"namespace:io.github.musette-cc" matching MCP tools:

• search_decisions_citing

  justicelibre

  Cherche les décisions qui citent EXPLICITEMENT un article de loi donné. Exploite l'index FTS5 sur les sources jurisprudence disponibles pour matcher les formulations courantes de citation (`"article 1382 du code civil"`, `"art. L. 1152-1 du Code du travail"`, etc.). Cross-référencement inverse : partant d'un article, on trouve la jurisprudence pertinente. **LIMITATION CONNUE** : ce tool trouve UNIQUEMENT les citations explicites du numéro d'article. Il ne capte PAS : - les références indirectes ("conformément aux dispositions du Code civil relatives à la responsabilité délictuelle…") - les renvois à une section entière sans numéro précis - les citations du code par abréviation seule sans article ("en vertu du CT") Pour une recherche conceptuelle plus large, préférer `search_all` avec l'expansion thésaurus (ex: "harcèlement" → inclut "intimidation" etc.). Args: code: code court de l'article (ex : "CT", "CC") num: numéro de l'article (ex : "L1152-1", "1240") sources: liste optionnelle de sources à interroger parmi ["dila", "jade", "cedh", "cjue"]. Par défaut : toutes. limit: nombre de décisions par source (défaut 20, max 50) Returns: dict `{"code", "num", "total", "per_source": {source: count}, "decisions": [{source, id, juridiction, date, title, extract}]}`

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• score_facility

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants an independent 0-100 grade for ONE existing facility across 7 dimensions — power, fiber, water, climate_risk, tax_environment, talent_pool, expansion. Example: "How does the CoreWeave Las Vegas site score, power-weighted?" — score_facility facility_id=<id> weighting=power_priority. Params: facility_id or name (required); weighting one of "balanced" (default) | "power_priority" | "risk_priority" | "expansion_priority". Returns: composite 0-100, tier_classification, peer comparison, and per-dimension detail. Do NOT use for a raw lat/lon parcel (use analyze_site), to compare 2 or more sites (use compare_sites), or to find similar sites (use find_alternatives).

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• compare_isos

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants a side-by-side of 2-4 ISO grids — fuel mix, demand, renewable/gas share, interconnection-queue depth, time-to-power — in one call instead of N sequential get_grid_intelligence calls. Example: "Compare PJM vs ERCOT vs CAISO on gas share, renewable share, and queue depth right now." — compare_isos isos="PJM,ERCOT,CAISO". Params: isos is a comma-separated list (2-4 max) drawn from the 7 live US ISOs: "PJM" | "ERCOT" | "CAISO" | "MISO" | "SPP" | "NYISO" | "ISO-NE". Returns: {isos[], comparison:{<iso>:{demand_mw, generation_mix_pct, renewable_share_pct, gas_share_pct, constraint_score, excess_power_score, avg_time_to_power_months, queue_depth_gw, retail_price_cents_kwh}}, as_of}. Do NOT use to rank ALL grids globally (use get_grid_scoreboard) or for the single-ISO deep brief (use get_grid_intelligence).

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• remember

  Weather Gc Ca

  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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• get_inflections

  slavonic

  Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} per the declared outputSchema — switch on result.category ('nominal' | 'verbal' | 'not_found') before reading the body. Content from en.wiktionary.org (CC BY-SA 4.0).

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• latin_get_inflections

  hub

  Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} per the declared outputSchema — switch on result.category ('nominal' | 'verbal' | 'not_found') before reading the body. Content from en.wiktionary.org (CC BY-SA 4.0).

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• slavonic_get_inflections

  hub

  Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} per the declared outputSchema — switch on result.category ('nominal' | 'verbal' | 'not_found') before reading the body. Content from en.wiktionary.org (CC BY-SA 4.0).

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• query_power_interconnection_queue_ercot_v1

