Skip to main content
Glama
261,119 tools. Last updated 2026-07-05 11:02

"A remote Minecraft server that provides meaningful features or gameplay" matching MCP tools:

  • 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.
    Connector
  • 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
  • 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.
    Connector
  • Connectivity check that confirms the Nordic MCP server process is responding. Use this at the start of a session to verify the server is reachable before making other calls. Do not use as a proxy for database health — the server can respond while the Qdrant vector database is temporarily unavailable. To confirm data availability, call search_filings directly. Returns: A greeting string: "Hello {name}! Nordic MCP server is running."
    Connector
  • Download a completed Future Video Studio final render URL to a local file. Use this only after fvs_get_render_status or fvs_get_paid_render_status returns a final_video_url for a completed render. The tool performs an unauthenticated HTTPS GET to that signed URL and writes the response bytes to output_path on the MCP server's local filesystem. It does not call the FVS Agent API, spend wallet credits, require FVS_AGENT_API_KEY, cancel jobs, or modify remote render state. Side effects and constraints: output_path is a local filesystem path for the MCP server process, parent directories are created, existing files are not replaced unless overwrite is true, and large videos may take minutes to download. The request timeout is 600 seconds. Use a fresh status check to refresh expired signed URLs, and do not pass arbitrary or untrusted URLs.
    Connector
  • Server self-description — capability matrix, tool catalog, classifier counts, supported query patterns, primary sources. Free tier. Use this tool when an agent first connects and needs the capability matrix to decide whether this server can answer the user's question, or when the user asks "what can koreanpulse do" or "what data sources does this MCP server provide". Returns a structured dict that downstream agents can ingest directly.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • send-that-email MCP — wraps StupidAPIs (requires X-API-Key)

