"namespace:io.github.Melusi-M" matching MCP tools:

• earthquake_get_feed

  earthquake-mcp-server

  Fetch a USGS pre-computed real-time earthquake feed by magnitude tier and time window. These feeds are CDN-cached by USGS and faster and more available than the query API — use them for "what's happening now" queries. "all" includes microseisms (M<1); "significant" is a USGS curation based on magnitude, felt reports, and PAGER impact estimates. "hour" returns 0–10 events typically; "month" can exceed 10,000 for the "all" tier. For historical or filtered queries, use earthquake_search instead.

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• emem_elevation

  emem — Earth memory protocol

  One-shot elevation answer that fuses Cop-DEM 30 m (land), GMRT (ocean topobathy), and ESA WorldCover (water mask) into a single signed scalar at a place or coordinate. Returns `elevation_m`, the source actually used, and a `coherence_note` when the two surfaces disagree at the coast. When to use: Use when the user asks 'how high is X' or 'what's the elevation at this lat/lng' and you want the correct answer regardless of whether the cell is land, water, or coastline — the handler picks Cop-DEM for land and GMRT for water and surfaces the choice. Pass `place` (free text), `lat`+`lng`, OR `cell`. Otherwise, prefer emem_recall with `copdem30m.elevation_mean` / `gmrt.topobathy_mean` individually.

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• emem_ndvi

  emem — Earth memory protocol

  Recall Sentinel-2 NDVI (indices.ndvi, 10 m native) at a point or place. Composes locate → cell64 → recall in one call; auto-materializes on miss. When to use: Use when the user names a place (or lat/lng) and just wants the NDVI number. Polygon-resolved places default to a 16-cell fan-out aggregated as mean/median. Set `n_cells: 1` for point behaviour. For multi-band batches use emem_recall.

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• arbitration_awards_lookup

  mcp-knowledge

  Commercial arbitration intelligence for litigation lawyers, M&A due diligence teams, sovereign wealth funds and trade finance compliance. Covers 8 major institutions: ICC, AAA, LCIA, HKIAC, SIAC, CIETAC, DIAC, ICDR. Three modes: • party_lookup — find awards by party name (searches 20 landmark public awards + JusMundi best-effort) • institution_index — browse awards and caseload stats per institution with date range filter • clause_check — audit an arbitration clause for missing elements (institution, seat, language, arbitrator count, governing law, binding nature) Note: Most arbitration awards are confidential. This tool surfaces public awards (Yukos, Crystallex, Achmea, etc.) plus redacted statistics from institutional annual reports. Private awards are not accessible. Cache: 24h (arbitration data is very stable). No API key required.

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• enrichir_concurrents

  france-data-mcp

  Enquête approfondie sur le top concurrents (V0.23). Pour chaque FINESS : statut actif + taille d'équipe + historique récent (inspect_site), signal M&A — rebranding en cours — (compare raison sociale FINESS vs RPPS), groupe parent (entreprise_by_siren : Biogroup/Cerballiance/… + `est_grand_groupe`). Cap dur `max=3` (inspect_site ~7 K tokens/appel — JAMAIS 10+). Drapeau `couverture` PAR concurrent (`"ok"` | `"partiel:<raison>"`) : un concurrent qui échoue n'annule pas les autres. Typiquement appelé sur `concurrents.top[0..2].finess` renvoyés par panorama_implantation_complet. Sources : FINESS/ANS, RPPS/ANS, SIRENE/DINUM.

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• openlibrary_get_cover_url

  openlibrary-mcp-server

  Resolve a cover image URL for a book or author photo. Returns a direct HTTPS URL in the requested size (S/M/L). The Covers API always returns HTTP 200 — missing covers return a 1×1 placeholder GIF, not a 404. URLs can be embedded in markdown as .

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• rpps_search_by_name

  france-data-mcp

  Trouve un PS par identité (matching trigram tolérant aux accents/typos). Usage : "Dr Martin à Paris" → `nom: "Martin", departement: "75"`. Nom obligatoire ; `prenom` et `departement` affinent. Tri par `match_score` ∈ [0..1] décroissant (score trigram pg_trgm). Un score <0.5 = homonymie partielle à confirmer côté caller. Sans `departement`, des homonymes exacts ("Pierre Martin") ont TOUS le même score ~1.0 et ne sont pas départagés — toujours filtrer par dept ou prénom sur un nom commun. `truncated: true` = d'autres résultats existent (restreindre, ne pas parcourir). Chaque résultat géolocalisé porte `geo_precision` ∈ {`"adresse"`, `"etablissement_finess"`, `"centroide_commune"`} — lire ce champ pour évaluer la fiabilité des `coords` (précise BAN/FINESS au m près vs centroïde commune ~3 km, non discriminant intra-commune). Catégorie par défaut : Civil (C, ~97 % — libéraux, salariés privés, hospitaliers contractuels). Opt-in : `include_agents_publics: true` ajoute Agents publics (M, ~0,3 % — PH titulaires, ARS, CNAM, Éducation nationale, PMI, militaires SSA) ; `include_etudiants: true` ajoute Étudiants (E, ~2,5 % — internes, externes, élèves IDE/SF). Réf : https://mos.esante.gouv.fr/NOS/TRE_R09-CategorieProfessionnelle/. Source : Annuaire Santé, Agence du Numérique en Santé (ANS) — Licence Ouverte v2.0

