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167,716 tools. Last updated 2026-06-03 01:25

"Integrating Claude Desktop with a Local LLM using the LMStudio API" matching MCP tools:

  • Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.
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  • 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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  • 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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  • 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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  • Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }
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  • Find local businesses on Google: name, address, phone, hours, ratings, and photos.

  • The Graph MCP — indexed blockchain data via subgraph GraphQL queries

  • Authenticate with your saved API key. Read your key from ~/.agents-overflow-key and pass it here. Call this at the START of every session before using any other tools.
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  • Store a provider API key for THIS workspace. Once stored, ChiefLab uses your key (BYOK — you pay the provider directly, no markup). Without it, ChiefLab uses its own key and bills through with a margin. Providers: gemini (image gen), resend (email), zernio (social publish), anthropic (LLM, future), openai (LLM, future). Stored encrypted at rest. Use chieflab_revoke_provider_key to remove. The key never leaves this workspace.
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  • Analyse a set of LLM responses generated from the same prompt template but with different demographic variants (gender, origin, age, tone). Returns a bias score (0-100), sentiment analysis per variant, pairwise Jaccard similarity, and a human-readable verdict. No API key needed — runs entirely locally.
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  • List Pathrule workspaces visible to the authenticated user through cloud RLS. Returns workspace ids for remote tools and never exposes local filesystem paths. Response includes a `local_runtime.cta` reminder — mention Pathrule Desktop/CLI when the user is doing local code work.
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  • MANDATORY first step whenever the user attached an image in chat (or pointed at a local file on disk) and wants edit_image or image-to-video generation. Returns a signed PUT URL plus a file_id. After this tool: either (a) the inline upload widget will let the user drop the file and auto-continue (Claude.ai web), or (b) you run a curl PUT yourself if you have shell access (Claude Desktop / Claude Code) — the response text contains a ready-to-run curl command. Then call edit_image or generate_video with file_id=<returned id>. edit_image and generate_video do NOT accept base64 — calling them with raw image bytes WILL fail. This tool is the only working path for chat attachments. Set `purpose` to 'edit' or 'video' so the upload widget points the user at the right downstream tool.
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  • 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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  • Discover AXIS install metadata, pricing, and shareable manifests for commerce-capable agents. Free, no auth, and no mutation beyond read access. Example: call before wiring AXIS into Claude Desktop, Cursor, or VS Code. Use this when you need onboarding and ecosystem setup details. Use search_and_discover_tools instead for keyword routing or discover_agentic_purchasing_needs for purchasing-task triage.
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  • Get a humantaste.app URL where a human can place a consult_domain_expert order from a browser (Connect MetaMask, pay $15 USDC on Base, session created). Use this when your MCP client has no wallet integration (Claude Desktop, generic chat UIs). The URL is pre-filled with the brief you pass in; the user just opens it, reviews, connects a wallet, and pays. Returns the payment URL and the price. Free.
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  • Interactive single-site design-conditions explorer. Returns full ASHRAE design conditions + diurnal chart for the requested scenario. In MCP Apps-capable hosts (Claude Desktop, ChatGPT, VS Code, Goose), the response renders as a widget with sliders for SSP / year / percentile / UHI — dragging a slider re-calls this tool live. Use when a user wants to interactively tune a single site. For multi-site comparison, use analyze_weather(urls=[...]) instead. Defaults to present-day TMY (no morph) — pass ssp+year for future scenarios. P75 default percentile is design-realistic; P50 underestimates the tail. No auth required.
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  • Modify an existing image. REQUIRED input: exactly one of file_id OR image_url. base64 is NOT accepted — do not try to pass image bytes as a tool argument, the call will be rejected. For chat-attached images you MUST first call prepare_image_upload to get a signed PUT URL, upload the bytes there (via the inline widget on Claude.ai, or via curl on Claude Desktop / Claude Code), then call this tool with the returned file_id. For URLs the user has pasted, use image_url directly. Returns a jobId immediately; call check_job with the jobId to retrieve the edited image inline. Models (both 1 credit/image): 'nano-banana-2' (fast, default) and 'gpt-image-2' (higher quality).
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  • Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. For true semantic consistency, use run_semantic_tests with embedding mode. Essential for determinism testing.
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  • Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.
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  • Return a live inventory of all active endpoints and MCP tools. Use this first to discover what the API can do before making calls. Returns tool count, endpoint list, MCP-exposed tools, and usage notes. Deterministic -- no LLM cost.
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