputs — each vendor's per-seat price + AI billing model + per-unit price, totalled at named scenarios (e.g. 5 agents at 1,000 and 5,000 AI resolutions/mo) with the arithmetic shown. Quote-only inputs return a null total, never a fabricated number. Optionally pass agents + resolutions for a custom scenario. This is the citable answer to 'what does <AI tool> actually cost' that a base model gets wrong.

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• count_tokens

  inktomd MCP Server

  Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status

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