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275,467 tools. Last updated 2026-07-08 22:43

"Linux Foundation" matching MCP tools:

  • Ten-card Celtic Cross spread — traditional ten-position tarot layout. Draws 10 unique cards using cryptographic randomness and assigns each to one of the 10 classical Celtic Cross positions. SECTION: WHAT THIS TOOL COVERS The Celtic Cross examines a situation from 10 angles simultaneously: Position 1 (present) — the core situation Position 2 (challenge) — what crosses or complicates it Position 3 (root) — unconscious foundation or distant past Position 4 (past) — recent events that led here Position 5 (possible_outcome) — what could happen if current energy continues Position 6 (near_future) — what is coming in the next weeks Position 7 (self) — how you see yourself / your attitude Position 8 (external) — how others see you or environmental factors Position 9 (hopes_and_fears) — what you hope for or fear Position 10 (outcome) — the most likely final resolution All position meanings are included in the response — callers do not need external tables. SECTION: WORKFLOW BEFORE: None — standalone reading, or follow asterwise_get_tarot_three_card_spread when a more detailed examination of the same question is needed. AFTER: None. SECTION: INPUT CONTRACT allow_reversed (bool, default false) — Each card independently has 50% reversal chance. question (optional string, max 500 chars) — The question or situation being examined. Example: 'Should I accept the job offer in London?' SECTION: OUTPUT CONTRACT data.spread_type (string — 'celtic_cross') data.positions[] — 10 objects in order [present, challenge, root, past, possible_outcome, near_future, self, external, hopes_and_fears, outcome]: card — full card object is_reversed (bool) position (string — named position key) position_meaning (string — what this position represents) active_meaning (string — orientation-appropriate interpretation) active_keywords[] (string array) data.question (string or null — echoed) SECTION: RESPONSE FORMAT response_format=json — full 10-card spread object. response_format=markdown — formatted Celtic Cross reading. SECTION: COMPUTE CLASS FAST_LOOKUP — cryptographic randomness, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_three_card_spread — 3 positions only; use for simpler questions. asterwise_draw_tarot_cards — free draw with no positional meaning. asterwise_get_tarot_yes_no — binary answer, not positional analysis.
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  • Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.
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  • Authenticated — returns the caller's Blueprint learning-path state: current course slug, stage progress, certification status (Foundation, Practitioner, Capstone), Capstone track eligibility flags, and the next recommended stage. WHEN TO CALL: the user asks 'where am I', 'what's next', or 'am I Capstone-eligible'; before suggesting next-step coaching content. WHEN NOT TO CALL: as a heartbeat (state changes only when the user completes a stage); to read another user's progress. BEHAVIOR: read-only, idempotent. Auth: Bearer <token> (any plan, including basic). Returns user_email, course_slug, stages list with completion timestamps, certification block, and a next_stage hint.
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  • Live capability snapshot of the responder's GPU sidecar — extensions[] (e.g. gpu, clay-v1.5, prithvi-eo2), cuda_available, models_loaded[], healthy, last_polled_unix_s. Refreshed every 30 s by a background poller; reads are constant-time. When to use: Call before scheduling a GPU-heavy plan (Clay / Prithvi / Galileo embeddings, foundation-anchored algorithms) so the agent knows whether the GPU tier is up *right now* without per-request /health round-trips. Pair with `emem_topics` (its `algorithm_availability` map says which algorithm keys can run given the current capabilities) and `emem_explain_algorithm` (full inference-tier metadata per algorithm). When `extensions[]` is empty the sidecar is unreachable — only CPU/scalar/cached tiers will produce facts; foundation-anchored materializers will sign Absence with `gpu_unavailable` reason.