  osint

  Query the ERCOT generator interconnection queue — ERCOT's public GIS Report (EMIL PG7-200-ER), the waiting line of generation projects that have REQUESTED to connect to the ERCOT (Texas) grid. Returns cited, project-level records with ERCOT's full published structure across four lifecycle sheets (Large Gen + Small Gen = active; Inactive Projects; Cancellation Update): the requested `capacity_mw` (ERCOT publishes ONE capacity figure — no summer/winter split), the ERCOT `fuel` and `technology` codes (e.g. SOL/PV solar, OTH/BA battery, GAS/CC combined-cycle, WIN/WT wind — HYD is HYDROGEN, hydro is WAT), the `cdr_reporting_zone` (NORTH/SOUTH/WEST/COASTAL/HOUSTON/PANHANDLE), the `interconnecting_entity`, the `poi_location`, the composite `gim_study_phase` token string, and the milestone dates (`screening_study_started`, `fis_approved`, `ia_signed`, `construction_start`/`construction_end`, `approved_for_energization`/`approved_for_synchronization`, `projected_cod`). Group or filter by `application_status`, `size_category`, `fuel`, `technology`, `cdr_reporting_zone`, `county_fips`, `state`, `gim_study_phase`, or `interconnecting_entity`; filter `projected_cod` by the `projected_cod_from` / `projected_cod_to` range. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"application_status": "ACTIVE", "fuel": "SOL"}` for active solar requests; `{"application_status": "ACTIVE", "group_by": ["fuel"], "order_by": "capacity_mw", "top_n": 5}` for the active pipeline's biggest fuels by requested MW. The GIS Report is published MONTHLY and its full history is queryable — this is NOT a single point-in-time snapshot. Omit `as_of` for the latest month, or pass `as_of` (a date) to get the queue as it stood at a past month: `as_of` resolves to the newest monthly snapshot at or before it, with vintages back to 2018-12 (the floor; an earlier `as_of` is refused, naming the floor). Example: `{"application_status": "ACTIVE", "as_of": "2019-06-30"}` returns the active queue as of mid-2019. Each month is a full point-in-time snapshot (a project that has since withdrawn is simply absent from later months — query the earlier `as_of` to see it), so a multi-month trend is one query per month; `as_of` is the history axis, not a row filter. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `capacity_mw` is REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built. NEVER read a queue-MW total as installed or operating capacity — it is additive across distinct rows but is a REQUESTED total only. ERCOT prints NO status column, so `application_status` is derived from the sheet ERCOT files the project on: `ACTIVE` is the live pipeline (Large/Small Gen), `INACTIVE` and `CANCELLED` are projects that recently left the queue (the Inactive / Cancellation sheets list RECENT departures, NOT the full historical withdrawn set). The build-progress reading is carried SEPARATELY in `gim_study_phase` (e.g. "SS Completed, FIS Completed, IA") + the milestone dates and is never collapsed into `application_status`. For built/operating capacity use query_power_capacity_v1. ERCOT only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for PJM use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York) queue use query_power_interconnection_queue_nyiso_v1; for the ISO-NE (New England) queue use query_power_interconnection_queue_isone_v1; for the SPP (central US) queue use query_power_interconnection_queue_spp_v1. This tool serves ERCOT's GIS Report, which is GENERATION-only; ERCOT's separate large-load / data-center interconnection queue is an unstructured source (TAC-meeting PDF slides) and is NOT served here, and this tool does not infer which projects are data-center-driven — that interpretation is the analyst's, from cited rows.

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• quizbase_list

  QuizBase

  Cursor-paginated browse over the catalog. Quality-first: by default excludes questions flagged for review (use quality='all' for full pool). USE WHEN: full catalog sync, delta sync (updated_since), exhaustive enumeration by filter. NOT WHEN: you only need N random samples (use quizbase_random) or a single record (use quizbase_question_by_id). PAGINATION: stable cursor over id UUIDv7 DESC. First call: omit cursor. Next: pass meta.nextCursor. Stop when nextCursor is null. KEY FILTERS (full parity with REST): - lang: ISO 639-1, default "en". Supported: en, pl. - category (slug), difficulty (trivial|easy|medium|hard|expert — LLM-calibrated), type (multiple|boolean), subcategory (raw slug). - tags (AND), tags_any (OR, max 10): raw tag slugs. - topic (curated, alias resolver), topics_any (OR over curated): higher precision than tags. - regions (cultural affinity, AND): empty = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source (array): include only these of 12 (opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). - exclude_source (array): drop these sources, e.g. ["entityq"]. Applied after source. - license (SPDX): e.g. CC-BY-SA-4.0, MIT. - quality: 'high' (default) = cleanest, most broadly-useful. 'standard' = broader pool incl. niche/too-specific. 'all' = full pool incl. flagged; when 'all', each question gains a "quality" field ('high' or 'needs_review'). - updated_since (ISO 8601): only questions updated after this — for delta sync caches. BATCH + TRANSLATION MAPPING: - ids (up to 250): fetch those exact records in one call (anti-repeat, deep-links, restoring a saved set). Terminal selector — browse filters and cursor are ignored. Missing ids → meta.missing. - content_language (en|pl): with ids, returns each question's sibling in that CONTENT language across the translation chain — the same questions in another language. Distinct from lang (labels only). PAGINATION + COUNTING: - cursor (string): from previous meta.nextCursor. Omit for page 1. - limit (1-100, default 20). - count: none (default, skip — page via nextCursor) | exact (precise COUNT(*), index-only ~25-90ms). OUTPUT: { questions: [...], meta: { count, countMode, language, nextCursor, total? } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions. ATTRIBUTION REQUIRED if you redistribute. Credit each question using its own attribution object — see license + licenseUrl + modifications fields per record. COMMON MISTAKES: not passing the cursor on subsequent calls (you'll re-read page 1); polling without updated_since when doing delta sync.

