"Utilizing Local LLMs for Query Preprocessing with Minions Framework" matching MCP tools:

• discovery_status

  Disco

  Check the status of a Disco run. Returns current status and progress details: - status: "pending" | "processing" | "completed" | "failed" - job_status: underlying job queue status - queue_position: position in queue when pending (1 = next up) - current_step: active pipeline step (preprocessing, training, interpreting, reporting) - estimated_wait_seconds: estimated queue wait time in seconds (pending only) Poll this after calling discovery_analyze. Use discovery_get_results to fetch full results once status is "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

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• create_snapshot

  BorealHost

  Create a local container snapshot (async). Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed" or "failed". Available for VPS, dedicated, and cloud plans (any plan with max_snapshots > 0). Local snapshots are stored on the host disk and count against disk quota. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "snap-...", "status": "creating", "storage_type": "local", "message": "Snapshot started. Poll list_snapshots() to check status."} Errors: VALIDATION_ERROR: Max snapshots reached or insufficient disk quota

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• query_registry

  Arclan MCP Registry

  Search the Arclan registry for MCP servers. By default returns only connectable servers (active, mcp_partial, auth_gated). Use status=stdio to browse local-only servers available for installation. Use status=all to query the full index. Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%. Use read_only=true to restrict to servers with no write or exec tools. Use this before connecting to an MCP server to check its validation status and score. After using a server, call report_server to contribute reliability data.

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• pathrule_resolve_refresh

  Pathrule

  Close a Pathrule refresh task after reviewing its brief. Normal remote flow: call pathrule_list_pending_refreshes, then pathrule_get_refresh_brief, then use this tool with status='rejected' when the signal is stale or not actionable. Remote MCP may refuse status='applied' because it cannot verify local source files; use Pathrule Studio/CLI for applied resolutions that require local verification.

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• get_saved_visual_idea_preset

  Heista

  Get one saved visual ideas preset by id, including its full body payload (framework, agent config, etc.). Call the matching list tool first to discover ids. Free, read-only.

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• get_project_context

  Zephex

  Orient on any codebase before editing. One focused slice per call — 11 topics: identity, framework, backend, frontend, database, auth, deploy, run, structure, integrations, security. Each topic returns different fields (focus, summary, data, hint, related_topics, next_calls, meta). Sources: (1) local absolute path — stdio MCP reads disk directly, e.g. /Users/alice/myapp; (2) GitHub/GitLab URL — hosted server clones once and caches, e.g. https://github.com/owner/repo; (3) inline_files when transport has no filesystem. Workflow: get_project_context({ topic: "identity" }) first, then 1-2 related_topics. DO NOT use for function bodies (read_code), search (find_code), or flows (explain_architecture). Read-only.

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• discovery_estimate

  Disco

  Estimate the credits required to run a Disco analysis. Returns `required_credits` for public (always 0) and private, with private split by whether LLMs are enabled (use_llms=False is faster, use_llms=True adds smarter preprocessing, literature context and a written summary). Also returns per-visibility depth caps and accepted file formats. No authentication required — when an API key is supplied, also returns the caller's available credits. Call this before discovery_analyze whenever cost or feasibility is unclear. Args: file_size_mb: Size of the dataset in megabytes. num_columns: Number of columns in the dataset. analysis_depth: Search depth (1=fast, higher=deeper). Used to compute the private-run cost. Default 2. api_key: Disco API key (disco_...). Optional. When provided, the response includes `account.available_credits`.

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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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• upload_media

  SendIt

  FOR CLAUDE DESKTOP ONLY (with filesystem access). For Claude.ai/web: Use create_upload_session instead - it provides a browser upload link. Upload local media to cloud storage, returning a public HTTPS URL. WHEN TO USE: • Instagram, LinkedIn, Threads, X: REQUIRED for local files before calling publish_content • TikTok: NOT NEEDED - pass local path directly to publish_content SUPPORTED FORMATS: • Images: jpg, png, gif, webp (max 10MB) • Videos: mp4, mov, webm (max 100MB) Returns { url: 'https://...' } for use in publish_content mediaUrl parameter.

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• ship_project

  shipyard

  Publish a new project to Shipyard under your account. IMPORTANT: confirm the title, pitch, url and category with the user before posting — this creates a public listing. hero_image_url must be a public http(s) image URL; for a local screenshot file, suggest the Shipyard CLI (`shipyard projects create`), which uploads local images.

