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127,390 tools. Last updated 2026-05-05 15:10

"A server for software engineers that integrates with Jira and Slack" matching MCP tools:

  • Submit a list of URLs to be checked. Returns a job_id that can be polled via get_job_status or fetched via get_job_results. For up to ~200 URLs this tool waits for completion (up to 60 seconds) and returns the results directly; for larger jobs it returns early with job_id and the agent should poll.
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  • Multipart file upload for content that exceeds a single model response's output token cap (big SPA bundles, large seed data, inline vendor libs). Flow: first call with chunk_index=0 and NO upload_id — response returns an upload_id. Subsequent calls pass that upload_id with chunk_index=1, 2, 3…. Last call sets final=true to atomically concatenate and commit as one ProjectFile. Chunks are staged in Redis with a 10-minute TTL. chunk_index overwrites (safe to retry). Max chunk size: 64 KB. Max assembled file: 20 MB.
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  • Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. 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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  • Creates an automation on a perspective. Triggers: per_interview (fires on every completed conversation) or scheduled (daily/weekly digest). Channels: webhook, email, slack, hubspot. Execution modes: direct (fast, deterministic) or agent (LLM-powered). Behavior: - Each call creates a new automation — even if name/config matches an existing one. - Once enabled, the automation starts firing on real events: per_interview sends on every completed conversation going forward; scheduled sends a real message on the configured cadence (daily/weekly). - Webhook URLs are validated. For HubSpot, the workspace's HubSpot connection is required — errors with "Could not resolve HubSpot portal ID — please reconnect HubSpot" if not connected. - Errors when the perspective is not found or you do not have access. When to use this tool: - The user wants ongoing notifications on every completed conversation (per_interview). - Building a daily/weekly digest delivered to Slack, email, HubSpot, or a webhook (scheduled). When NOT to use this tool: - Trying a one-off send before going live — create the automation, then use automation_test (use override_email / override_webhook to avoid hitting real recipients). - Editing or toggling an existing automation — use automation_update. - Connecting Slack or HubSpot — use integration_manage first; the provider must be connected before slack/hubspot channels work. Example — per-conversation Slack notify: ``` { "perspective_id": "...", "automation": { "name": "Notify Slack", "trigger": { "type": "per_interview" }, "execution_mode": "agent", "channel": { "type": "composio", "delivery_config": { "provider": "slackbot", "tool_slug": "SLACKBOT_SEND_MESSAGE", "params": { "channel": "#research" }, "resource_id": "...", "resource_name": "..." } } } } ``` Typical flow: 1. integration_manage (operation: "list"/"connect") → ensure Slack / HubSpot is connected (only needed for those channels) 2. automation_create → create the automation 3. automation_test (with overrides) → verify delivery before relying on it
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  • Resolves a place mention (free-text name, address, or lat/lng) to the protocol's cell64 identifier, and returns the topic-grouped inventory of bands and algorithms available at that location. When to use: Use whenever the input refers to a real-world location and the next step needs the cell64 identifier or wants to know which bands are available before recalling. The response carries `data_at_this_cell` with three sub-fields: `live_bands_by_topic` (every band recallable here, grouped by topic such as flood_water_event_window, vegetation_condition, built_up_human_geography), `algorithms_for_topic` (composition recipes that fuse those bands into named scores), and `declared_but_no_materializer_at_this_responder` (cube slots reserved without a live connector). For the single-shot path that runs the full chain server-side and returns one packaged answer, use `emem_ask` instead.
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Matching MCP Servers

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    A Model Context Protocol server implementation that enables AI assistants to interact with Slack workspaces, allowing them to browse channels, send messages, reply to threads, add reactions, and retrieve user information.
    Last updated
    9
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    Apache 2.0
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    Enables AI agents to interact with Slack through 20 specialized tools and resources for managing channels, messages, users, and files. It features built-in rate limit handling, safety controls for message sending, and full support for Slack Block Kit formatting.
    Last updated
    MIT

Matching MCP Connectors

  • Jira MCP Pack

  • Slack MCP for self-host or managed Cloud, with Gemini CLI and secure-default HTTP.

  • Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")
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  • Return statistics about the session-scoped resource cache. Useful for verifying that caching is working: call get_synset_info (or similar) twice for the same ID and check that cache_size grows by 1 on the first call but not on the second, and that cached_keys contains the expected IDs. Returns: Dict with: - cache_size: Total number of cached entries - cached_keys: List of (base_url, resource_id) pairs currently cached
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  • Create an authenticated server session and return a `sessionId` for subsequent tool calls. Default mode is wallet signature login for platform tools; secondary mode is `apiKey` login for internal tools. For wallet login, ALWAYS call `tronsave_get_sign_message` first, sign that exact message client-side, then call `tronsave_login` with `signature_timestamp` in exact format `<signature>_<timestamp>` (signature and timestamp joined by `_`). Use returned `sessionId` as `mcp-session-id` on every subsequent request.
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  • Run a generic M/M/c queue simulation. Provide an arrival rate (λ, arrivals/hour), a service rate per server (μ, customers/hour each server can finish), and a server count (c). Optional: distribution shapes, service coefficient of variation, run length. Returns per-hour metrics and an overall summary (avg wait, queue length, offered load, throughput). This is the primary tool for 'how many servers do I need?' / 'what's my average wait?' style questions. ALSO preferred over simulate_scenario for what-if questions about scheduled scenarios (Coffee Shop, ER) when the user wants flat uniform numbers — pull the peak params from describe_scenario and run them here. That usually matches user intent better than collapsing a schedule.
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  • Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Recommends a complete stack from BuyAPI's corpus with a structured decision matrix, cost estimate, assumptions, unknowns, alternatives, and sources. Use this when the user is starting a project or asks for a complete stack choice. Do not use this for local coding/debugging/docs questions that do not involve software or vendor selection. Do not call vendors.resolve first; this tool handles retrieval and ranking.
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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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  • Connectivity check — returns server version and current timestamp. Use to verify MCP server is reachable before calling other tools.
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  • Fetch HTTP response headers for a URL. Use when inspecting server configuration, security headers, or caching policies.
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  • Check server health and connectivity. Returns: Dictionary with health status including: - status: "healthy" or "unhealthy" - version: Server version - environment: Current environment (dev/staging/prod)
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  • Find recipes using natural language search. Use this tool when: - User refers to a recipe by partial name, description, or keywords (e.g., "run my GitHub PR recipe", "the slack notification one") - User wants to find a recipe but doesn't know the exact name or ID - You need to find a recipe_id before executing it with RUBE_EXECUTE_RECIPE The tool uses semantic matching to find the most relevant recipes based on the user's query. Input: - query (required): Natural language search query (e.g., "GitHub PRs to Slack", "daily email summary") - limit (optional, default: 5): Maximum number of recipes to return (1-20) - include_details (optional, default: false): Include full details like description, toolkits, tools, and default params Output: - successful: Whether the search completed successfully - recipes: Array of matching recipes sorted by relevance score, each containing: - recipe_id: Use this with RUBE_EXECUTE_RECIPE - name: Recipe name - description: What the recipe does - relevance_score: 0-100 match score - match_reason: Why this recipe matched - toolkits: Apps used (e.g., github, slack) - recipe_url: Link to view/edit - default_params: Default input parameters - total_recipes_searched: How many recipes were searched - query_interpretation: How the search query was understood - error: Error message if search failed Example flow: User: "Run my recipe that sends GitHub PRs to Slack" 1. Call RUBE_FIND_RECIPE with query: "GitHub PRs to Slack" 2. Get matching recipe with recipe_id 3. Call RUBE_EXECUTE_RECIPE with that recipe_id
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  • Evaluate any MCP service for trustworthiness before spending money on it. Connects to the target server, checks reachability, governance declarations, tool definition quality, and audit endpoints. Returns a trust score from 0 to 100 with a recommendation: PROCEED, PROCEED WITH CAUTION, HIGH RISK, or DO NOT TRANSACT. No API key needed.
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  • Search O*NET occupations by keyword. Returns a list of occupations matching the keyword with their SOC codes, titles, and relevance scores. Use the SOC code from results with other O*NET tools to get detailed information. Args: keyword: Search term (e.g. 'software developer', 'nurse', 'electrician'). limit: Maximum number of results to return (default 25).
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