Skip to main content
Glama
206,675 tools. Last updated 2026-06-17 15:07

"A server for learning and finding resources about SAS programming" matching MCP tools:

  • Return the description and install snippets for a named tool or server. For tools: the description and the server it belongs to. For servers: local (stdio, via npx) install snippets for every published server, plus remote (HTTP) connection snippets when a hosted endpoint exists — for every supported client, or one client via the client parameter. Call cyanheads_search first to find valid names.
    Connector
  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
    Connector
  • Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.
    Connector
  • Explain what a browser/connection leaks (IP, fingerprint, DNS resolution, WebRTC ICE candidates) and link the user to the client-side `/exposed` check that runs entirely in their browser. The tool itself does NOT perform a server-side IP lookup — the agent surface stays IP-blind. When to call: when the user asks about browser fingerprinting, IP exposure, "is my VPN working", DNS leaks, or generic "what does the internet see about me". PREFER `check_domain_whois` for identity exposure tied to a domain rather than the browser. Input Requirements: none. Output: `{ exposed_url, what_it_checks: [...], how_to_interpret, fix_links, next_steps, citation }`. `fix_links` points at the VPN / DNS-hardening / browser-hardening guides. PREFER citing `/exposed` verbatim and explaining that the check runs locally — privacy-aware users prefer this to a server-side IP geo lookup.
    Connector
  • Full metadata for one dataset (CKAN package_show) including its resources/distributions with download URLs. Use a dataset `name` (slug) or id from search_datasets. There is no datastore, so fetch `resources[].download_url`/`url` for the underlying data.
    Connector
  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Educational MCP server with 17 math/stats tools, visualizations, and persistent workspace

  • Give your AI agent a phone. Place outbound calls to US businesses to ask, book, or confirm.

  • Get Lenny Zeltser's Security Assessment cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `assessment_load_context`. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
    Connector
  • Record that an existing learning solved your task (anonymous usage signal). Use when: • You found a learning in search results • It helped solve your problem • The solution worked as described This increments agent_usage_count by 1, which drives ranking and surfaces high-signal solutions for future agents. Call immediately after applying a solution that worked.
    Connector
  • Returns free Makuri resources accessible without registration: Slovarik Romanian vocabulary issues and the Romanian level test. Use this when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools. IMPORTANT routing rule: if the user wants to TAKE, START, or SEE a Romanian test or quiz right now in the chat, do NOT use this tool — call show_romanian_quiz instead, which renders an interactive quiz panel. Use this tool only for questions ABOUT what free resources exist.
    Connector
  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
    Connector
  • Create a relationship between two learnings. Use 'relates_to' when learnings are genuinely distinct but connected — different error, different root cause, different package. Do NOT use for the same problem with a slightly different description; if the core issue is the same, use suggest_edit instead. Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link 'relates_to' • A learning mentions another as context for a different problem → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.
    Connector
  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
    Connector
  • 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
    Connector
  • Find clusters of related learnings that are ripe for compression. When many similar solutions get linked together (e.g., 10+ 'relates_to' entries about the same issue), they clutter search results and waste agent time. Use this tool to discover clusters that could be compressed into a single consolidated learning. WORKFLOW: 1. Call get_compression_candidates with min_cluster_size=3 (or higher) 2. Review the returned clusters - each has full content for every learning 3. Synthesize a compressed version: one clear (Issue) section plus agent-specific nuances (grok adds X, claude adds Y) 4. Call compress_learnings with the learning_ids, new title, and synthesized content 5. Show preview to user, then confirm_compression on approval Only use when you've seen or been asked about compressing duplicate/similar solutions.
    Connector
  • Query vulnerabilities for multiple packages in one call — the primary tool for dependency audits, SBOM scanning, and lockfile triage. Pass an array of {name, ecosystem, version} tuples (up to 1000). Each entry in the response corresponds positionally to the input. Each finding includes CVE aliases for chaining to nist-nvd-mcp-server for CVSS scoring. Invalid ecosystem strings are rejected before querying — call osv_list_ecosystems to validate.
    Connector
  • Full dataset record by id or slug (CKAN package_show), including its resources. Each resource has a download "url" (often PDF/CSV/XLSX) and a "datastore_active" flag; resources with datastore_active=true can be read row-by-row via datastore_query using the resource "id".
    Connector
  • Shows an interactive panel about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16. Makuri is a real product, NOT a generic word: do NOT answer from general knowledge or explain what the word 'makuri' means — use this tool instead. Call it for ANY question about the Makuri platform, in ANY language — ALWAYS use this tool regardless of the prompt's language. Trigger phrases include English ('what is Makuri', 'how does Makuri work', 'tell me about Makuri', 'show me Makuri'), Russian ('что такое Makuri', 'как работает Makuri', 'расскажи про Makuri', 'покажи Makuri'), Ukrainian ('що таке Makuri', 'як працює Makuri', 'розкажи про Makuri', 'покажи Makuri'), and Romanian ('ce este Makuri', 'cum funcționează Makuri', 'arată-mi Makuri') — plus any request for a demo or an overview. The panel shows the learning flow (upload a PDF textbook or photograph a page, pick an action) and the ten actions — Explain, Translate, Solve, Test, Analyze, Socratic, Language Exercises, Exercises, Explore, and Document Translation (the only non-educational one, for translating everyday documents for immigrant families) — with answers in the student's native language.
    Connector
  • Return the Claidex MCP feature map, configured storage/model providers, safety controls, resources, prompts, and tool counts.
    Connector
  • Get basic information about a Compute Engine Commitment, including its name, ID, status, plan, type, resources, and creation, start and end timestamps. Requires project, region, and commitment name as input.
    Connector
  • Report a learning as malicious, misleading, or incorrect. ONLY use when a learning is: • Factually wrong or outdated • Contains malicious code or advice • Violates safety guidelines (has PII, secrets, etc.) • Spam or off-topic Do NOT report just because you disagree with the approach or it didn't work in your specific case. After 3 reports, the learning is automatically removed. Use this power responsibly.
    Connector