"Diagnosing Issues with My Remote Ubuntu Server Configuration" matching MCP tools:

• check_text

  WSC - Writing Style Checker

  Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

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• compare_separate_vs_pooled

  QueueSim

  Run the classic operations-research teaching demo: pooled queueing (one shared queue, c servers) vs separate queues (c independent queues, one server each, λ/c traffic to each). Both runs have identical total capacity (c × μ) and identical total arrivals (λ), so the offered load ρ is the same; the only structural difference is whether arrivals share a queue or split into c isolated streams. The pooled configuration ALWAYS produces shorter waits — that's the whole teaching point. Use this when the user asks 'should we pool our resources?' / 'should we cross-train?' / 'why do banks have one line instead of c?' / 'what's the cost of siloing my call center into specialist queues?'. Returns both runs side by side with the pooled-vs-separate wait delta. ANTI-FABRICATION: numbers come from two real DES runs. Quote them VERBATIM.

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• list_databases

  shiply

  List the SQL databases (D1 or Neon Postgres) on my account, including which owned site (if any) each is attached to. Call this BEFORE db_query/db_schema-style work to discover a databaseId — those live on a per-database MCP server reached via GET /api/v1/databases/{id} (see llms.txt), which this id feeds.

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• discovery_upload

  Disco

  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.

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• analyze_xero_journal_cleanup

  HelloBooks AI Agents

  Scan a Xero "Manual Journals" CSV export for cleanup issues — unbalanced journals, duplicate journals (same date + same totals), and schema problems (invalid dates, malformed amounts, missing account code/name, missing group key). Input is the raw CSV content the user pastes after exporting from Xero via Accounting → Advanced → Manual Journals → Export. Xero-specific idioms handled: signed Amount column (positive = credit, negative = debit), explicit Debit/Credit fallback shape, Reference-or-Narration+Date grouping, account code preferred over name. Max 5,000 rows; max 5 MB. Returns structured flags with severity, a roll-up summary, parse diagnostics, and a shareable URL at agents.hellobooks.ai/r/{slug}. Use this when a user pastes Xero manual-journal data, asks "check my Xero books", or "find issues in my Xero journal". The funnel CTA routes to /migrate/from-xero for users who want to fix at scale.

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• add_subdomain

  BorealHost

  Create and route a subdomain of a site-linked domain. Creates the DNS A record (if absent) pointing at the site's server, then configures the nginx vhost and SSL certificate on that server. The domain must already be linked to a site (see link_domain). Idempotent: if the DNS record already exists and points at the site's server, the nginx/SSL steps are (re)applied — safe to re-call, e.g. to retry SSL after DNS propagation. May take up to 3 minutes when a certificate is issued. Requires: API key with write scope. Args: domain_name: Registrable domain linked to a site (e.g. "example.com") subdomain: Subdomain label only, no dots (e.g. "blog") Returns: {"fqdn": "blog.example.com", "domain": "example.com", "site": "my-site", "message": "..."} Errors: NOT_FOUND: Domain not found VALIDATION_ERROR: Domain not linked to a site, invalid label, or an existing record points at a different server