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  • Paid tier only. Calling this without an authenticated CivilQuants account returns TIER_INSUFFICIENT — sign up at https://civilquants.com/pricing or use the free-tier alternative compute_cantilever_wall. Linear reinforced concrete strip foundation, level or stepped. Closes the P1 launch set alongside pad_foundation. Integrates BS 8666:2020 reinforcement scheduling. Stepping is the v10 demonstration of cross-cutting standards-handler discrimination. Example params: length=10 m (1–100), width=0.75 m (0.3–2.5), thickness=0.35 m (0.15–1.5). Example call: {"params": {"length": 10, "width": 0.75, "thickness": 0.35}, "standard": "MMHW"}. Omitted parameters use sensible engineering defaults. Pass deliverables=["xlsx","dxf","pdf"] (any subset) to also receive one-shot download URLs in the same call: Excel BoQ (both tiers, watermarked free) plus the dimensioned DXF (CAD) and PDF drawing sheets (paid tier).
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  • List supported Linux operating systems and their corresponding versions for use with the `linux_audit` tool. ## What this tool does Returns an array of supported OS/version pairs, each in the form: {"os":"name", "versions":["version or codename"]} This allows the LLM and the user to know exactly which inputs are valid for the `linux_audit` tool. ## When to use this tool Use this tool when: - the user does not know which OS names or versions are supported - the user provides unclear or ambiguous OS information - you need to validate `os`/`version` before performing a Linux audit This tool should typically be called **before `linux_audit`** whenever parameters are uncertain. ## Inputs This tool does not require any input. ## Outputs Returns an array of objects: - **os**: supported Linux distribution identifier - **versions**: corresponding list of supported release or codename Example: [ {"os": "ubuntu", "versions": ["noble","focal"]}, {"os": "debian", "versions": ["bookworm","sid"]}, {"os": "redhat", "version": ["redhat-9.0"]} ] ## LLM usage guidelines - Use this tool to validate or suggest correct OS/version combinations before calling `linux_audit`. - If the user provides invalid or misspelled OS names, retrieve the official list here and ask them to select one. - Do not guess operating system identifiers-always rely on this tool to confirm correctness.
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  • Foundation discovery and grant intelligence for nonprofits. 174K+ US funders, IRS 990 data.

  • MEOK AAIF Agent Card MCP — Linux Foundation AAIF agent identity bridge. Issues + verifies +

  • Find grantmakers that have ACTUALLY funded organizations LIKE the caller's, using the real 7.5M-edge who-funds-whom grant graph (IRS 990-PF, 2022-2026). This is the strongest free-tier prospecting move: collaborative-filtering peer prospecting, distinct from search_funders (name/topic lookup) and search_open_grants (active RFPs). HOW IT WORKS: for each peer organization, it looks up every foundation that granted to that peer, then merges funders across peers. A funder that gave to several of your peers ranks highest. Every result carries real grant evidence — which peers the funder funded and for how much. INPUTS (provide one): - peer_orgs (PREFERRED): names or 9-digit EINs of organizations LIKE the one you're raising for — peers, aspirational orgs, or orgs with a similar mission. The graph is keyed by recipient EIN, so naming real peers yields the sharpest evidence. Up to 12 are used. - org_description: a plain-language description of the nonprofit (mission, cause, who it serves). Fallback that resolves well-funded peers by keyword over the IRS BMF; prefer peer_orgs when you can name a few peers. RECOMMENDED WORKFLOW: establish the org's mission/cause, then name 2-5 peer organizations and call this tool. Deepen any candidate with get_funder_profile / get_foundation_grants (pass the returned ein).
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  • Get the Senzing JSON analyzer script to validate mapped data files client-side. REQUIRED: `workspace_dir` (writable directory, e.g. ~/sz-workspace) — the call WILL FAIL without it. The analyzer validates records against the Entity Specification, examines feature distribution, attribute coverage, and data quality. Returns a Python script (no dependencies) with instructions. No source data is sent to the server. Typical workspace_dir values: Linux `/tmp` or `~/sz-workspace`; macOS `~/sz-workspace`; sandboxed envs: explicit path under home (do NOT assume /tmp exists).
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  • Look up grantmaking organizations by name, topic, or location. This tool searches 174K+ grantmaking organizations from IRS data using organization names plus grant-purpose/topic signals. Use it when you know the funder's name, want aligned funders for a cause area, or want to browse by location/size/NTEE code. Multi-word searches are ranked by relevance; simple browse/name fallback results are ordered by total assets. IMPORTANT: Use search_open_grants when the user needs active grant programs or RFPs. search_funders is for finding aligned grantmakers, including ones that may fund by relationship, LOI, or annual cycle rather than a live call.
