"Red" matching MCP tools:

• asset_lookup

  redm-mcp

  Resolve a RedM game-data asset (ped model, weapon, object, door, vehicle) by exact name, 32-bit hash, or partial-name search. O(1) structured lookup against pre-parsed discoveries tables — replaces the common workflow of grepping `a_c_bear_01` in peds_list.lua, then cross-referencing RELATIONSHIP/README.md for its relationship group. Returns: type, name, normalized hash (`0x` + 8 uppercase hex), source file + line, plus type-specific metadata (peds get `variants` + `relationship`, weapons get `group`, doors get `coords` + `model_hash`, objects get `category`/`subcategory`). Catalog ~22,500 entries (mostly objects). Typical latency p50 ~15ms, p95 ~65ms. NOT for: - **Script natives** like `SET_ENTITY_COORDS`, `GetPedHealth`, or hashes from `Citizen.InvokeNative(0x...)` — use `lookup_native`. Native hashes are 64-bit (`0x06843DA7060A026B`); asset hashes are 32-bit (`0xBCFD0E7F`). Different namespaces, never collide. - **Flag enums, settings, clipsets, scenario keys** like `CPED_CONFIG_FLAGS`, `MP_Style_Casual`, `mech_loco_m@`, `MAGGIE_SEAT_CHAIR_DESK_WRITING`. Those live as tokens in lua source but not in this catalog. Use `grep_docs`. - **Behavior queries** ("which animal is the bear", "weapons in the lemat family") — use `semantic_search`. Pass exactly ONE of `name` / `hash` / `search`. Optional `type` narrows to a category (useful when a fragment like "horse" hits both peds and vehicles). Note: `type` reflects the SOURCE FILE — the same asset name can exist under multiple `type`s. e.g. `mp006_p_mshine_int_door01x` appears as `type=object` (1 row from object_list.lua) AND `type=door` (2 rows from doorhashes.lua, different door hashes for distinct in-world instances with `coords`). Pick `type=door` when you want lockable in-world doors with positions; `type=object` for the model itself. Examples: - `{name: "a_c_bear_01"}` → exact ped lookup, returns variants=11 + relationship=REL_WILD_ANIMAL_PREDATOR. - `{hash: "0xBCFD0E7F"}` → resolves to ped `a_c_bear_01` (omit `0x` ok). - `{search: "lemat", type: "weapon"}` → substring match → `weapon_revolver_lemat`. - `{search: "moonshine", type: "door"}` → exact substring misses (no door name contains "moonshine"), fuzzy trigram fallback fires → `mp006_p_mshine_int_door01x`. Fuzzy mainly fires when `type` narrows out the exact-substring matches; without `type`, common terms find substring hits first and never reach fuzzy.

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• semantic_search

  redm-mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings. If you are retrying after a previous call returned no useful results, populate `prior_attempt` so the server can surface alternative wordings and learn what's missing from the docs.

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• ceevee_generate_report

  CareerProof MCP

  Generate a CeeVee career-intel report asynchronously (15 credits, takes 2-3 min). Returns report_id and status. POLLING: Call ceevee_get_report(report_id) every 30 seconds, up to 40 times (20 min max). If status='completed', download PDF with ceevee_download_report(report_id). If status='failed', relay the error_message to the user. If still processing after 40 polls, stop and inform the user with the report_id so they can check later. Call ceevee_list_report_types first to discover valid report_type values and required inputs. Report categories: Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, Fractional Leadership.

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• asterwise_get_crystals

  Asterwise

  Returns all 50 crystals in the database sorted alphabetically. Each entry includes chakra associations, elemental correspondences, Vedic and Western planetary assignments, physical/emotional/spiritual healing properties, geographic origins, affirmations, and safety cautions. SECTION: WHAT THIS TOOL COVERS Dual-tradition crystal database distinguishing classical Vedic assignments from modern Western metaphysical ones. vedic_correspondence field is always one of: 'navaratna' (primary classical gem per BPHS — one of the nine planetary gems), 'uparatna' (classical substitute gem), or 'none_classical' (no Vedic text assigns this stone — Western tradition only). The nine Navaratna: Ruby (Sun), Pearl (Moon), Red Coral (Mars), Emerald (Mercury), Yellow Sapphire (Jupiter), Diamond / White Sapphire (Venus), Blue Sapphire (Saturn), Hessonite Garnet (Rahu), Cat's Eye Chrysoberyl (Ketu). Crystals like Labradorite and Amazonite are marked none_classical — they were unknown in ancient India. Does not compute natal chart recommendations (asterwise_get_crystal_recommendations). SECTION: WORKFLOW BEFORE: None — standalone catalogue. AFTER: asterwise_get_crystal_by_planet — filter by Vedic planet for remedial use. SECTION: INPUT CONTRACT No required parameters. SECTION: OUTPUT CONTRACT data.total (int — 50) data.crystals[] — 50 objects each: slug, name, colors[], hardness_mohs (float) chakras[] (string array) element (string) zodiac_signs[] (string array) vedic_planet (string or null) vedic_correspondence (string — 'navaratna'|'uparatna'|'none_classical') western_planet (string or null) keywords[] (string array) healing_physical, healing_emotional, healing_spiritual (strings) description (string) origins[] (string array) affirmation (string) caution (string or null) SECTION: RESPONSE FORMAT response_format=json — full 50-crystal array. response_format=markdown — formatted catalogue. Both return identical data. SECTION: COMPUTE CLASS FAST_LOOKUP — static database, no ephemeris. SECTION: ERROR CONTRACT INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_crystal — single crystal detail by name. asterwise_get_crystal_by_planet — filter by Vedic planetary correspondence. asterwise_get_crystal_recommendations — recommendations by zodiac/chakra/intention.