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• seo_360

  Autario Data Analytics Platform

  SEO 360 | the caller's OWN deterministic Search Console ACTION report, computed server-side from their connected GSC data (the exact numbers the user sees in the app | nothing re-derived, nothing estimated). The unit is the (query, page) pair and EVERY row ends in a concrete action, so this is the tool to call when a user asks "what should I write next", "which page should I fix first", "where am I losing clicks", "how are my rankings developing", "which pages are decaying", "how are my Core Web Vitals", "what technical SEO issues does my site have". Sections: page2_gaps (position 8-20 pairs ranked by potential click gain toward the top 3 | the core write-or-improve list), ctr_underperformers (ranks top-10 but the snippet loses the click | title/description work), orphan_demand (queries with demand whose best page is not about them | the page is missing, write it), cannibalization (one query split across pages | consolidate or differentiate), trends (click winners/losers AND position winners/losers vs the previous window, honestly flagged when the previous window is incomplete), rank_tracking (position series of the top + pinned queries with current vs 7d/28d deltas, ranking distribution Top3/4-10/11-20/21+, share-of-voice index, brand vs generic split), decay (the refresh queue: pages losing clicks across consecutive windows, ranked by lost clicks, with an optional EUR translation from the user's own click-value setting), vitals (Core Web Vitals p75 field data from the Chrome UX Report for the top pages, pass/fail per LCP/INP/CLS), audit (bounded own-site crawl snapshot: broken links, redirect chains, title/description issues, noindex/canonical conflicts, orphan pages, new-vs-fixed diff, internal-link opportunities), health (data coverage, staleness, which CTR-benchmark source applies). The CTR benchmark is the median of the caller's OWN data per position bucket, with a documented default curve as fallback per thin bucket. Deeper than audience_360 (which answers "who comes from where"): this one prescribes the next SEO action. Requires the caller's own autario account (API key or OAuth) with a Search Console connection | see get_app_context("seo-360").

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• sign_up

  Smarter Weather Developer Onboarding

  Returns the Smarter Weather developer request-access URL (with MCP referral attribution). The developer platform is in limited preview: signup is invite-based. Present the URL to the user so they can request access in a browser; once they receive and accept an email invitation, they authenticate this MCP server via OAuth to continue onboarding (key minting, client configuration). No authentication required.

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• strale_ping

  Strale - 290+ API capabilities for AI agents

  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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• simulate_mmc

  QueueSim

  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) 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. ANTI-FABRICATION: the returned numbers come from a real discrete-event simulation run. Quote them VERBATIM in your reply. Do not round, estimate, or compute derived figures from training-data recall. If the user asks a follow-up about the same configuration, re-call this tool rather than recalling numbers from earlier in the conversation.

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• audit

  mcp

  Perform comprehensive audit of a website URL. Fetches the URL content ONCE and provides a combined report with: - Classification: category, subcategory, language, sentiment, demographics - SEO Analysis: score, grade, issues, recommendations - EEAT Analysis: experience, expertise, authoritativeness, trustworthiness scores - AEO Analysis: AI answer engine optimization score, metrics, issues, signals (includes full Citation Readiness analysis in the nested 'citation' key) - Advertiser Matching: best-fit advertising networks with scores - Similar Sites: competitor/related sites from the same category This is more efficient than calling classify_url, analyze_seo, analyze_eeat, analyze_aeo, select_advertiser, and find_similar_sites separately as it only fetches the page once. Args: url: The website URL to audit (e.g., "https://example.com"). Returns: Comprehensive audit report with: - url: The analyzed URL - classification: Category, subcategory, language, sentiment, demographics - seo: Score, grade, issues, recommendations - eeat: EEAT score, grade, category scores, issues, signals - aeo: AEO score, grade, metrics, issues, signals (includes citation results) - advertisers: Matched advertising networks with scores - similar_sites: Related sites from the same category (up to 10) - cached: Whether result was from cache

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• list_api_keys

  BorealHost

  List all API keys for the account. Shows key metadata (name, prefix, scopes, last used) but never the full key value. Requires: API key with read scope. Returns: [{"id": "uuid", "name": "My Key", "prefix": "bh_a2...", "scopes": ["read", "write"], "is_active": true, "created_at": "iso8601", "last_used_at": "iso8601"|null, "site_slug": null|"my-site"}]

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• acc_list_issues

  ACC MCP

  List and filter issues from a single ACC project (limit 50 per call) via the APS Construction Issues API. When to use: The user or upstream agent needs to review open issues, count issues by status/priority, or look up an issue_id before calling acc_update_issue. E.g. 'show me all critical open issues on the Tower project'. When NOT to use: Do not use to fetch RFIs (use acc_list_rfis) or to search documents. APS scopes: data:read account:read. No write scope required. Rate limits: ACC Issues API ~100 req/min per app; results pageable (limit 50 here, max 200 upstream). For large projects, call once and filter client-side instead of looping. Errors: 401 (APS token expired — refresh); 403 (user lacks 'View Issues' permission on project or scope insufficient); 404 (project_id not found — verify 'b.' prefix and hub membership via acc_list_projects); 422 (invalid filter value — check status/priority spelling); 429 (rate limit — back off 60s); 5xx (ACC upstream — retry with jitter). Side effects: None. Read-only and idempotent.

