"Matrix" matching MCP tools:

• matrix

  Openrouteservice

  "Distance matrix between locations" / "all-to-all travel times" / "N×M routing grid" / "drive / walk / bike times between [list]" — N×M distance and duration matrix between many points via OpenStreetMap routing. Use for traveling-salesman setup, multi-stop optimization, nearest-warehouse, fleet dispatch. Profile-aware (car / truck / bike / foot / wheelchair).

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• install-matrix

  mesh-connector

  Install Matrix — Generate copy-paste-correct MCP install snippets for ~29 clients at once — with each client's config-key traps already encoded (Antigravity demands `serverUrl` and lowercase names; Gemini CLI demands `httpUrl`; AnythingLLM demands type 'streamable'; Goose/Kiro/Cursor/VS Code get working deeplinks; ChatGPT gets the search+fetch requirement spelled out). Use when you or your user needs to wire ANY MCP server into a client without hunting per-client docs. Deterministic, no model call. Input: {server_url: string (required, http(s) URL), name?: string, transport?: 'streamable-http'|'sse', auth?: 'none'|'bearer-optional'|'bearer-required'}. Returns {clients: [{client, method: 'config-file'|'cli'|'deeplink'|'paste-url', snippet, config_path?, notes?}], count}. (1 MESH/call, a tool · devtools)

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• calculate_review_metrics

  eDiscovery Decoder News/Calc

  Score a coded sample when you have a full confusion matrix (true/false positives and negatives) — e.g. comparing a TAR model's calls against a reviewer's. Returns recall, precision, F1, accuracy, and in-sample elusion. Use calculate_control_set_recall if you only have relevant-found vs relevant-missed; calculate_elusion for a discard/null-set sample. Aggregate counts only; not legal advice.

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• competitive_deep_dive

  gapup-mcp

  Gold-standard competitive deep dive — STRUCTURED multi-source data (no LLM narrative). Pair tool: `competitor_intel` for LLM-narrated board briefing + slide script. Aggregates Wikipedia, Yahoo Finance, SEC EDGAR, Wayback Machine, DuckDuckGo, HackerNews, domain scraping — all keyless. Returns agent-shaped JSON: KPIs (funding, employees, revenue, market cap), P0/P1/P2 competitive signals, pricing radar, competitor comparison matrix, Wayback timeline, positioning (sector/industry/icp_hypothesis/moat_signals), quality score. Every field is sourced or marked unavailable — no hallucinated figures. SLA: p50 ~25s, p95 ~30s · score 80+ on listed targets (US/EU/foreign) · score ~40 on private companies (no EDGAR/Yahoo data). Use sync for batch agents (≤30s tolerance). Use `competitive_deep_dive_async` + `competitive_deep_dive_result(job_id)` for conversational agents. Inputs: company name or domain (required), optional competitor list (≤5), optional depth (easy/medium/hard).

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• patternfetch_capabilities

  patternfetch

  Return patternfetch's own capability matrix: which asset classes are covered (US stocks, ETFs, crypto spot), the data source and delay for each, the supported timeframes, the endpoint list, the per-call prices and tier limits, and the product version. Takes no arguments and returns the same static self-description on every call — it contains NO market data (no quotes, candles, patterns or base rates). WHEN: once at the start of a session, to learn which asset classes and timeframes are supported before calling brief/multi/delta/analogs/scan, instead of guessing and getting a validation error. WHEN NOT: you already know the ticker and timeframe are supported (go straight to brief), or you want actual market data (this returns none).

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• get_account_status

  shiply

  Get the signed-in account's plan, capabilities, and upgrade URL. Call this FIRST when figuring out what features you have access to — it tells you exactly what's available and what's blocked. The upgrade_url is human-clickable; show it in chat when a feature requires a higher plan. Returns plan id + name + subscription status, hard limits (sites, databases, custom domains, drives), and a capability matrix listing every gated feature (workers_lite, databases_neon_postgres, custom_domains, etc.) with whether you have access and the minimum plan needed.

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• generate_qr

  mcp

  Use this when you need to turn text or a URL into a real, scannable QR code rather than describing one. Deterministic: same input, same output. Byte mode, error-correction level M, versions 1-10 auto-selected by length (up to 213 bytes); the encoder scores all 8 mask patterns and keeps the lowest-penalty one. Returns both the module matrix as rows of 0/1 (1 = dark module) and a ready-to-render self-contained SVG string. moduleSize sets SVG pixels per module (default 10) and quietZone the border width in modules (default 4). Example: {text:'HELLO'} -> version 1, size 21x21, byteLength 5. Longer text auto-bumps the version and matrix size; over 213 bytes returns an error.

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• compare_protocols

  Intelligence MCP

  Get a side-by-side comparison matrix of all five agent payment protocols (AP2, ACP, x402, MPP, UCP) across creator, layer, agent delegation, budget limits, cross-merchant coordination, and MCP integration. Use when the user asks to compare protocols ('AP2 vs ACP', 'which protocol handles budgets?', 'what's the difference between x402 and MPP?', 'show me the landscape'). Use get_protocol_info instead for deep details on a single protocol.

