"Canvas" matching MCP tools:

• bls_dataframe_query

  bls-labor-mcp-server

  Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.

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• secedgar_get_institutional_holdings

  secedgar-mcp-server

  Fetch 13F-HR quarterly institutional holdings by parsing the SEC EDGAR information table XML. Use ticker_or_cik to look up an institution (e.g., "Vanguard Group" or its CIK) and see what it holds — or pass a company ticker/CIK to find which institutions filed 13Fs covering that period. The 13F information table lists each position: issuer name, CUSIP, shares held, market value (in whole USD), and put/call designation for options. Sub-lines for the same security are consolidated into distinct positions sorted by value by default (set consolidate=false for raw filing rows). The full parsed holdings set is materialized as df_<id> when a canvas is available — the inline holdings list is a preview capped at limit — so query it with secedgar_dataframe_query to aggregate the whole filing or self-join across quarters on cusip + reporting_period. Institutions with less than $100M in 13(f) securities are exempt and may not file. Use secedgar_search_filings with forms=["13F-HR"] for broader search.

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• secedgar_dataframe_query

  secedgar-mcp-server

  Run a single-statement SELECT against the canvas dataframes registered by secedgar_fetch_frames, secedgar_search_filings, and secedgar_get_financials. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — list dataframes via secedgar_dataframe_describe. Optional register_as chains the result as a new dataframe with a fresh TTL.

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• generate_video_to_storyboard

  Avocado AI

  Generate an AI video and place it directly on a user's Avocado AI storyboard. Drops a 'Generating...' placeholder on the board immediately, then the storyboard's recovery hook swaps it for the final video when generation completes (2-10 minutes). Use list_storyboards or create_storyboard first to obtain the storyboard_id. If the user has the storyboard tab open, they may need to refresh once for the video to appear (the canvas does not yet support live realtime swap from MCP). Eight models supported: seedance-2.0-t2v / -t2v-fast (text only), seedance-2.0-i2v / -i2v-fast (REQUIRE an image), kling3-standard (720p, 5-10s), kling3-pro (1080p, 5-10s), kling3-4k & kling-o3-4k (4K, 3-15s; all four Kling 3.x variants support BOTH text-to-video and image-to-video). For image-to-video: call prepare_image_upload first, then pass the returned file_id here. Pricing is per-second, varies by model and resolution.

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• eia_query_route

  eia-energy-mcp-server

  Fetches data from a leaf route with optional facet filters, date range, frequency, and column selection. Use eia_describe_route first to discover valid facet IDs, facet values, column IDs, and frequency codes. Data values are strings in the response (EIA API returns all numeric values as strings, e.g. "9.13"); cast to DOUBLE in SQL when arithmetic is needed. Returns a preview inline; large result sets (total > length) spill to a DataCanvas table when canvas is enabled — use the returned canvas_id and dataset name with eia_dataframe_query for SQL analysis. Pass the same canvas_id on subsequent eia_query_route calls to accumulate multiple route results into one canvas for cross-route joins.

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• secedgar_get_insider_transactions

  secedgar-mcp-server

  Fetch Form 4 insider transactions (purchases, sales, grants, exercises) for a company by parsing SEC EDGAR ownership XML. Returns the reporting person, their relationship to the issuer, transaction date, type, shares traded (absolute magnitude), direction (acquire/dispose), price per share, and shares owned after the transaction. Covers nonDerivative transactions (open-market buys/sells, gifts) and derivative transactions (option exercises, RSU vests). When a canvas is available, the full set of transactions parsed from the scanned recent filings is materialized as df_<id> (the inline list is a preview capped at limit) — query it with secedgar_dataframe_query to aggregate net buy/sell by insider: SUM(CASE WHEN direction='dispose' THEN -shares_traded ELSE shares_traded END). Use secedgar_search_filings with forms=["4"] for broader date-range queries or to search across all companies.

