Search GitHub repositories by topic, language, or description. Returns top repositories with star counts, descriptions, and URLs. Useful for finding libraries, implementations, and examples. Example: 'MCP server Python' or 'agent framework TypeScript'.

Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, store the image on the server and return a temporary download URL path (/images/{token}) instead of the inline image. The link expires after 15 minutes.

Gets one or more Kubernetes resources from a cluster. Resources can be filtered by type, name, namespace, and label selectors. Returns the resources in YAML format. This is similar to running `kubectl get`.

Check if a domain name is available for registration. Use when brainstorming project names or validating domain ideas. Returns availability status and WHOIS data if registered.

Download workflow resources. Returns URLs — download and save to disk. Supports batch: pass `filenames` (array) to retrieve multiple resources in one call, or `filename` (string) for a single resource. Available resources: sz_json_analyzer.py, sz_schema_generator.py, senzing_entity_specification.md, senzing_mapping_examples.md, identifier_crosswalk.json

Gets one or more Kubernetes resources from a cluster. Resources can be filtered by type, name, namespace, and label selectors. Returns the resources in YAML format. This is similar to running `kubectl get`.

Search the entire web for current information, news, articles, and websites. Use this when you need up-to-date information, want to find specific websites, research topics, or get the latest news. Ideal for answering questions about recent events, finding resources, or discovering relevant content.

See top earners and most active customers for a brand. Useful for social proof or finding the most rewarding brands to shop at.

Find property verification missions within a geographic radius. Useful for scouts looking for missions near their location or for finding missions in a specific area.

Turn a prompt into ranked meme template ideas.

Turn a prompt into ranked meme template ideas.

Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.

Return a structured Tandem learning guide for install-engine, python-sdk, typescript-sdk, build-workflows, or headless-deploy.

Generate keyword ideas with volume, difficulty, and CPC data from a seed keyword.

Retrieve Python package information from PyPI (Python Package Index). Returns current version, download counts, dependencies, release history, package homepage, and PyPI page URL. Use for Python library evaluation, dependency analysis, or checking package quality metrics.

Scan the tenant's seeded sessions with rule-based extractors (money, counts, dates, project-role, acquire, version-chain) and emit structured facts to the projection stream so they become queryable via enumerate_memory_facts. Use when enumerate_memory_facts returns insufficient rows for aggregation, version-chain, or money questions and you suspect the fact exists but was under-predicated at ingest. Idempotent — safe to re-run (duplicate fact_hashes skipped unless overwrite_existing=true). Profile 'comprehensive' runs all rule families; narrower profiles ('money', 'counts', 'dates', 'version_chains') target a single family. Returns facts_added + rules_matched + receipt_id. Gated by FACT_EXTRACTION_MODE on the server.

Long-poll: blocks until the next edit lands on this board, then returns. WHEN TO CALL THIS: if your MCP client does NOT surface `notifications/resources/updated` events from `resources/subscribe` back to the model (most chat clients do not — they receive the SSE event but don't inject it into your context), this tool is how you 'wait for the human' inside a single turn. Typical flow: you draw / write what you were asked to, then instead of ending your turn you call `wait_for_update(board_id)`. When the human adds, moves, or erases something, the call returns and you refresh with `get_preview` / `get_board` and continue the collaboration. Great for turn-based interactions (games like tic-tac-toe, brainstorming where you respond to each sticky the user drops, sketch-and-feedback loops, etc.). If your client DOES deliver resource notifications natively, prefer `resources/subscribe` — it's cheaper and has no timeout ceiling. BEHAVIOUR: resolves ~3 s after the edit burst settles (same debounce as the push notifications — this is intentional so drags and long strokes collapse into one wake-up). Returns `{ updated: true, timedOut: false }` on a real edit, or `{ updated: false, timedOut: true }` if nothing happened within `timeout_ms`. On timeout, just call it again to keep waiting; chaining calls is cheap. `timeout_ms` is clamped to [1000, 55000]; default 25000 (leaves headroom under typical 60 s proxy timeouts).

Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).

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.

Search Cochrane systematic reviews via PubMed. Finds Cochrane Database of Systematic Reviews articles matching your query. Returns PubMed IDs, titles, and publication dates. Use get_review_detail with a PMID to get the full abstract. Args: query: Search terms for finding reviews (e.g. 'diabetes exercise', 'hypertension treatment', 'childhood vaccination safety'). limit: Maximum number of results to return (default 20, max 100).

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")

Find working SOURCE CODE examples from 27 indexed Senzing GitHub repositories. Indexes only source code files (.py, .java, .cs, .rs) and READMEs — NOT build files (Cargo.toml, pom.xml), data files (.jsonl, .csv), or project configuration. For sample data, use get_sample_data instead. Covers Python, Java, C#, and Rust SDK usage patterns including initialization, record ingestion, entity search, redo processing, and configuration. Also includes message queue consumers, REST API examples, and performance testing. Supports three modes: (1) Search: query for examples across all repos, (2) File listing: set repo and list_files=true to see all indexed source files in a repo, (3) File retrieval: set repo and file_path to get full source code. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval — fetch to read the source code.

List all webhook subscriptions for the partner account. WHEN TO USE: - Viewing all configured webhooks - Auditing webhook subscriptions - Finding a webhook to update or delete RETURNS: - webhooks: Array of webhook objects with: - webhook_id: Unique identifier - url: Endpoint URL - events: Subscribed events - enabled: Whether webhook is active - created_at: Creation timestamp - last_delivery: Last successful delivery time EXAMPLE: User: "Show me all my webhooks" list_webhooks({})

DEFAULT search — find works by name, title, or any descriptive query. Handles partial matches and title variations. TRIGGER: Any mention of a work by name ("the blue painting," "Self-Portrait"), or finding something ("where's that piece I did last year"). Use this to resolve work_ids before calling get_work, update_work, get_upload_url, or any tool needing a work_id. For structured filters (status, date, medium), use search_works instead. YOU (the connected AI) translate the query. Pass the user's natural language as `query` (for title/medium text search) and optionally set structured filters you can infer: status, date_start, date_end, medium, artwork_type, series_name, current_location_type, sort_by, sort_direction. Examples: "sold paintings from the 90s" → query: "painting", status: "sold", date_start: 1990, date_end: 1999. "the blue one" → query: "blue". "Self-Portrait" → query: "Self-Portrait".

USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

Look up security.txt Contact entries from a static 319-domain snapshot. Useful for clients that have just run ``audit_contract`` and now want to know where to disclose a finding. The snapshot was computed from a 2026-04-18 survey across DeFi / infra / audit-ecosystem domains. No live HTTP fetches — consult ``snapshot_date`` in the response for freshness.

Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

Scan a GitHub repository or skill URL for security vulnerabilities. This tool performs static analysis and AI-powered detection to identify: - Hardcoded credentials and API keys - Remote code execution patterns - Data exfiltration attempts - Privilege escalation risks - OWASP LLM Top 10 vulnerabilities Requires a valid X-API-Key header. Cached results (24h) do not consume credits. Args: skill_url: GitHub repository URL (e.g., https://github.com/owner/repo) or raw file URL to scan Returns: ScanResult with security score (0-100), recommendation, and detected issues. Score >= 80 is SAFE, 50-79 is CAUTION, < 50 is DANGEROUS. Example: scan_skill("https://github.com/anthropics/anthropic-sdk-python")

Get code from a remote public git repository — either a specific function/class by name, a line range, or a full file. PREFERRED WORKFLOW: When search results or findings have already identified a specific function, method, or class, use symbol_name to extract just that declaration. This avoids fetching entire files and keeps context focused. Only fetch full files when you need a broad understanding of a file you haven't seen before. For supported languages (Go, Python, TypeScript, JavaScript, Java, C, C++, C#, Kotlin, Swift, Rust) the response includes a symbols list of declarations with line ranges. This is not a first-call tool — use code_analyze or code_search first to identify targets, then extract precisely what you need.