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• baas_provider_comparator

  AINumbers Fintech Intelligence Suite

  Score and compare BaaS providers across 10 capability dimensions (regulatory standing, programme management, card issuance, rails, KYC/KYB, disputes, developer experience, pricing, FDIC pass-through, compliance tooling) with a user-adjustable 1-5 weighting matrix. Outputs a weighted comparison matrix and Markdown evaluation memo. Browser-based, client-side only, zero PII. Renders the interactive AINumbers tool as a widget; inputs are applied via the AIN Bridge and the tool runs client-side (zero PII, zero network).

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• ris_list_reference

  ris-austria-mcp-server

  Ground the opaque German codes across the RIS surface — static and offline, no upstream call. Topics: applications (all 39, with coverage windows, binding status, and content formats), courts (17 codes with Geschäftszahl examples and successor mapping), states (the three Bundesland request spellings), decision_types, decision_kinds (per-court Entscheidungsart values), issuing_bodies (dsk/dok/pvak/verg bodies and social-insurance issuers), ministries (abbreviations and full designations, historical included), collections (the 7 announcement collections and their parameter matrix), stages (lawmaking pipeline), changed_since_intervals, section_types, gazette_parts (BGBl parts and era tiers), law_types, district_authorities (all Bezirksverwaltungsbehörden), justiz_subject_areas (Fachgebiet taxonomy), search_syntax (operators and wildcard rules), and citation_formats (the shapes ris_lookup_citation parses). Recovery hints from the other ris_* tools route here.

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• practice_management_info

  HelloBooks AI Agents

  Return HelloCPA Practice Management info — the standalone product at practice.hellobooks.ai for running a CPA / CA / bookkeeping practice (proposals + CPQ, workflow, time tracking, billing, 6-role RBAC, Gmail/Outlook/Calendar sync, CSV migration from TaxDome / Karbon / Canopy). NOT the Partner Program and NOT a tier in list_plans. Per-user pricing model — US shipped at $9.99/user/month (free up to 2 users + 10 clients, 90-day trial, enterprise at 50+ users). 7 other markets (IN, GB, AU, CA, AE, SG, NZ) are roadmap as of 2026-06-12. Call with no args for the full 8-region matrix + features + meta, or with `country` for one region's status + pricing + competitor frame.

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• get_catalog

  Backtest360

  Fetch one engine reference catalog. Catalogs (cheap, cacheable per session): - 'operators' — comparison operators for condition expressions - 'execution-modes' — entry/exit anchors and fill algorithms, with the validity matrix by market type - 'stop-types' — stop-loss types, re-entry modes, and their parameters - 'sizing-methods' — position-sizing methods and their parameters - 'bar-frequencies' — supported bar frequencies and the signal x execution validity matrix (which combinations are allowed) - 'sections' — the full metric catalog: every statistic's stable id, display label, section, and description - 'sampling-modes' — Monte-Carlo resampling modes, each with its status and parameters Fetch the relevant catalog BEFORE building a strategy or config; build only from values it lists — never guess parameter names or frequencies.

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• nmlp_resolve

  New Mexico Literacy Project

  One Box: resolve ANY physical-media identifier in one call — auto-detects the type (Library of Congress LCCN, ISBN-10/13, CD DIDX hub code, label catalog number, UPC/EAN barcode, vinyl matrix/runout etching) and returns ranked typed candidates across NMLP's 48M-record indexes (20.9M LCCN + 8.7M CD + 19M vinyl). ISBN input returns the exact Amazon listing link (ASIN=ISBN-10), never a title search. Misses return Attested Absence: exactly which indexes were checked at what dataset version, so 'not found' is usable evidence. Use this when you don't know what kind of number you have; use the specialized lookups when you do. Identification only — does NOT appraise or value items; NMLP never buys books. Human tool: https://newmexicoliteracyproject.org/lookup