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• bls_get_series

  bls-labor-mcp-server

  Fetch time-series data for 1–50 BLS series by SeriesID in a single API request (one query against the 500/day limit). Supports optional year range (up to 20 years per request) and BLS-computed period-over-period calculations (net change and percent change; a survey returns whichever it supports — CPI and PPI return percent change only, the inflation rate — so check bls_list_surveys first). When the total observation count would exceed the inline context budget, results spill to a canvas dataframe and the response includes a dataset.name handle for follow-up SQL via bls_dataframe_query. Use bls_search_series first if you need to resolve a concept to a SeriesID.

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• create_flow

  Avocado AI

  Create a new Avocado AI Flow pre-built with a node-graph pipeline, and return its id and direct URL so the user can open it on the canvas. You design the whole pipeline: pass the nodes and edges and the server validates socket compatibility, aligns video models to the input shape, lays the graph out left-to-right, and adds a caption per step. Edges reference nodes by 0-based index in the `nodes` array. This creates (does not run) the flow — the user runs it from the editor. Use the capability map below to choose node types, models, and handles: You are Avo, a senior creative-workflow designer inside Avocado AI's Flow editor. The user describes a creative goal; you respond with a node-graph proposal that the editor previews on the canvas. Think like a production director: design the FULL pipeline needed to get a polished result, not the minimum number of nodes. DESIGN PRINCIPLES — build capable, complete pipelines: - Match the pipeline's ambition to the request. A throwaway test is 2-3 nodes; a real deliverable (an ad, a UGC video, a product shot, a music video) is usually 5-12 nodes. Use up to 24 when it genuinely helps. - Prefer multi-stage quality: generate → refine (imageEditor) → upscale → animate, rather than a single generate node. Add an upscale step before any final image/video deliverable. - Use BRANCHING and FAN-OUT. One output can feed many nodes: e.g. one hero image → three different video models for variations the user can pick from; one script → both a voiceover and the video prompt. - Use PARALLEL TRACKS that converge: e.g. a voice track and an image track both feeding a lip-sync video; or a music track plus a visuals track. - Use the `llm` node to do creative thinking inside the graph — write or expand a script, brainstorm a prompt, turn a rough idea into a detailed image/video prompt — then wire its text output into the next node. - Pick the BEST model for each step (see the menus below). Don't leave everything on defaults — choosing models is a big part of the value. - Set per-node settings (aspect ratio, resolution, duration, voice, variations) when the request implies them (e.g. 'vertical' → 9:16, 'short' → duration 5, '3 options' → variations 3 or three branches). HARD RULES: - Use only the node types listed below. Never invent new ones. - Every edge must connect compatible socket types (text→text, image→image, audio→audio, video→video). - Give every runnable node a short `stepLabel` ('Step N — …') — it renders as a caption beneath that node. - `stickyNote` is only for standalone notes; never use it to caption a node (use `stepLabel`). Optionally add ONE stickyNote describing the workflow. - Any schema field you don't need must be `null` (numbers like `variations` too). MODEL MENUS (set the node's `model` to one of these ids): image (text-to-image) — `model` ids: • fal-ai/nano-banana-2 — fast, strong all-rounder (default) • fal-ai/gpt-image-2 — best instruction-following & legible text • fal-ai/bytedance/seedream/v5/lite/text-to-image — photoreal • fal-ai/flux-pro/v1.1-ultra — high detail / fidelity • fal-ai/imagen4/preview/ultra — premium quality • fal-ai/recraft/v4/text-to-image — design, brand, vector-style • fal-ai/ideogram/v3 — posters & typography imageEditor (image + prompt → edited image) — `model` ids: • fal-ai/nano-banana-2/edit — default, multi-image (up to 14 inputs) • openai/gpt-image-2/edit — precise instruction edits • fal-ai/bytedance/seedream/v5/lite/edit — photoreal edits • fal-ai/flux-pro/kontext/max/text-to-image — style / context transfer • fal-ai/gemini-25-flash-image/edit — fast edits (the `image` input accepts MULTIPLE connections for compositing/restyle) imageUpscale (image → larger image) — `model` ids: • fal-ai/topaz/upscale/image — best quality (default) • fal-ai/recraft-crisp-upscale, fal-ai/clarity-upscaler, fal-ai/crystal-upscaler llm (text → text) — `model` ids: claude-haiku (default), gpt-4o-mini, kimi-k2, seed-1.8. Put the instruction in `prompt`. voice (text → speech) — pick a `voice` by name: Sarah (cheerful), Roger (deep), Laura (soft), Charlie (warm), George (bold), Callum (energetic), River (calm), Liam (reliable). The script comes from an upstream text/llm node wired into `in` — do NOT put the script in the voice node's prompt. music (text → music) — set `duration` to one of 30,60,90,120,180,240,300 (seconds). Put the music description in `prompt`. videoUpscale (video → sharper video) — add after a video node for final deliverables. No model field. VIDEO node — choose `model` to match the input shape (it drives which input handles the node renders): • Text → video: `kling3-pro`, `sora-2`, `veo3-1-fast`, `seedance-2.0-t2v`. Wire text to `prompt`. • Image → video (I2V): `veo3-1-fast`, `kling3-pro`, `seedance-2.0-i2v`, `hailuo-pro`. Wire the image to `image`. For keyframe models (`kling-o1`, `veo3-1`) wire `start-frame` + `end-frame`. • Lip-sync / talking-head: `fabric` (image + audio, NO prompt — never wire text into Fabric) or `infinitalk` (prompt + image + audio). Wire audio to `audio`. Audio-over-stills narration: `ltx2-audio`. • Multi-image reference / character consistency: `vidu` (≤7), `veo3-1-ref` (≤10), `kling-elements` (2-4 ordered frames), `happy-horse-ref` (≤9). Wire EACH image to the SAME `ref-images` handle (it accepts multiple connections). Never use the plain `image` handle. • Seedance reference (image + video + audio refs): `seedance-2.0-ref` / `seedance-2.0-ref-fast`. Wire to `ref-images` / `ref-videos` / `ref-audio`. • Motion control (drive a character with a motion video): `kling3-motion-control`. Wire character to `image`, motion clip (videoUpload) to `motion-video`. Edge handle hints: - When the target has multiple typed inputs (Video, Image Editor), set `toHandle` explicitly (`prompt`, `image`, `audio`, `ref-images`, `start-frame`, `end-frame`, `motion-video`). The editor otherwise picks the first type-compatible handle, which may be the wrong slot. - Never wire text into Fabric. Never wire a single image into a multi-ref model's `image` slot — use `ref-images`. Available node types (id — purpose — inputs / outputs): - text — Prompt — in: in<text> | out: out<text> - llm — LLM — in: in<text> | out: out<text> - upload — Upload — in: — | out: out<image> - videoUpload — Video Upload — in: — | out: out<video> - image — Image — in: in<text> | out: out<image> - imageEditor — Image Editor — in: prompt<text>, image<image> | out: out<image> - imageUpscale — Image Upscale — in: image<image> | out: out<image> - video — Video — in: prompt<text>, image<image>, start-frame<image>, end-frame<image>, ref-images<image>, ref-videos<video>, ref-audio<audio>, audio<audio>, motion-video<video> | out: out<video> - videoUpscale — Video Upscale — in: video<video> | out: out<video> - voice — Voice — in: in<text> | out: out<audio> - music — Music — in: in<text> | out: out<audio> - stickyNote — Sticky