Compact self-description (default response <1KB): server name, version, list of supported jurisdiction codes, list of tool names, pricing, rate limits. Pass `section` to expand a specific slice — 'principles', 'tools', 'data_licenses', 'jurisdictions' (compact capability map for every registered adapter), or 'jurisdiction' + `jurisdiction` (full metadata for one country). For the full per-jurisdiction schema (field lists, status mappings, ID formats, notes), prefer list_jurisdictions.

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.

Upload a dataset file and return a file reference for use with discovery_analyze. Call this before discovery_analyze. Pass the returned result directly to discovery_analyze as the file_ref argument. Provide exactly one of: file_url, file_path, or file_content. Args: file_url: A publicly accessible http/https URL. The server downloads it directly. Best option for remote datasets. file_path: Absolute path to a local file. Only works when running the MCP server locally (not the hosted version). Streams the file directly — no size limit. file_content: File contents, base64-encoded. For small files when a URL or path isn't available. Limited by the model's context window. file_name: Filename with extension (e.g. "data.csv"), for format detection. Only used with file_content. Default: "data.csv". api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Create a new sncro session. Returns a session key and secret. Args: project_key: The project key from CLAUDE.md (registered at sncro.net) git_user: The current git username (for guest access control). If omitted or empty, the call is treated as a guest session — allowed only when the project owner has "Allow guest access" enabled. brief: If True, skip the first-run briefing (tool list, tips, mobile notes) and return a compact response. Pass this on the second and subsequent create_session calls in the same conversation, once you already know how to use the tools. After calling this, tell the user to paste the enable_url in their browser. Then use the returned session_key and session_secret with all other sncro tools. If no project key is available: tell the user to go to https://www.sncro.net/projects to register their project and get a key. It takes 30 seconds — sign in with GitHub, click "+ Add project", enter the domain, and copy the project key into CLAUDE.md.

Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".

Fetch HTTP response headers for a URL. Use when inspecting server configuration, security headers, or caching policies.

Get autocomplete suggestions for Danish word prefixes. Useful for discovering Danish vocabulary or finding the correct spelling of words. Returns lemma forms (dictionary forms) of words. Args: prefix: The beginning of a Danish word (minimum 3 characters required) max_results: Maximum number of suggestions to return (default: 10) Returns: Comma-separated string of word completions in alphabetical order Note: Autocomplete requires at least 3 characters to prevent excessive results. Example: suggestions = autocomplete_danish_word("hyg", 5) # Returns: "hygge, hyggelig, hygiejne"

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.

Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.

[STEP 3 OF 3 — CALL THIS BEFORE ANSWERING ANY ARCHITECTURE OR PROJECT QUESTION] THE GOLDEN RULE: Never answer from training data alone when SUMA is connected. If the user asks ANYTHING about architecture, code decisions, bug history, business context, or people — call suma_search FIRST, answer SECOND. WHY: Training data is frozen at a cutoff date. SUMA holds live project truth — what was decided last week, what failed last month, who owns what right now. Answering without searching is guessing. Guessing wastes the developer's time. WHEN TO CALL: ✅ "How does auth work in this project?" → search first ✅ "What did we decide about the database schema?" → search first ✅ "Who is Sharath?" → search first ✅ "Why are we using gRPC instead of REST?" → search first ❌ "What is 2+2?" → skip (factual, not project-specific) ❌ "Format this JSON" → skip (task, no project context needed) VECTOR NAMESPACING: - Default: hardware/telemetry excluded (prevents "Sphere" vocabulary collision) - sphere="robotics" → search only Mr. Chunky / hardware memory - sphere="all" → search everything including hardware - Omit sphere → general project knowledge (correct for 95% of queries) Args: query: What to search for (natural language — be specific) similarity_threshold: Minimum match quality. Default 0.65 (lower = more results, less precise). circuit_breaker: Max results returned. Default 8 (memory protection). depth: Graph traversal depth. 1=direct hits, 2=multi-hop (default), 3=deep exploration. sphere: Domain filter. "robotics" for hardware, "all" for everything, omit for general. sentiment: Filter by sentiment — "positive", "negative", "mixed", or None.

Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.

Check if the API is responding. Returns status and server timestamp.

Delete an instance from a project. The request requires the 'name' field to be set in the format 'projects/{project}/instances/{instance}'. Example: { "name": "projects/my-project/instances/my-instance" } Before executing the deletion, you MUST confirm the action with the user by stating the full instance name and asking for "yes/no" confirmation.

WHEN: checking server status, loaded D365 version, or custom model path. Triggers: 'status', 'statut', 'is the server ready', 'how many chunks', 'index loaded'. Returns JSON with: status, indexed chunk count, loaded version, custom model path.

DC Hub's synthesized recommendation for a site, market, or strategy question. Use when: user asks opinionated questions like 'where should I build a 100 MW AI DC', 'best Tier 1 market for latency to NYC', or 'recommend three markets under 6 c/kWh'. Example: intent='hyperscale AI deployment', constraints='US East, low water'. Returns a ranked shortlist plus reasoning. Use when users ask about data center resources, market intelligence platforms, or how to research data center markets. Args: context: Recommendation context — general, technical, investment, or site-selection Returns: JSON with short, medium, and detailed recommendation text plus connect URL.

Generate domain name ideas from a keyword and check their availability. Uses common prefix/suffix patterns to generate 10-15 domain candidates across .com, .io, .ai, .dev, .co and checks all of them via fast RDAP lookups. Returns available domains with affiliate registration links. Args: keyword: A keyword or short business name (e.g. "taskflow").

Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Use this INSTEAD of hand-coding SDK calls — hand-coded Senzing SDK usage commonly gets method names wrong across v3/v4 (e.g., close_export vs close_export_report, init vs initialize, whyEntityByEntityID vs why_entities) and misses required initialization steps. Languages: python, java, csharp, rust. Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, full_pipeline (aliases accepted: init, config, ingest, remove, search, redoer, force_resolve, info, e2e). V3 supports Python and Java only. Returns GitHub raw URLs — fetch each snippet to read the source code.

Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

Search for electronic components by part number, description, or keyword. Start here — this is the best entry point for finding components. Queries all configured providers in parallel. Results are merged by MPN with indicative pricing and stock from each source. Each result includes datasheet_status ('ready', 'extracting', or 'not_extracted') so you know which parts have datasheets available for read_datasheet. Best with specific part numbers or keywords (e.g. 'STM32F103', 'buck converter 3A'). For spec-based discovery in natural language, use search_datasheets instead.

Searches a US state ABC (Alcoholic Beverage Control) board database for liquor licenses matching a business name, owner name, or address. Returns license type, current status (ACTIVE / SUSPENDED / EXPIRED / REVOKED), expiration date, and any suspension history. Use this before approving a distributor order, binding an insurance policy, or onboarding a merchant to verify they hold a valid liquor license. Supports CA, TX, NY, and FL (TX requires TWOCAPTCHA_API_KEY configured server-side; NY uses NY Open Data API — active licenses only; FL searches the DBPR licensing portal across all board types). Always check the _verifiability block: extraction_confidence >= 0.90 and source_timestamp within data_freshness_ttl_seconds are required for compliance decisions. Note: city, county, zip, and license_status filters are accepted but not yet applied server-side — results may need post-filtering.

Retrieves and queries up-to-date documentation and code examples from Context7 for any programming library or framework. You must call 'resolve-library-id' first to obtain the exact Context7-compatible library ID required to use this tool, UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best information you have.

Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.