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• get_correlation_map

  mcp

  Get the cross-ticker ENTANGLEMENT map — which S&P 500 names the quantum model expects to co-move. "Entanglement" here is the Pearson correlation of the quantum model's own FORECAST return paths (mode="forecast", the default) — a forward-looking, model-implied co-movement signal. It is NOT a claim about realised market correlation, and you must not present it as one. mode="realized" instead correlates trailing daily returns (the consensus baseline) for comparison. Two ways to read it: • Diversification / risk lens — high entanglement means two names are effectively one trade. Stacking five mutually-entangled longs is one position with 5x the size, not a diversified book. • Pairs / divergence lens — strongly NEGATIVE entanglement flags names the model expects to move oppositely (hedge or pairs candidates). Honesty: every number is computed from real persisted scan output. Tickers with no usable forecast curve are listed in `missing`, never imputed. The universe is the top_n names ranked by 3mo forecast growth. Args: ticker: Optional. When set, returns only that ticker's row (correlations + most_entangled + most_divergent), not the full matrix. Case-insensitive. top_n: Universe size — top-N tickers by 3mo forecast growth (2–100, default 25). Smaller = tighter LLM payload. horizon: Forecast horizon to correlate — "1mo", "3mo", "6mo", or "1y" (default "3mo"). mode: "forecast" (model-implied, default) or "realized" (trailing-returns baseline). basis: Forecast-mode correlation basis. "mean" (default) correlates the single mean forecast curve. "ensemble" correlates across the full forecast band envelope (q05/q25/mean/q75/q95) and averages the per-band correlations — this surfaces TAIL co-movement (two names whose stress/downside paths move together) that the mean curve understates, which is exactly when diversification matters most. Ignored in realized mode. The method label becomes "quantum_forecast_ensemble" so you never conflate the two. Returns the full-matrix shape (tickers / matrix / top_pairs / missing) by default, or the single-ticker shape when `ticker` is given. Carries method, basis, generated_at, scan_date, and disclaimer in the payload.

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• query_jurisdiction_overlay

  CausalLayer MCP

  Multi-jurisdiction overlay (FK-METHOD-2026-004). Given a canonical attributable apportionment (party-id -> share), the union of all jurisdiction role tags on each actor, and the union of jurisdiction-specific flags, return side-by-side post-overlay shares for AU, EU, US, UK, CA (or a chosen subset) with the specific rules that fired in each, citation URLs, and a parties × jurisdictions matrix. v1 ships full implementations for AU and EU; US/UK/CA are research stubs marked `is_stub: true`. Use GET /api/v2/jurisdiction/catalog to discover support and stub status. Cost: 1 credit. Pure deterministic.

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• practice_management_info

  HelloBooks AI Agents MCP Server

  Return HelloCPA Practice Management info — the standalone product at practice.hellobooks.ai for running a CPA / CA / bookkeeping practice (proposals + CPQ, workflow, time tracking, billing, 6-role RBAC, Gmail/Outlook/Calendar sync, CSV migration from TaxDome / Karbon / Canopy). NOT the Partner Program and NOT a tier in list_plans. Per-user pricing model — US shipped at $9.99/user/month (free up to 2 users + 10 clients, 90-day trial, enterprise at 50+ users). 7 other markets (IN, GB, AU, CA, AE, SG, NZ) are roadmap as of 2026-06-12. Call with no args for the full 8-region matrix + features + meta, or with `country` for one region's status + pricing + competitor frame.

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• nmlp_vinyl_lookup

  New Mexico Literacy Project

  Identify a vinyl record from the matrix/runout etching in the deadwax (stamper suffixes vary copy to copy, so stable-prefix matching is built in), the label catalog number, or the barcode. Returns artist, album, year, label, catalog number. Backed by NMLP's Discogs-derived vinyl identifier index plus MusicBrainz fallback. Human tool (with a Goldmine-style grading checklist): https://newmexicoliteracyproject.org/vinyl-lookup

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• assay_pbo

  mcp

  Use this when you have the T x N returns of a grid search and want the probability of backtest overfitting (PBO, CSCV) -- did the sweep find an edge or manufacture one? A demote-only audit, not buy/sell advice. Did your parameter sweep FIND an edge -- or manufacture one? PBO over the whole trial matrix. Submit the full T x N payoff matrix of every configuration you tried (rows = time-ordered per-period returns, columns = the candidates from your grid search / parameter sweep / optimisation run) and get the Probability of Backtest Overfitting via combinatorial symmetric cross-validation (CSCV, Bailey/Borwein/Lopez de Prado/Zhu 2017) with purge and embargo against overlapping-label leakage: the fraction of train/test splits in which your in-sample winner ranks at or below the out-of-sample median. The dossier adds the performance-degradation slope (does out-of-sample decay as in-sample shines?), the probability of out-of-sample loss, and first-order stochastic dominance of your selected picks versus the whole pool. Complements the trial ledger: the ledger counts how many tries your family burned, PBO grades whether the SELECTION PROCESS itself is overfit -- PBO >= 0.5 earns the named demote PBO_HIGH. Exhaustive enumeration with nothing to seed, fail-closed on undersized, oversized or non-numeric input. Demote-only: it can kill a selection, never bless one. NOT a buy/sell signal. Price: per check; see https://api.alphaassay.com/v1/meta/pricing (api_key required -- account setup at https://api.alphaassay.com/account).

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• compare_listings

  Agentic.ai Directory

  Compare exactly two AI tools side-by-side. Returns structured field matrix and 'Choose A if... Choose B if...' verdict. Use this when a user wants to decide between two specific tools. For finding tools first, use search_listings or semantic_search.

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• connection-pool-calculator

  Utility Matrix

  Use this when a user asks how many database connections to configure in their connection pool, or is troubleshooting PostgreSQL connection exhaustion. Takes CPU cores, app instances, and max_connections. Returns recommended pool size per instance with utilization ratio.

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