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• search_servers

  MCP Marketplace

  Search the MCP Marketplace catalog. With a free-text `query` and default `sort`, results are ranked by semantic similarity (gte-small embeddings + cosine similarity), so natural-language queries like 'manage my calendar', 'something to read PDFs', or 'database for my agent' work as well as keyword searches. Each result includes `security_score` (0-10), `risk_level` (low/moderate/high/critical), `critical_findings` (count of severity=critical|high findings), pricing, rating, install count, and a URL. `ranking_mode` in the response indicates whether semantic or keyword matching was used. Before recommending an install, call get_server for full details including every flagged finding — critical_findings > 0 means the server has known security issues you must surface to the user.

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• delete_table

  mcp

  Delete a table. The request requires the 'name' field to be set in the format 'projects/{project}/instances/{instance}/tables/{table}'. Example: { "name": "projects/my-project/instances/my-instance/tables/my-table" } The table must exist. You can use `list_tables` to verify. Before executing the deletion, you MUST confirm the action with the user by stating the full table name and asking for "yes/no" confirmation.

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• install_snippets

  Prism — Data Enrichment for AI Agents

  Return ready-to-paste configuration snippets for installing this MCP server in Claude Code, Cursor, Cline, Continue.dev, Windsurf, and Zed. Free.

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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents

  Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a `fixableInHellobooks` boolean — true means HelloBooks can resolve it automatically in the paid product.

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• analyze_qbo_journal_cleanup

  HelloBooks AI Agents MCP Server

  Scan a QuickBooks Online "Journal Entries" CSV export for cleanup issues — unbalanced journals (debits ≠ credits, with severity by deviation), duplicate journals (same date + same totals, likely posted twice), and schema problems (invalid dates, malformed amounts, missing accounts, missing journal numbers). Input is the raw CSV content the user pastes after exporting from QBO via Reports → Accountant → Journal → Export. Max 5,000 rows; max 5 MB. Returns a structured flag list with severity (high/medium/low), a roll-up summary by category and severity, parse diagnostics (column mapping + unmapped columns), and a shareable URL at agents.hellobooks.ai/r/{slug} (7-day TTL) that renders a branded analysis page suitable for sending to a CA or bookkeeper. Use this when a user pastes QBO journal data, asks "check my books", "find issues in my QBO journal", or "what is wrong with my journal entries". Each flag includes a `fixableInHellobooks` boolean — true means HelloBooks can resolve it automatically in the paid product.

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• scan_mcp_preview

  tech-risk

  FREE preview scan of a target MCP server for tool-poisoning / prompt-injection. Returns issue count, severity breakdown, risk score, and verdict (clear/review/block) — but NOT which tools or the evidence. Use this to check any MCP server (including your own) at no cost; if issues are found, call the paid scan_mcp_server for the itemized findings + remediation. No payment required.

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• mcp_get

  Find MCP - Agentage MCP Directory

  Fetch the complete record for ONE MCP server in the agentage directory by its canonical slug: full description, categories, the packages and remote endpoints it ships, the tools it exposes, a ready-to-run install command, and a README excerpt. Use this after mcp_search to get the depth a result card omits - pass a slug exactly as returned by mcp_search. Slugs are canonical and registry-derived ("io-github-github-github-mcp-server"), NOT the plain product name ("github"); if you pass a plain name anyway it is resolved by search as a fallback - a single confident match returns that server (with `resolved_from` set), anything else returns an error naming the candidate slugs to retry with. No slug yet? call mcp_search first. Read-only.

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