  • Capture photos remotely from mobile devices via S3-backed upload URLs

  • Build the per-window x per-party concurrent-delay attribution matrix from a chronological list of XER snapshots. Implements the per-window concurrency view per AACE RP 29R-03 §4.2.B (concurrent delay apportionment). Where ``forensic_windows_analysis`` answers "how many days does each party own across the whole project?", this tool answers "how did each window distribute its shift across the parties?" — useful when defending or attacking concurrency findings on a window-by-window basis. Conservation rule (AACE 29R-03 §4.1): the sum of per-party column totals equals the sum of per-window completion shifts within ±1 day of rounding. The ``conservation_check`` field on the response reflects this; ``conservation_diff_days`` carries the exact gap. IMPORTANT — conservation is NOT attribution. ``conservation_check`` can be True (the columns sum to the grand total) even when 100% of the shift lands in the Unattributed column, i.e. no party owns any of the drift. Read ``unattributed_share_pct`` and ``high_unattributed_share_warning`` to know whether a meaningful apportionment actually occurred. A fully-unattributed matrix conserves perfectly but attributes nothing — never present its green conservation check as a validated apportionment. Use this tool when you only need the matrix view; use ``forensic_windows_analysis`` for the full claim. Args: schedules: chronologically ordered list of dicts — the SAME shape ``forensic_windows_analysis`` accepts. Each dict carries ``label`` (optional) and EXACTLY ONE of ``xer_content`` (full XER text, hosted/remote use) or ``xer_path`` (server-side path, local use). This is the preferred input for hosted/remote clients. xer_paths: legacy chronologically ordered list of server-side XER file paths (local-server use). xer_contents: legacy chronologically ordered list of XER text contents. Each element is the full text of one XER. Supply EXACTLY ONE of schedules / xer_paths / xer_contents (lists must have at least 2 entries either way). Returns: { "parties": ["Owner", "Contractor", "Concurrent", "Force Majeure", "Unattributed"], # Unit for every shift_* field and the grand totals. Always # "working_days" — the matrix measures the completion shift # in working days (Dana default). The *_calendar_days twins # express the SAME shift in calendar days so an unlabeled # "11" can never be mistaken for the 15-calendar-day value. "shift_unit": "working_days", "rows": [{ "window_label", "period_start", "period_end", # shift_days == shift_workdays (working days, # legacy alias). shift_calendar_days is the same # shift in calendar days; shift_basis names the # finish driver the shift was measured on. "shift_days", "shift_unit", "shift_workdays", "shift_calendar_days", "shift_basis", "parties": {party: days}, "cascade_inferred": bool }, ...], "column_totals": {party: days}, "grand_total_shift": int, # working days (legacy) "grand_total_shift_workdays": int, "grand_total_shift_calendar_days": int | None, "conservation_check": bool, "conservation_diff_days": int, # Disambiguates "conserved AND attributed" from "conserved # but entirely Unattributed". unattributed_share_pct is # |Unattributed| / sum|shift| as a percent; the warning # flips True when that share is dominant (>= 50%). "unattributed_share_pct": float, "high_unattributed_share_warning": bool, "standard": "AACE RP 29R-03 §4.2.B (concurrent delay apportionment)" }
    Connector
  • Mesh a kernelCAD .kcad.ts source server-side and return a COMPACT geometry summary — overall bounds plus, per feature, its id, kind, triangle count, and bounding box. Use this to INSPECT a model's geometry without a viewer: confirm a part is the size/shape you expect, see how many triangles each feature contributes, or check that every feature produced geometry. This runs the full server-side OCCT pipeline (the same one the Studio renderer uses), so it evaluates modern sources (assembly, path, .material, …) that the legacy client worker cannot. INPUT: `source` (required) the .kcad.ts script text; `fileName` (optional) a label for diagnostics; `params` (optional) a map of parameter-name → number overrides applied before meshing (stateless slider recompute). OUTPUT: { ok, bounds, featureCount, features: [{ id, kind, triangleCount, bbox: { min:[x,y,z], max:[x,y,z] } }], failedFeatureIds, diagnostics }. `ok` is true when every feature meshed; `failedFeatureIds` lists features that failed to compile (and `ok` is then false). Raw vertex/index/normal arrays are NEVER returned — this is a summary only. To SEE the rendered model, use open_in_studio + get_latest_render instead.
    Connector
  • Return step-by-step instructions for setting up x402 USDC autopay for this MCP server. Use this if a paid tool returned a 402 error or you're onboarding a new agent that needs to pay for API calls. Free.
    Connector
  • 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")
    Connector
  • Return step-by-step instructions for setting up x402 USDC autopay for this MCP server. Use this if a paid tool returned a 402 error or you're onboarding a new agent that needs to pay for API calls. Free.
    Connector
  • Register a new Fractera user and start the deployment of their server in one atomic call. Use this AFTER you have collected the user's email (entered twice for typo protection), server IP, and root password. Creates the User row (or reuses an existing one with the same email), creates a free Subscription, creates a ServerToken, wipes any previous installation on the target server, and launches bootstrap. The deploy is IP-first (phase-1): the server comes up on plain HTTP at http://<IP>:3002 in 8-14 minutes; it does NOT get a domain or HTTPS cert here (that is an optional later step inside the workspace). Returns session_id (for a single on-demand check_status read — do not poll) and server_token (so the user can recover via retry_deploy if anything breaks). Call this AT MOST ONCE per conversation.
    Connector
  • Register a new Fractera user and start the deployment of their server in one atomic call. Use this AFTER you have collected the user's email (entered twice for typo protection), server IP, and root password. Creates the User row (or reuses an existing one with the same email), creates a free Subscription, creates a ServerToken, wipes any previous installation on the target server, and launches bootstrap. The deploy is IP-first (phase-1): the server comes up on plain HTTP at http://<IP>:3002 in 8-14 minutes; it does NOT get a domain or HTTPS cert here (that is an optional later step inside the workspace). Returns session_id (for a single on-demand check_status read — do not poll) and server_token (so the user can recover via retry_deploy if anything breaks). Call this AT MOST ONCE per conversation.
    Connector
  • Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's deprecated /audio-features endpoint. Provide EXACTLY ONE identifier: - `track` (optionally with `artist`) — e.g. track="Blinding Lights", artist="The Weeknd". - `isrc` — e.g. "USUM71900001". - `mbid` — a MusicBrainz recording UUID. - `spotify_id` — a Spotify track ID, URI, or URL. Returns a JSON object of features. Some feature fields may be null for tracks resolved via the fallback catalogs (only audio-derived values are present for fully analysed tracks). If a track name is not yet in the catalog, the API queues an on-demand analysis and this tool reports that it is queued — retry in ~30s-2min. If you only have a fuzzy or partial name, call search_catalog first to find the exact track.
    Connector
  • Show the account safety policy. Useful before custom memory-writing that may include sensitive content; normal writes are already sanitized server-side.
    Connector
  • Return fixed sample Haunt extraction JSON. No signup, API key, remote fetch, provider call, or quota usage.
    Connector
  • 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.
    Connector
  • Render a Slidev presentation from markdown and return its hosted URL. IMPORTANT: Before calling this tool, you MUST call get_theme with the theme name you plan to use. Each theme has unique layouts, components, and frontmatter options. Apply the theme's specific features in your markdown to produce high-quality slides that match the theme's design. If the user has not specified a theme, call list_themes to pick one. If you are unfamiliar with Slidev markdown syntax, call get_slidev_guide. Images must be remote URLs or base64-encoded inline. Local file paths are not supported.
    Connector
  • Temporal catalog: for every materializable band the upstream-of-record window the data genuinely covers, the temporal `kind` (static | annual_snapshot | annual_stack | time_series | now_only | per_release), tempo seconds, upstream wire path, and whether `emem_backfill` is meaningful. When to use: Call before `emem_backfill` or any historical recall to check whether a band has a meaningful past at the requested time. Each entry includes `history_available_from_unix` / `history_available_to_unix` (and ISO strings) plus `backfill_supported`. Use this to avoid trial-and-error 422s on now-only bands (`weather.*`) and to enumerate the per-year `geotessera.YYYY` vintages the responder ships. The catalog is driven by the same registry the recall path consults — so what it lists is exactly what materializes.
    Connector
  • Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
    Connector