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  • Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)
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  • Returns all 22 Major Arcana cards (The Fool through The World) as a structured array. Major Arcana represent universal archetypes and major life themes. SECTION: WHAT THIS TOOL COVERS The 22 Major Arcana are the foundation of the tarot — they deal with karmic and spiritual lessons, major life events, and universal forces. They are numbered 0 (The Fool) through 21 (The World). Each has an astrological correspondence and elemental association. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_draw_tarot_cards — draw from this subset by filtering by arcana_type. SECTION: INPUT CONTRACT response_format — Required: markdown | json. SECTION: OUTPUT CONTRACT data[] — 22 card objects, each identical to asterwise_get_tarot_card output. Ordered 0–21 (The Fool through The World). SECTION: RESPONSE FORMAT response_format=json — array of 22 card objects. response_format=markdown — formatted list. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_cards — full 78-card deck including Minor Arcana. asterwise_get_tarot_suit — 14 Minor Arcana cards by suit.
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  • List statutory tax-rate slabs by jurisdiction — IN GST (5/12/18/28 + zero + exempt + composition trader/manufacturer/restaurant + compensation cess), UK VAT (20 / 5 / zero / exempt), AU GST (10 / GST-free), US sales-tax (state-administered summary, no federal rate), CA GST 5% + HST 13% ON / 15% Atlantic, SG GST 9%, NZ GST 15%, AE VAT 5%. Filter by `country`, `taxType` (GST/VAT/Sales-Tax/HST/IGST/CGST-SGST/TDS/TCS), or `scheme` (standard / reduced / zero / exempt / composition / cess / state-summary). Every entry carries an effective-from date and an authoritative `source` URL (CBIC, gov.uk, ATO, CRA, IRAS, IRD, FTA, Tax Foundation) — agents should confirm the rate against the source before quoting figures to a user. Use this when a user asks "what is the GST rate on X?", "what VAT band does Y fall into?", or "what are the composition slabs in India?". This is the public statutory reference — for an org-specific tax assignment use the authenticated books_classify_event tool.
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  • List statutory tax-rate slabs by jurisdiction — IN GST (5/12/18/28 + zero + exempt + composition trader/manufacturer/restaurant + compensation cess), UK VAT (20 / 5 / zero / exempt), AU GST (10 / GST-free), US sales-tax (state-administered summary, no federal rate), CA GST 5% + HST 13% ON / 15% Atlantic, SG GST 9%, NZ GST 15%, AE VAT 5%. Filter by `country`, `taxType` (GST/VAT/Sales-Tax/HST/IGST/CGST-SGST/TDS/TCS), or `scheme` (standard / reduced / zero / exempt / composition / cess / state-summary). Every entry carries an effective-from date and an authoritative `source` URL (CBIC, gov.uk, ATO, CRA, IRAS, IRD, FTA, Tax Foundation) — agents should confirm the rate against the source before quoting figures to a user. Use this when a user asks "what is the GST rate on X?", "what VAT band does Y fall into?", or "what are the composition slabs in India?". This is the public statutory reference — for an org-specific tax assignment use the authenticated books_classify_event tool.
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  • Forecast the next `horizon` readings of a canonical telemetry series using TimesFM (Google's time-series foundation model). Returns a point forecast plus quantile uncertainty bands (q0.1 … q0.9) — no per-machine training required. The kernel already normalizes raw OEM telemetry into canonical FCS fields; this predicts where a field is headed next. Args: time_series historical canonical values, oldest→newest (≥16 recommended) canonical_field the FCS field the series represents (e.g. "spindle_load_pct"), carried through for labeling/provenance horizon number of steps to predict (1–256, default 24) frequency accepted for forward-compat; TimesFM 2.5 auto-detects cadence USE WHEN: a user wants to know where a metric is trending — "what will spindle load look like over the next 2 hours", "project coolant temperature", "forecast throughput". For threshold/failure questions use predict_breach or remaining_life instead. PREMIUM (Pro tier) — runs ML inference (~$0.05/call once metered billing is active).