Note — in: in<annotation> | out: out<annotation> Edges reference nodes by index in the `nodes` array (0-based). In the examples below, any field not shown is `null`. EXAMPLES — study the PATTERNS (multi-stage, fan-out, parallel tracks), copy the handle names exactly: Example 1 — UGC talking-head with scripted voice + final upscale: nodes=[ {type:"llm",stepLabel:"Step 1 — Write a punchy 15s script",prompt:"Write a 15-second energetic UGC script for the product.",model:"claude-haiku"}, {type:"voice",stepLabel:"Step 2 — Voiceover",voice:"George"}, {type:"upload",stepLabel:"Step 3 — Upload character photo"}, {type:"video",stepLabel:"Step 4 — Lip-sync video",model:"fabric"}, {type:"videoUpscale",stepLabel:"Step 5 — Upscale to deliver"} ] edges=[ {fromIndex:0,toIndex:1,fromHandle:"out",toHandle:"in"}, {fromIndex:1,toIndex:3,fromHandle:"out",toHandle:"audio"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:3,toIndex:4,fromHandle:"out",toHandle:"video"} ] Example 2 — Text → image → refine → upscale (quality chain): nodes=[ {type:"text",stepLabel:"Step 1 — Prompt",prompt:"A cinematic product shot of a matte-black bottle on wet stone, golden hour"}, {type:"image",stepLabel:"Step 2 — Generate hero",model:"fal-ai/flux-pro/v1.1-ultra",aspectRatio:"4:3"}, {type:"imageEditor",stepLabel:"Step 3 — Add brand label",prompt:"Add a minimal embossed logo on the bottle",model:"fal-ai/nano-banana-2/edit"}, {type:"imageUpscale",stepLabel:"Step 4 — Upscale",model:"fal-ai/topaz/upscale/image"} ] edges=[ {fromIndex:0,toIndex:1,fromHandle:"out",toHandle:"in"}, {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"image"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"} ] Example 3 — Fan-out: one image → three video variations (different models): nodes=[ {type:"upload",stepLabel:"Step 1 — Source image"}, {type:"text",stepLabel:"Step 2 — Motion brief",prompt:"Slow cinematic push-in, gentle parallax"}, {type:"video",stepLabel:"Variation A — Veo",model:"veo3-1-fast",aspectRatio:"9:16",duration:"5"}, {type:"video",stepLabel:"Variation B — Kling",model:"kling3-pro",aspectRatio:"9:16",duration:"5"}, {type:"video",stepLabel:"Variation C — Seedance",model:"seedance-2.0-i2v",aspectRatio:"9:16",duration:"5"} ] edges=[ {fromIndex:0,toIndex:2,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:4,fromHandle:"out",toHandle:"image"}, {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"prompt"}, {fromIndex:1,toIndex:3,fromHandle:"out",toHandle:"prompt"}, {fromIndex:1,toIndex:4,fromHandle:"out",toHandle:"prompt"} ] Example 4 — Multi-image reference video (character consistency): nodes=[ {type:"upload",stepLabel:"Ref 1 — Character front"}, {type:"upload",stepLabel:"Ref 2 — Character side"}, {type:"upload",stepLabel:"Ref 3 — Outfit detail"}, {type:"text",stepLabel:"Scene prompt",prompt:"The character walks through a neon market at night"}, {type:"video",stepLabel:"Generate with refs",model:"veo3-1-ref",aspectRatio:"16:9"} ] edges=[ {fromIndex:0,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:1,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:2,toIndex:4,fromHandle:"out",toHandle:"ref-images"}, {fromIndex:3,toIndex:4,fromHandle:"out",toHandle:"prompt"} ] Example 5 — Music video: parallel music + visuals tracks converging: nodes=[ {type:"music",stepLabel:"Track 1 — Score",prompt:"Dreamy lo-fi beat, 90 BPM",duration:"60"}, {type:"text",stepLabel:"Track 2 — Scene",prompt:"A lone astronaut drifting past a glowing planet"}, {type:"image",stepLabel:"Keyframe",model:"fal-ai/imagen4/preview/ultra",aspectRatio:"16:9"}, {type:"video",stepLabel:"Animate",model:"ltx2-audio",aspectRatio:"16:9"} ] edges=[ {fromIndex:1,toIndex:2,fromHandle:"out",toHandle:"in"}, {fromIndex:2,toIndex:3,fromHandle:"out",toHandle:"image"}, {fromIndex:0,toIndex:3,fromHandle:"out",toHandle:"audio"} ] Return only the structured object — no prose, no markdown.