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• grep_docs

  redm-mcp

  Find an EXACT literal token in raw doc files (markdown + lua). Use for specific weapon/ped/animation/prop/interior/zone names (`weapon_pistol_volcanic`, `a_c_bear_01`, `p_campfire01x`), known hashes (`0x020D13FF`), walkstyles/clipsets (`MP_Style_Casual`, `mech_loco_m@`), or any string you'd `grep` for. NOT for behavior/concept queries (use `semantic_search`) or script-native hash/name lookup (use `lookup_native`). REQUIRED for tokens inside the largest rdr3_discoveries data tables (audio_banks, ingameanims_list, cloth_drawable, cloth_hash_names, object_list, megadictanims, entity_extensions, imaps_with_coords, propsets_list, vehicle_bones) — only preview-indexed for embeddings, so `semantic_search` will NOT find tokens in them. Optional: `contextBefore`/`contextAfter` for ±N surrounding lines (saves a follow-up `get_document` call); `filesOnly: true` to get paths only (cheap exploration); `multiline: true` for cross-line patterns (`(?s)foo.*bar`). Pattern uses Rust regex syntax (rg engine). PREFER one targeted call over giant `a|b|c|d|e` alternations — split into separate calls; alternations rarely improve recall and bloat the regex automaton. Returns matched lines with path + line number. Long matched lines are windowed ±60 chars around the match (…); fetch full context via `get_document({path})`. If you are retrying after a previous pattern returned no matches, populate `prior_attempt` so the server can record what didn't work and steer alternative spellings.

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• shippingrates_congestion

  FreightGate MCP Server

  Get port congestion metrics — vessel waiting times, berth occupancy, and delay trends for a specific port. Use this to assess port efficiency and anticipate detention risk. High congestion often leads to longer container dwell times and higher D&D costs. For shipping disruption news and alerts (Red Sea, Suez, chokepoints), use shippingrates_congestion_news instead. PAID: $0.02/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: { port, congestion_level, avg_waiting_hours, berth_occupancy_pct, vessel_count, trend, period_days }.

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• validate_claim

  Fred

  Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).

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• get_document

  redm-mcp

  Fetch full markdown of a doc by `path` (as returned by `browse`, `semantic_search`, or `grep_docs`). Use to retrieve full content after a search snippet looks promising. Pass `heading` (full breadcrumb like `Character Management > Inventory Management`, or just the leaf — case-insensitive, fuzzy) to fetch only that section. Deep-heading matches auto-prepend the H2 parent's intro for context. For individual script natives prefer `lookup_native`. The largest rdr3_discoveries lua data tables are keyed catalogs: call with no `heading` to list their top-level keys, then pass a key as `heading` to fetch that one entry; use `grep_docs` to search values inside. For code symbols (`addItem`) use `grep_docs`. Community findings use `learning:N` paths, not `learnings/<slug>.md`. On 404 returns available headings + cross-file hints.

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• list_namespaces

  redm-mcp

  Orient yourself: list available doc categories and their namespaces. Use once at session start (or when unsure) before applying a `category=` / `namespace=` filter to `browse` / `semantic_search`. NOT a content search. Categories: `natives` (PLAYER, ENTITY, VEHICLE, …), `vorp`, `rsgcore`, `oxmysql`, `discoveries` (AI, weapons, peds, animations, clothes, objects, …), `jo_libs` (menu, notification, callback, framework-bridge, …, dev_resources, redm_scripts), `guides`, `learnings`.