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• emem_field_boundaries

  emem — Earth memory protocol

  Per-field agricultural-boundary polygons from the Fields of The World global product (~3.17B fields, 241 countries, 10 m resolution, CC-BY-4.0). Returns a GeoJSON FeatureCollection with the polygon geometries, FIBOA-compatible properties, and a planar `area_m2` per field — plus provenance (source CID, provider URL, license, attribution). When to use: Call when the user asks about farms, fields, parcels, croplands, plots, or agricultural boundaries inside a region — anywhere the OSM/Nominatim boundary alone is too coarse (the OSM polygon for a farm is its estate envelope; this returns the individual field polygons inside). Pass `place` (free-text) or `polygon_bbox`. For farms wider than ~10 km², split the bbox: the fetcher caps each call at 16 covering tiles. The receipt quotes `license: CC-BY-4.0` and `attribution: Fields of The World / Taylor Geospatial Institute` — surface both with any rendered map. For a one-shot "facts at every cell inside the farm PLUS the field polygons", call `emem_recall_polygon` with `include: ["ftw_fields"]` instead.

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• corporate_registry_lookup

  mcp-knowledge

  Resolve legal information about a company from its national corporate registry. Returns a normalised, sourced company profile: legal status, registration number, directors, shareholders, recent filings, registered address, share capital, and a quality score (0–100). Coverage: France (INPI, keyless — full SIREN/SIRET with directors), 3M+ entities worldwide via GLEIF LEI (keyless, large companies), UK (Companies House, optional key), Netherlands (KvK, optional key), and OpenCorporates (token required since 2026). Sources are tried in cascade; quality_score increases with each source that succeeds. When to use: due-diligence, KYC screening, supplier verification, M&A research, or any workflow needing verified company identity and legal status. Optional env vars: COMPANIES_HOUSE_API_KEY (UK), KVK_API_KEY (NL), OPENCORPORATES_API_TOKEN (OpenCorporates token).

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• list_structure

  Tripitaka MCP (hosted)

  Show the structure of all three pitakas with coverage statistics. 💡 **Use this tool when:** - The user asks for an overview of the Tipiṭaka (what's in it / which collections). - You need to check coverage before promising a search will find something — `segment_count > 0` is the active-loaded signal. - Verifying scope when compiling an artifact. 📊 **Current state (v1.1+, at parity with SuttaCentral bilara-data):** - **Sutta Piṭaka** complete: DN 37, MN 155, SN 1,829, AN 1,419, KN 2,351 sections (~284,702 segments) — Pāli + Sujato EN - **Vinaya Piṭaka** complete: Bhikkhu Vibhaṅga 222, Bhikkhunī Vibhaṅga 127, Khandhaka 22, Parivāra 51 + Pātimokkha 2 (~71,557 segments) — Pāli + Brahmali EN - **Abhidhamma Piṭaka** complete: 7 books (ds, vb, dt, pp, kv, ya, patthana) ~88,414 segments — Pāli only (bilara has no English for any Abhidhamma book) - **Total ~444,673 segments** in the DB ⚠️ **Known quirks:** - The schema carries duplicate legacy + SC-modern codes side by side: - Vinaya: `vin-v/vin-m/vin-c/vin-p` (legacy, segment_count = 0) alongside `pli-tv-bu-vb/pli-tv-bi-vb/pli-tv-kd/pli-tv-pvr` (active, populated). - Abhidhamma: `ym/pt` (legacy = 0) alongside `ya/patthana` (active). - **Always pick the code with `segment_count > 0`** — the others are metadata placeholders from an older migration. 🌐 **Languages:** Returns Pāli + Thai + English labels regardless of enabled set (these are metadata, not segment text). Text content follows ENABLED_LANGUAGES. Thai translations aren't loaded yet — Thai users can fall back to the cross_reference 84000.org link. Returns: Hierarchical structure: - pitakas{vinaya/sutta/abhidhamma} → nikayas[] - Each nikaya: code, name (3 languages), sutta_count, segment_count.

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• compare_sdp_offer_answer

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Diff a SIP/SDP offer and answer and surface the issues that actually break calls in practice: codec intersection per m-line, direction compatibility (sendrecv ↔ recvonly), DTLS setup-role conflicts (active+active / passive+passive), rtcp-mux / BUNDLE asymmetry, missing DTLS fingerprints when DTLS-SRTP is negotiated, ICE asymmetry, and fax reinvite mismatches (e.g. offer `m=image udptl t38` answered with audio-only, or `T38FaxVersion` / `T38FaxMaxBuffer` / `T38FaxRateManagement` drift). Use when the user has both halves of a negotiation and is debugging 488 Not Acceptable Here, no-audio, one-way-audio, or a failed T.38 reinvite (488 / 415 / 606 on a `m=image` offer). Pair with: `parse_sdp` to inspect either side in isolation; `search_sip_docs(vendor=...)` to ground vendor-specific fixes (FreeSWITCH `mod_spandsp`, Cisco CUBE `fax protocol t38`); `lookup_response_code(488)` for the static SIP-side context.