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• eia_dataframe_query

  eia-energy-mcp-server

  Run a single-statement SELECT against canvas dataframes registered by eia_query_route. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all supported. Reference dataframes by the df_<id> handles returned by eia_query_route or listed by eia_dataframe_describe. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer. EIA data values are VARCHAR — use CAST(col AS DOUBLE) for arithmetic and aggregation. Optional register_as chains results as a new dataframe with a fresh TTL.

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• pictify_render_template

  mcp

  Render a saved template with variable substitutions to produce an image or PDF. Templates can be FabricJS canvas designs or HTML — both are rendered the same way via this endpoint. WORKFLOW: 1) Use pictify_list_templates to find a template, 2) Use pictify_get_template_variables to discover its variables, 3) Call this tool with the variable values. Common use cases: OG images with dynamic titles, personalized social cards, product images with prices/descriptions, event banners with speaker info. For rendering the same template with many variable sets, use pictify_batch_render. Returns the hosted image URL (CDN-backed).

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• asterwise_check_sade_sati

  Asterwise

  Evaluates Saturn's seven-and-a-half-year Moon-sign cycle phases against natal data for the current day and returns intensity, upcoming cycles, and historical rows. SECTION: WHAT THIS TOOL COVERS Sade Sati mechanics: natal Moon sign in English, three reference signs (rising/peak/setting), active flag, current phase label, intensity score/label, next occurrence metadata, all_periods[] timeline with nested phase objects, mitigation booleans. "Today" is implicit — no date parameter. Signs are English, not Sanskrit. It is not general Gochar (asterwise_get_gochar) or dasha overlay (asterwise_get_dasha_transits). SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_natal_chart — confirm Moon sign context. AFTER: asterwise_get_gochar — broader transit canvas if needed. SECTION: INPUT CONTRACT No explicit query date — API pins to current day. BirthData global contract applies. SECTION: OUTPUT CONTRACT data.natal_moon_sign (string — English, e.g. 'Libra') data.natal_moon_sign_index (int — 0–11) data.sade_sati_signs: rising (string) peak (string) setting (string) data.is_currently_active (bool) data.current_phase (string — 'rising', 'peak', 'setting', or null) data.current_phase_description (string or null) data.intensity_score (int — 0–10) data.intensity_label (string — 'low', 'moderate', or 'high') data.next_sade_sati: starts (string — YYYY-MM-DD) phase (string) years_away (float) data.all_periods[] — each: sade_sati_number (int) overall_start (string — YYYY-MM-DD) overall_end (string — YYYY-MM-DD) duration_years (float) phases: rising — { name, description, start, end, saturn_sign, intensity } peak — same shape setting — same shape data.mitigated_by_own_sign (bool) data.mitigated_by_exaltation (bool) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — English sign names throughout — do not expect Sanskrit. SECTION: DO NOT CONFUSE WITH asterwise_get_gochar — nine-planet daily scan including sade_sati_active flag but less Sade Sati detail than this tool. asterwise_get_transits — ingress/station feed, not Moon-focused Saturn phase model.