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• switch_dannet_server

  dannet

  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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• get_framework_docs

  main

  Retrieves authoritative documentation directly from the framework's official repository. ## When to Use **Called during i18n_checklist Steps 1-13.** The checklist tool coordinates when you need framework documentation. Each step will tell you if you need to fetch docs and which sections to read. If you're implementing i18n: Let the checklist guide you. Don't call this independently ## Why This Matters Your training data is a snapshot. Framework APIs evolve. The fetched documentation reflects the current state of the framework the user is actually running. Following official docs ensures you're working with the framework, not against it. ## How to Use **Two-Phase Workflow:** 1. **Discovery** - Call with action="index" to see available sections 2. **Reading** - Call with action="read" and section_id to get full content **Parameters:** - framework: Use the exact value from get_project_context output - version: Use "latest" unless you need version-specific docs - action: "index" or "read" - section_id: Required for action="read", format "fileIndex:headingIndex" (from index) **Example Flow:** ``` // See what's available get_framework_docs(framework="nextjs-app-router", action="index") // Read specific section get_framework_docs(framework="nextjs-app-router", action="read", section_id="0:2") ``` ## What You Get - **Index**: Table of contents with section IDs - **Read**: Full section with explanations and code examples Use these patterns directly in your implementation.

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• get_adoption_stage

  Stratalize Governance

  Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.

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• get_workload_logs

  Control Plane

  Query workload logs from a GVC. Provide structured params (gvc, workload, container, location, filter) OR a raw LogQL `query` — a raw query REPLACES the structured params, so it must embed ALL labels itself. Available labels: gvc, workload, container, location, provider, replica, stream — replica and stream are only reachable via a raw query. `filter` is a literal substring match (|=), not regex; for regex use a raw query with |~. Cron workload? Get jobExecutions via list_deployments (with `location`), then re-query with a raw query scoping replica= plus the execution's time window — embed gvc/workload/location labels in the raw query. Returns structured JSON with timestamps, messages, and labels. Recommended reading before first use: get_cpln_skill("workload-troubleshooting") — the runbook for this tool family (read once per session).

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• get_project_context

  main

  Captures the user's project architecture to inform i18n implementation strategy. ## When to Use **Called during i18n_checklist Step 1.** The checklist tool will tell you when to call this. If you're implementing i18n: 1. Call i18n_checklist(step_number=1, done=false) FIRST 2. The checklist will instruct you to call THIS tool 3. Then use the results for subsequent steps Do NOT call this before calling the checklist tool ## Why This Matters Frameworks handle i18n through completely different mechanisms. The same outcome (locale-aware routing) requires different code for Next.js vs TanStack Start vs React Router. Without accurate detection, you'll implement patterns that don't work. ## How to Use 1. Examine the user's project files (package.json, directories, config files) 2. Identify framework markers and version 3. Construct a detectionResults object matching the schema 4. Call this tool with your findings 5. Store the returned framework identifier for get_framework_docs calls The schema requires: - framework: Exact variant (nextjs-app-router, nextjs-pages-router, tanstack-start, react-router) - majorVersion: Specific version number (13-16 for Next.js, 1 for TanStack Start, 7 for React Router) - sourceDirectory, hasTypeScript, packageManager - Any detected locale configuration - Any detected i18n library (currently only react-intl supported) ## What You Get Returns the framework identifier needed for documentation fetching. The 'framework' field in the response is the exact string you'll use with get_framework_docs.

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• list_service_types

  LocalPro

  List the valid service type categories for a given niche directory. Use this before calling search_providers with a service_type filter to ensure you pass a valid value. Each niche has its own taxonomy — for example, "coated-local" has epoxy, polyaspartic, metallic_epoxy, etc., while "radon-local" has radon_testing, radon_mitigation, ssd_installation, etc.

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• contacts_find

  DialogBrain

  👤 Search for contacts in your address book by name or username. When to use: - User asks 'find contact X' or 'who is Y?' - User wants to know someone's username or ID - Before sending a message to verify contact exists - To get contact's channel reference for messaging Examples: ❓ User: 'find contact named [name]' → contacts_search(query='[name]', limit=5) ❓ User: 'who is [full name]?' → contacts_search(query='[full name]', limit=1) ❓ User: 'search for @username' → contacts_search(query='username', limit=10) Returns: name, username, channel, channel_ref, similarity_score, match_type. Plus: - entity_id: local DB key — pass to contacts.profile. Null for live-discovered contacts (skip contacts.profile for those). - telegram_user_id (when channel='telegram'): the Telegram user ID — pass to calls.make / messages.send. NOT entity_id.

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• recommend_framework

  jeda-ai

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

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• generate_framework_analysis

  jeda-ai

  Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }

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