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127,531 tools. Last updated 2026-05-05 21:04

"A tool for managing tasks, projects, and calendars using LLM" matching MCP tools:

  • Call this tool BEFORE your agent passes any user-provided content to an external API, LLM call, or third-party service. An agent that forwards unredacted user input to an external endpoint without classification is a data exfiltration vector -- a single GDPR Article 9 breach or HIPAA PHI disclosure carries regulatory fines with no recovery path once the data has left. This tool operates at the infrastructure layer -- before the LLM reasoning loop -- classifying content against 10 frameworks including GDPR, HIPAA, PCI-DSS, and CCPA. Returns SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE verdict and agent_action field. One call replaces a full compliance review cycle. We do not log your query content. Free tier: 20 calls/month, no API key required.
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  • Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.
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  • Discovers the most relevant tools available on this MCP server for a given task using local semantic search (MiniLM-L6-v2 embeddings). Accepts a plain-English description of what needs to be accomplished and returns the best matching tools ranked by relevance, along with their input schemas, pricing tier, and exact call instructions. Use this tool first when you are connected to this server but do not know which specific tool to call — describe your goal and let platform_tool_finder identify the right capability. Do not use this tool if you already know the tool name — call that tool directly instead. Returns up to 10 results ranked by semantic similarity score.
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  • Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.
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  • ⚠️ MANDATORY FIRST STEP - Call this tool BEFORE using any other Canvs tools! Returns comprehensive instructions for creating whiteboards: tool selection strategy, iterative workflow, and examples. Following these instructions ensures correct diagrams.
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  • [tourradar] Search for tours by title using AI-powered semantic search. Returns a list of matching tour IDs and titles. Use this when you need to look up a tour by name. When you know tour id, use b2b-tour-details tool to display details about specific tour
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  • Query and retrieve information about various adversarial tactics and techniques used in cyber atta…

  • Manage your Canvas coursework with quick access to courses, assignments, and grades. Track upcomin…