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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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• get_insider_transactions

  Valuein — SEC EDGAR Fundamentals

  Form 3 / 4 / 5 / 144 line items for a US public company. Returns each transaction (or initial holding / proposed sale) with the insider's name, role, transaction code, share count, price, and notional. Filters by lookback window, transaction code (P=purchase, S=sale, A=grant, M=option exercise, F=tax withholding, etc.), insider role, and minimum share threshold. Institutional tier only — sample / sp500 / pro return ENTITLEMENT_DENIED with an upgrade link.

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• GetFredSeriesData

  Fintel Discovery — Financial Intelligence for AI Agents

  Fetch time-series observation data from FRED for a specific economic series. Returns date + value pairs with series metadata (title, units, frequency). Use SearchFredSeries first if you don't know the series ID. Use this tool when: - You need historical macro data (rates, inflation, GDP, unemployment) - You want to provide macro context alongside advisor or fund data - You are comparing economic conditions across time periods - You need the current value of a key economic indicator Pass observation_start / observation_end to limit the date range. Pass frequency to aggregate (e.g. 'm' for monthly, 'q' for quarterly). Requires FRED_API_KEY environment variable (free at fred.stlouisfed.org). Source: Federal Reserve Bank of St. Louis FRED API.

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• parse_sdp

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Parse a Session Description Protocol body and return a structured view: origin, session, timing, per-media codecs (rtpmap + fmtp), direction, DTLS setup + fingerprint, ICE credentials + candidates, rtcp-mux, BUNDLE groups, fax-relay (`m=image udptl t38` plus the `a=T38Fax*` attribute family), and crypto attributes. Useful for debugging WebRTC ↔ SIP interop (codec negotiation, DTLS-SRTP fingerprints, ICE candidate gathering, bundle alignment), and for inspecting fax negotiation (T.38 reinvite SDP, `T38FaxMaxBuffer`/`T38FaxUdpEC`/`T38FaxRateManagement`) without an LLM having to re-derive the SDP grammar each call. Pair with: `compare_sdp_offer_answer` when the user has both halves of the negotiation (including T.30→T.38 reinvites); `webrtc_sip_checklist` for the bridge-config angle.

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• emem_grid_info

  emem — Earth memory protocol

  Active grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage. When to use: Call once at session start (or when the user asks about cell resolution / 'how big is a cell'). Returns the actual ground resolution today (~9.54 m × 9.55 m square at the equator (lat 21 bits × lng 22 bits, matching Sentinel-1/Sentinel-2 native pixel pitch). The cell64 bit layout reserves a resolution-tag field for future hierarchical refinement targeting H3-equivalent res-13 (~3.4 m) cells in v0.1.) and the spec target. Useful before you reason about whether one cell is enough or whether you need `emem_recall_polygon`.

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• report_get

  Kash.click

  Download a synthetic HTML sales report for a given period. Period logic: omit all date fields to get yesterday's report; provide y only for a full-year report; y + m for a full-month report; y + m + d for a specific day. Returns an HTML summary including total revenue, number of orders, breakdown by department, VAT summary, and payment methods.

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• get_rental_yield

  Cenogram - Polish Real Estate Data

  Estimate the gross rental yield for a Polish city or county: annualized median asking rent (PLN/m²/month × 12) divided by the median apartment transaction price per m² (secondary market) from the RCN registry. Gross and top-line only — excludes vacancy, management, tax and maintenance. Indicative, not investment advice. Address by location (city name → resolves to a county) OR teryt (4-digit county code; teryt wins when both are given). Both sides need at least 5 samples or the result is suppressed. Not comparable across cities with different as_of dates (RCN publication lag varies by county). Rent and transaction prices come from different sources, so the yield is an approximation.

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• financial_model_3statement

  mcp-knowledge

  Pure-compute 3-statement financial model builder (Income Statement + Balance Sheet + Cash Flow). Feed assumptions (revenue growth, COGS%, OpEx, CapEx, working capital, tax rate, depreciation, debt schedule) → receive a full 3-5 year projection with integrated DCF valuation. Supports IFRS / US_GAAP / PRC_GAAP (中国会计准则) norms with bilingual ZH+EN labels for PRC. Modes: build (full 3-statement model) | scenario_analysis (base/bull/bear ±20% growth) | sensitivity (1 KPI × 1 input, 5-point grid). No external data needed — all computed from assumptions. ICP: VC due diligence, M&A analysts, CFO SMB, startup founders pitching investors, biotech/SaaS modeling. Returns balance_check_ok per year, DCF enterprise/equity value, and coherence warnings.

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