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• recall

  Ted Eu

  Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

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• legal_clause_extractor

  gapup-mcp

  Structured extraction of clauses, obligations and deadlines from legal documents (SaaS contracts, NDAs, employment agreements, loan agreements, leases, M&A deals, IP licences). Complements contract_risk_scanner with granular per-clause output. ICP: legal ops, M&A lawyers, paralegals, contract managers, compliance officers. Capabilities: • Auto-detects document type (7 types) and language (EN/FR/DE/ES/PT) • Extracts parties with roles (buyer, seller, licensor, employee, etc.) • Splits document into sections and classifies 16+ clause types • Per-clause: 20 obligation patterns (EN/FR/DE), 10 deadline patterns, 18 risk detectors • Document-level: red flags (liability cap, auto-renewal, IP overreach, etc.), missing clauses per doc type • Global deadline calendar with P0/P1/P2 severity • Cross-reference map between sections • Cache: 7 days (legal docs stable once provided) 100% pure compute — no external fetch required. Accepts 10k–100k char documents.

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• bet_research

  Ted Eu

  Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet (crypto price / Fed rate / geopolitical / sports / corporate / drug approval / election / other), fans out to the right packs (e.g. crypto+fred+gdelt for a BTC bet, fred+bls for a Fed bet, gdelt+acled+comtrade for Strait of Hormuz), and returns an evidence packet plus a simple market-vs-model comparison so the caller can see where the implied probability disagrees with the data. Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?". This is the core demo product — agents that get bet-relevant context here convert better than ones that have to discover the packs themselves.

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• resolve_entity

  Ted Eu

  Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.

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• semantic_search

  RedM Mcp

  Search RedM/RDR3 docs by behavior, concept, OR exact token. Use when you don't have a specific native hash/name (use `lookup_native`) and the term isn't a known asset name in a large data table (use `grep_docs`). Hybrid mode (default) handles 'how do I X' queries ('teleport player', 'spawn vehicle', 'inventory add item') AND tokens ('addItem', 'weapon_pistol_volcanic', 'CPED_CONFIG_FLAG_') — fused via RRF over vector + BM25. Returns ranked snippets (path, breadcrumb, heading, snippet, score). Call `get_document({path, heading})` for full chunk content. `mode=semantic` for pure vector; `mode=lexical` for pure BM25. Filter via `category=vorp|rsgcore|oxmysql|natives|discoveries|jo_libs|learnings` or `namespace`. Community findings merged by default; `category=learnings` returns only findings. If you are retrying after a previous call returned no useful results, populate `prior_attempt` so the server can surface alternative wordings and learn what's missing from the docs.

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• pipeworx_feedback

  Ted Eu

  Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

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• atlas_technique_lookup

  ContrastAPI

  Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.

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• ceevee_list_report_types

  CareerProof MCP

  List all available CeeVee career-intel report types with descriptions, required/optional input fields, and credit costs. Call this BEFORE ceevee_generate_report to discover valid report_type values and the exact inputs each type requires. Categories include Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, and Fractional Leadership. Free.

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• remember

  Ted Eu

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• check_stock

  agentic-shelf

  Check real-time inventory, price, and shipping for a product SKU. This tool queries the connected e-commerce platform (Shopify, WooCommerce, etc.) for live inventory data. Returns current stock level, price, and availability status. Args: sku: Product SKU (Stock Keeping Unit) - e.g., "RED-WIDGET-001" Returns: Dictionary with: - sku: The requested SKU - stock: Current inventory count - price: Current price in USD - can_ship_today: Boolean indicating same-day shipping availability - message: Human-readable status message Example: >>> await check_stock("WIDGET-001") { "sku": "WIDGET-001", "stock": 42, "price": 29.99, "can_ship_today": True, "message": "✅ WIDGET-001 (Awesome Widget) - 42 in stock at $29.99" }

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• redirect_chain

  ContrastAPI

  Walk an HTTP redirect chain hop-by-hop, returning per-hop {url, status_code, location, latency_ms}. Use to deobfuscate URL shorteners (bit.ly / t.co / lnkd.in), audit suspicious links from phishing investigations, or trace marketing tracking redirects. SSRF-guarded: each redirect target's resolved IP is re-validated before connecting (private IPs and non-HTTP schemes rejected). Up to 10 hops; loop_detected=true if a hop would revisit a previously-seen URL (we abort before the duplicate fetch); truncated=true if the chain still had a 30x at hop 10. Per-target eTLD+1 throttle (60 req/min) consumed once for the start host AND once per new host reached — a chain across 11 unrelated domains cannot bypass the cap. Free: 30/hr, Pro: 500/hr. Returns {start_url, final_url, hops, hop_count, final_status, loop_detected, truncated, summary}. Returns 502 ErrorResponse on hard fetch failure (timeout / TLS / connect); 429 with Retry-After if a hop's eTLD+1 throttle is exceeded mid-chain.

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