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• get_grid_scoreboard

  DC Hub — Data Center Intelligence MCP Server

  Live GLOBAL grid scoreboard — 7 US grid operators (PJM, ERCOT, CAISO, MISO, SPP, NYISO, ISO-NE) + Great Britain (NESO) + 24 European bidding zones (Germany, France, Netherlands, Italy/Milan, Spain, Poland, Switzerland, Portugal, the Nordics + Central/Eastern Europe — via ENTSO-E) + Taiwan (Taipower) + Australia NEM (AEMO), ranked side-by-side RIGHT NOW: renewable share %, gas share %, full fuel mix (gas/nuclear/coal/wind/solar/hydro MW), and demand. One call answers "which grid worldwide is greenest, or most gas-reliant, for siting a data center?" — vs compare_isos (pairwise) or get_grid_data (single ISO). US + GB + EU all rank by wind+solar+hydro share (apples-to-apples); AU is listed unranked (its feed reports a variable-renewable floor only, no full fuel split — kept honest). Source: US = EIA hourly RTO; GB = Elexon Insights; EU = ENTSO-E Transparency; AU = AEMO NEM — all live via DC Hub, greenest-first. Quote with attribution to DC Hub (CC-BY-4.0). Try: get_grid_scoreboard.

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• search_within

  Czso Cz

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• get_inflections

  latin

  Fetch the full declension (nominals) or conjugation (verbs) table for a lemma identified by a search result's inflection handle (entry_id + word_class). Use this only when a search ran without inline forms or left a match un-expanded — a default search already returns each match's table inline. Returns Markdown plus the table as structuredContent with the shape {"result": <paradigm>} per the declared outputSchema — switch on result.category ('nominal' | 'verbal' | 'not_found') before reading the body. Content from en.wiktionary.org (CC BY-SA 4.0).

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• deep_research

  Weather Gc Ca

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1241 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 4,770 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Expect 15-60s.

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• entity_profile

  Weather Gc Ca

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

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• recent_changes

  Weather Gc Ca

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• global_indicator

  dynamic-feed

  A flagship development statistic from Our World in Data: the latest value for a country plus a short multi-year trend, with full source attribution. ONE source, MANY indicators (breadth) — CO2 per capita, population, fertility, urbanisation, GDP-per-capita (a development stat in PPP, NOT a market price), extreme poverty, R&D spend, Human Development Index, literacy, internet access, electricity access. Distinct from `global_macro` (World Bank): OWID adds the long-run development + climate set. `indicator` = a slug/alias from the curated allowlist (default "co2-emissions-per-capita"; aliases: co2, pop, gdp, hdi, literacy, internet, poverty, fertility, urban, rd) — call indicator="list" for the full menu. `country` = ISO-3 code (AUS, USA, CHN, GBR, IND, …); omit for the World aggregate. Source: Our World in Data (ourworldindata.org) — OWID's processing layer is CC BY 4.0, keyless; every response carries BOTH OWID's attribution AND each underlying producer's citation + licence. Only indicators whose underlying sources are cleared for commercial re-serving (CC BY / CC BY IGO / CC0 / public domain) are served — a fail-closed runtime gate refuses any non-redistributable indicator. Annual-ish statistics, not a live-telemetry feed. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• pollen_forecast