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  • Search Linux kernel CVEs. No API key required: keyless callers get the free public tier — recent high-severity Linux kernel CVEs (capped at 25 results). Free *keyed* callers see only CVEs published in the last 60 days; basic+ keyed callers get the full corpus. ``query`` matches against CVE id and description (case-insensitive). ``severity`` filters by effective severity (``critical``/``high``/``medium``/``low``). ``cvss_min`` filters by effective CVSS score. ``published_after`` (ISO 8601) returns only CVEs newer than that date. Returns up to ``limit`` (max 100) CVEs, newest first.
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  • WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents
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  • Start the purchase flow for an available domain via Stripe checkout. IMPORTANT: Before calling this tool, you MUST first call check_domain to get the price, then clearly show the user the price and get their explicit confirmation before proceeding. Never call buy_domain without the user seeing and approving the price first. The registrant contact details are required because the domain will be registered in the buyer's name (they become the legal owner). WHOIS privacy is enabled by default, so these details are not publicly visible. Creates a Stripe checkout session. IMPORTANT: After this tool returns, you MUST open the checkout_url by running the shell command `open "<checkout_url>"` (macOS/Linux) so the payment page launches automatically in the user's default browser. Do NOT paste the URL as a link. Tell the user the page has been opened and they can complete payment there. Args: domain: The domain to purchase (e.g. "coolstartup.com"). first_name: Registrant's first name. last_name: Registrant's last name. email: Registrant's email address. address1: Registrant's street address. city: Registrant's city. state: Registrant's state or province. postal_code: Registrant's postal/zip code. country: 2-letter ISO country code (e.g. "US", "GB", "DE"). phone: Phone number in format +1.5551234567. org_name: Organization name (optional, leave empty for individuals). Returns: Dict with order_id, checkout_url, price_cents, and price_display.
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  • List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools.
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  • Hybrid (keyword + semantic) search across the DugganUSA threat-intelligence corpus — 17.9M+ indexed documents. Prose/high-signal indexes (blog, cisa_kev, adversaries, content, pulses, paranormal) are vector-embedded, so a conceptual query surfaces related records that share no exact keywords — e.g. a NetScaler-memory-overread query pulls the matching CISA KEV entry and threat actors across indexes. Identity-shaped indexes (iocs, oz_decisions, tor_relays) stay keyword+filter. Public indexes only, read-only, prompt-injection sanitized. Returns up to 25 hits with title, snippet, source, and timestamp. Available indexes: • iocs (1.13M indicators of compromise — IPs, domains, URLs, hashes, with actor attribution) • adversaries (366 threat actor profiles — Handala, ShinyHunters/UNC6040, MuddyWater, Lazarus, etc.) • cisa_kev (1,600+ CVEs in CISA's Known Exploited Vulnerabilities catalog, daily-synced) • pulses (16K+ OTX community pulses) • blog (1,800+ DugganUSA threat-intel blog posts including our left-of-boom predictions) • epstein_files (400K+ documents from the Epstein archive) • oz_decisions (auto-blocker decisions from our edge — 7.5M+ rows) • paranormal (3,400 fringe-research docs) • tor_relays (1.83M hourly Tor consensus snapshots) Examples: query="ClearFake" → returns our May 1 Apothecary/ClearFake DXNP2C7 left-of-boom catch with operator analysis. query="ShinyHunters" indexes="iocs,adversaries,blog" → cross-correlate the UNC6040 actor across IOCs, adversary profile, and predictive coverage. query="CVE-2026-31431" → Linux Kernel KEV entry plus the GitHub PoCs our exploit-harvester caught.
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  • Vector delta between the same cell at two tslots: returns the per-element residual, its L2 norm (scalar change-magnitude), the cosine between the two source vectors (orientation drift), and both source fact CIDs so the agent can quote both attestations as evidence. When to use: Call when the user asks 'how much did X change between A and B' for a foundation embedding at one place. Pass `tslot_a` and `tslot_b` (must differ); default `encoder=geotessera`. For per-band scalar change (NDVI delta, elevation delta) use `emem_diff` instead.
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