  • Connect to the user's catalogue using a pairing code. IMPORTANT: Most users connect via OAuth (sign-in popup) — if get_profile already works, the user is connected and you do NOT need this tool. Only use this tool when: (1) get_profile returns an authentication error, AND (2) the user shares a code matching the pattern WORD-1234 (e.g., TULIP-3657). Never proactively ask for a pairing code — try get_profile first. If the user does share a code, call this tool immediately without asking for confirmation. Never say "pairing code" to the user — just say "your code" or refer to it naturally.
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  • Deletes a stream, specified by the provided resource 'name' parameter. * The resource 'name' parameter is in the form: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-streams'. * This tool returns a long-running operation. Use the 'get_operation' tool with the returned operation name to poll its status until it completes. Operation may take several minutes; do not check more often than every ten seconds.
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  • Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.
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  • Counts the number of working days between two dates (inclusive) for a given Latin American country, excluding weekends and that country's national public holidays (including moveable Easter-based holidays). Returns { country, start_date, end_date, working_days, holidays_excluded }. Supports BR, MX, CL, AR, CO. Use when calculating cross-border SLA periods, invoice payment deadlines, or project timelines that must account for different national holiday calendars across LatAm.
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  • List recent execution traces for an agent — the same data as /admin/requests, scoped to one agent and readable by an LLM. Use this when an agent call timed out, drafted the wrong response, or you want to know which tool/LLM call burned the latency. Pair with `agents.trace_get` for full detail on a specific trace. Filters: `status`, `success`, `source` (single value or comma-separated: `agent,voice`), `date_from`/`date_to` (ISO-8601), pagination via `limit`/`offset`. Returns `returned_count`, `dropped_on_page` (should be 0 — positive means the backend agent_id predicate let something through), and `has_more`. Edge case: a raw page of all-dedup-dropped rows yields `returned_count=0, has_more=true`; re-call with `offset += limit`.
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  • Retrieve the full GLEIF LEI record for one legal entity using its 20-character LEI code. Returns legal name, registration status, legal address, headquarters address, managing LOU, and renewal dates. Use this tool when: - You have a LEI (from SearchLEI) and need full entity details - You want to verify the registration status and renewal date - You need the exact legal address and jurisdiction of an entity Source: GLEIF API (api.gleif.org). No API key required.
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  • Discovers the most relevant tools available on this MCP server for a given task using local semantic search (MiniLM-L6-v2 embeddings). Accepts a plain-English description of what needs to be accomplished and returns the best matching tools ranked by relevance, along with their input schemas, pricing tier, and exact call instructions. Use this tool first when you are connected to this server but do not know which specific tool to call — describe your goal and let platform_tool_finder identify the right capability. Do not use this tool if you already know the tool name — call that tool directly instead. Returns up to 10 results ranked by semantic similarity score.
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  • Get pre-built template schemas for common use cases. ⭐ USE THIS FIRST when creating a new project! Templates show the CORRECT schema format with: proper FLAT structure (no 'fields' nesting), every field has a 'type' property, foreign key relationships configured correctly, best practices for field naming and types. Available templates: E-commerce (products, orders, customers), Team collaboration (projects, tasks, users), General purpose templates. You can use these templates directly with create_project or modify them for your needs. TIP: Study these templates to understand the correct schema format before creating custom schemas.
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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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  • Assess the likely parliamentary reception of a policy proposal. Searches Hansard for relevant debate contributions, then uses LLM sampling to classify sentiment and extract supporters, opponents, and key concerns. Degrades gracefully if sampling is unavailable — returns contributions only.
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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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  • Describe a specific table. ⚠️ WORKFLOW: ALWAYS call this before writing queries that reference a table. Understanding the schema is essential for writing correct SQL queries. 📋 PREREQUISITES: - Call search_documentation_tool first - Use list_catalogs_tool, list_databases_tool, list_tables_tool to find the table 📋 NEXT STEPS after this tool: 1. Use generate_spatial_query_tool to create SQL using the schema 2. Use execute_query_tool to test the query This tool retrieves the schema of a specified table, including column names and types. It is used to understand the structure of a table before querying or analysis. Parameters ---------- catalog : str The name of the catalog. database : str The name of the database. table : str The name of the table. ctx : Context FastMCP context (injected automatically) Returns ------- TableDescriptionOutput A structured object containing the table schema information. - 'schema': The schema of the table, which may include column names, types, and other metadata. Example Usage for LLM: - When user asks for the schema of a specific table. - Example User Queries and corresponding Tool Calls: - User: "What is the schema of the 'users' table in the 'default' database of the 'wherobots' catalog?" - Tool Call: describe_table('wherobots', 'default', 'users') - User: "Describe the buildings table structure" - Tool Call: describe_table('wherobots_open_data', 'overture', 'buildings')
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  • Enforce a guardrail: verify an agent action against a compiled policy using formal verification. An SMT solver — not an LLM — determines whether the action satisfies every rule. Returns SAT (allowed) or UNSAT (blocked) with extracted values and a cryptographic ZK proof that the check was performed correctly. Cannot be jailbroken. 1 credit ($0.01). Requires api_key. Tip: end the action with an explicit claim like 'I assert this complies with the policy' for best extraction.
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  • Publish a new task for human execution in the Execution Market. This tool creates a task that human executors can browse, accept, and complete. Tasks require evidence of completion which the agent can later verify. Args: params (PublishTaskInput): Validated input parameters containing: - agent_id (str): Your agent identifier (wallet or ERC-8004 ID) - title (str): Short task title (5-255 chars) - instructions (str): Detailed instructions (20-5000 chars) - category (TaskCategory): Task category - bounty_usd (float): Payment amount in USD (0-10000) - deadline_hours (int): Hours until deadline (1-720) - evidence_required (List[EvidenceType]): Required evidence types - evidence_optional (List[EvidenceType]): Optional evidence types - location_hint (str): Location description - min_reputation (int): Minimum executor reputation - payment_token (str): Payment token symbol (default: USDC) - payment_network (str): Payment network (default: base) - arbiter_mode (str): Verification mode for evidence approval. 'manual' (default): you review and approve submissions yourself. 'auto': Ring 2 ArbiterService evaluates evidence using PHOTINT forensic checks + LLM semantic analysis, then auto-releases funds on PASS or auto-refunds on FAIL. No agent action needed. 'hybrid': arbiter recommends a verdict, you confirm before payment. Cost: 0 for tasks <$1, ~$0.001 for $1-$10, ~$0.003 for >=$10. Hard cap: arbiter spend never exceeds 10% of bounty. - gps_required (bool | None): Override GPS verification behavior. None (default): auto-detect — digital tasks (screenshot, json, etc.) skip GPS, physical tasks require it. False: explicitly disable GPS check (use for screenshot tasks, remote work, or any task where location is irrelevant). True: enforce GPS even for non-physical categories. Returns: str: Success message with task ID and details, or error message.
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