  dynamic-feed

  Airborne pollen / allergen forecast — six pollen types (alder, birch, grass, mugwort, olive, ragweed) in grains/m³, each with a plain-English severity band (low / moderate / high / very high), for a hay-fever / asthma / rhinitis audience. Returns the current hour's reading plus the NEXT-DAY peak per type, an overall worst-case level, and a summary naming the dominant allergens. `location` = city name or preset; EU presets carry real data (london [default], paris, berlin, madrid, rome, amsterdam, vienna, warsaw, athens, dublin), AU/world presets (brisbane, sydney, melbourne, perth, new york, tokyo) are convenience but normally report `coverage` = out_of_region. Or pass explicit `lat` + `lon`. COVERAGE IS EUROPE-ONLY: the CAMS pollen model covers Europe (~4-day forecast in season); outside Europe — including all of Australia, the Americas, Asia, Africa — there is NO pollen model and the response says so honestly (`coverage` = out_of_region) instead of guessing. DISTINCT from `air_quality` / `air_quality_fusion` (those serve POLLUTANTS — PM2.5/ozone/AQI; this serves BIOLOGICAL pollen). Source: Open-Meteo Air Quality API / Copernicus Atmosphere Monitoring Service (CAMS) European ensemble — CC BY 4.0 under Open-Meteo's paid commercial plan (keyless to you); attribution to Open-Meteo + CAMS carried in provenance. Informational only, not medical advice. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• rooms_messages

  DC Member API

  GET /rooms/:roomID/messages — List messages in a room List messages in a room you are a member of. **Read-only — no write side effects, no unread-state mutation, no reactions/posts/edits.** Cursor-paginated newest-first. **Access:** strict — the caller must be a subscribed member of the room (same `seen` doc check used by the web inbox). For browsable public channels, any DCer can read. Private rooms, DMs (`dm`), group DMs (`group`), event rooms, and city/country/mastermind rooms hard-block non-members with 403. Hidden/deleted/sunk messages are excluded. **Pagination:** pass `?before=<nextCursor>` from a previous response to fetch the next (older) page. Default page size 50, max 50. **See also:** For specific content in this room (`did anyone mention X?`), `POST /search/messages` with `q=` and `roomID=` searches body text directly — far faster than paginating with `?before`. This endpoint is the right call when you want a chronological window (last N messages, conversation reconstruction); search is the right call when you want a topic.

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• traverse_coordination

  Warmth Engine Observatory

  Walk the Coordination Topology lineage graph (precomputed lookups over the parent→child Coordination Connection DAG). `mode="path"` returns every path between `source` and `target`, each with its CC-type (connection-type) sequence and whether it is pure lineage (homogeneous) or crosses a sibling/bidirectional edge; `mode="ancestors"`/`"descendants"` return the nodes reachable up/down the hierarchy; `mode="neighbours"` returns direct parents and children. For a node's structural metrics or the whole-graph summary rather than walks, use `get_topology`. Optional `cc_types` restricts edge types, `max_depth` caps path length, `homogeneous_only` drops any path crossing a sibling edge.

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• get_metro_fiber

  DC Hub — Data Center & Power Intelligence

  Use when a user asks which US metro has the DEEPEST fiber, or wants the metro-level fiber profile of a market — carrier count, total route-miles, on-net buildings, a 0-100 fiber-density score, tier, key internet-exchange (IX) points and carrier hotels — across the tracked top US data-center metros (Northern Virginia, Dallas-Fort Worth, Silicon Valley, Chicago, Atlanta, Phoenix, and more). Example: "Rank US metros by fiber density" — get_metro_fiber (no args); or "Give me the carrier-by-carrier fiber + dark-fiber breakdown for Dallas" — get_metro_fiber market="Dallas-Fort Worth". Params: market (optional metro name OR slug, e.g. "Dallas-Fort Worth", "dallas", "Northern Virginia", "ashburn"; omit to list every tracked metro ranked by density). Returns: without market -> {markets:[{market, state, tier, fiber_density_score, total_carriers, total_route_miles, total_on_net_buildings}], total_markets, total_route_miles}; with market -> {market, summary:{fiber_density_score, total_carriers, total_route_miles, total_on_net_buildings, tier, key_ix_points, key_carrier_hotels}, carriers:[{carrier, route_miles_approx, on_net_buildings, fiber_type, services}]} including dark-fiber routes. Cite DC Hub (dchub.cloud, CC-BY-4.0). Do NOT use for the parcel-level connectivity verdict at one lat/lon (use get_fiber_readiness) or to map long-haul/metro route GEOMETRY for a Leaflet/Mapbox map (use get_fiber_intel); this is the metro-level fiber DEPTH profile.

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