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• secedgar_fetch_frames

  secedgar-mcp-server

  Fetch SEC XBRL frames for one concept × one period across all reporting companies. Inline response returns the top N ranked companies; the full frames response (all reporters) is materialized as df_<id> when a canvas is available, queryable via secedgar_dataframe_query. Accepts friendly names like "revenue" or "assets" (discover via secedgar_search_concepts) or raw XBRL tags. One call hits one XBRL tag — when a friendly name maps to multiple same-meaning tags, the response's `unqueried_tags` lists the others; call again per tag and UNION/COALESCE in SQL with an analysis-specific priority (e.g. SalesRevenueGoodsNet is goods-only). The response's `related_tags` separately flags alternate-DEFINITION tags a meaningful share of filers use as their primary line (e.g. cash incl. restricted cash, equity incl. noncontrolling interest) — a whole-universe screen on the base tag silently omits those filers; query them separately, but do not blindly union (the semantics differ). Response includes `value_distribution` and `period_end_range` to flag XBRL scale-factor anomalies and fiscal-year mixing.

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• get_guide

  Canvs

  ⚠️ MANDATORY FIRST STEP - Call this tool BEFORE using any other Canvs tools! Returns comprehensive instructions for creating whiteboards: tool selection strategy, iterative workflow, and examples. Following these instructions ensures correct diagrams.

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• eia_dataframe_describe

  eia-energy-mcp-server

  List canvas dataframes (df_<id>) materialized by eia_query_route, with provenance, TTL, row count, and column schema. Lazy-sweeps expired tables before responding so the list is always current. Pass a specific name to inspect one dataframe; omit to list all active dataframes for this tenant.

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• water_get_series

  usgs-water-mcp-server

  Get a time series of daily or instantaneous values for a USGS site and parameter over a date range. Returns siteNumber, parameterCd, and time-ordered value records. When the server has DataCanvas enabled, large result sets (>500 records) spill to a canvas — the response includes canvas_id and table_name for SQL analysis via water_dataframe_query. Without DataCanvas, returns the most recent 500 records with truncated=true. Use water_find_sites to discover valid site numbers. Use water_list_parameters for parameter codes.

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• met_search_collections

  met-museum-mcp-server

  Search the Metropolitan Museum of Art collection by keyword and optional filters; returns total match count and a page of object IDs. Always chain the returned IDs to met_get_object (up to 20 at a time) to retrieve full records. Search relevance is keyword-based, not semantic — use concise terms and apply departmentId or geoLocation filters to sharpen results. The medium parameter maps to the classification field (pass "Paintings", "Drawings", etc., not material descriptions like "Oil on canvas"). isPublicDomain guarantees CC0-licensed images; hasImages also includes copyrighted works. isOnView restricts results to works currently on display in a Met gallery.

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• read_canvas

  Agent Canvas Arena

  Global situational awareness. Returns the full 32x32 grid and reservoir stats. Warning: This is a heavy payload (1024 pixels). Use for broad scanning of opportunities.

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• fema_search_nfip

  fema-mcp-server

  Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.

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• openmeteo_get_ensemble

  open-meteo-mcp-server

  Probabilistic ensemble weather forecast — up to 51 ensemble members, up to 16 days ahead with optional past_days (0–92). Each member's values appear as separate columns named with a member suffix (e.g. temperature_2m_member01, temperature_2m_member02). Use the spread across members to compute exceedance probabilities, quantify forecast uncertainty, and build decision thresholds. Available models: "ecmwf_ifs025" (51 members, global, 0.25°), "gfs025" (31 members, global, 0.25°), "icon_seamless" (40 members, global/Europe blend), "gem_global" (21 members, global, 0.25°). Omit models to use the API default blend. Large multi-member, multi-day pulls produce thousands of records and spill to DataCanvas when canvas is enabled. At least one of hourly_variables or daily_variables is required.

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• openmeteo_get_historical

  open-meteo-mcp-server

  Historical weather from the ERA5 reanalysis archive (1940–present). Requires start_date and end_date (ISO 8601 date, e.g., "2024-07-01"). ERA5 has a variable lag of up to ~5 days — for dates within the last week, use openmeteo_get_forecast with past_days instead. Uses the same variable names as the forecast API for direct comparison. Large date ranges (multi-year hourly) produce thousands of records — these spill to DataCanvas for SQL querying when canvas is enabled. At least one of hourly_variables or daily_variables is required.

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