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262,013 tools. Last updated 2026-07-05 16:22

"How to fetch assignments from Canvas" matching MCP tools:

  • 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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  • Fetch observations from an ABS dataflow. dataKey is a dot-separated SDMX filter with one position per dimension (order from dataflow_structure); each position is a code, "+"-joined codes, or empty for wildcard. Pass "all" to fetch everything (can be large). Returns decoded series with their dimension labels and time-indexed values. Fetch dataflow_structure first to learn the dimension order and valid codes.
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  • Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.
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  • 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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  • 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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  • Fetch web pages and extract exactly the content you need. Select elements with CSS and retrieve co…

  • MCP server (stdio): fetch web pages as clean readable markdown via the AgentForge API

  • 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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  • Project reference / help desk about Fractera. Use this to answer ANY user question about what Fractera is, how it works, its architecture, components, modes, data ownership, pricing, use cases, partner program, etc. — especially while a deploy is running and the user wants to learn more. TOKEN-ECONOMY: call with NO arguments first to get the lightweight list of section ids+titles, then call again with a single `section` id to fetch just that section. NEVER try to fetch everything at once; pull only the section(s) relevant to the user question. Set `lang:"ru"` for Russian-speaking users.
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  • 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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  • Update elements on a canvas. Requires room_id from a previous Canvs tool result. Pass elements array with id and fields to update. IMPORTANT: in each update object ONLY `id` is required; all other fields are optional patch fields. Include ONLY elements that need changes; elements omitted from the request remain unchanged on the board. Prefer this tool for SMALL edits to existing diagrams (rename/move/restyle a few elements), typically after query_elements.
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  • Query elements on a canvas. Requires room_id from a previous Canvs tool result. Returns elements matching optional filters. Use this before update_elements when making small edits to existing diagrams. If no browser has the canvas open, returns an error — ask the user to open the canvas URL in their browser and retry.
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  • Preferred method for creating diagram elements from Mermaid. ⚠️ IMPORTANT: Call get_guide first and follow its instructions! Use this tool for NEW diagrams and LARGE changes to existing diagrams whenever the request can be represented in Mermaid. Prefer translating the request into Mermaid instead of manually recreating it with add_elements. If room_id is NOT provided - creates a NEW canvas and returns url plus room_id. If the user did not explicitly mention an existing board/canvas/room, do NOT ask for a room_id; create a new canvas instead. If a previous Canvs tool result or assistant message in the same conversation contains a room_id, reuse it for follow-up requests like 'add to it' or 'same board'. If you only have a room URL, extract room_id from https://[host]/?room=[room_id] or https://[host]/gdrive?id=[room_id]. If the user refers to a previous board but no usable room_id is available, create a new canvas instead of asking for the URL by default. If room_id IS provided - adds diagram elements to that canvas. If the canvas is displayed as an inline widget in the interface, do NOT include the url in your reply. If no widget is shown, share the url so the user can open the canvas.Supports: flowchart, graph, flowchart-elk, sequenceDiagram, classDiagram, classDiagram-v2, stateDiagram, stateDiagram-v2, erDiagram, journey, gantt, pie, gitGraph, mindmap, timeline, C4Context, C4Container, C4Component, C4Dynamic, C4Deployment, sankey, sankey-beta, quadrantChart, xychart, xychart-beta, requirement, requirementDiagram, kanban, architecture, block, block-beta, packet, packet-beta, radar-beta, treemap, info. Example: "flowchart TD\n A[Start] --> B{Decision}\n B -->|Yes| C[OK]\n B -->|No| D[Cancel]"
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • Fetch a file from a public URL and attach it to one of your personal notes (personal notes only; for team or shared notes use files-create_upload_url). Follows one redirect. Required: note_id (integer), url (string). Optional: filename (default: derived from URL), content_type (default: from HTTP response), description.
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  • Fetch a sample robots.txt from httpbin.org (/robots.txt). Use to test robots.txt parsing or as a content-type placeholder.
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  • Fetch a single occurrence record by its GBIF occurrence key. Returns the complete Darwin Core record — all coordinates, administrative geography (GADM), dates, collections metadata, collector identifiers, media links, and quality issue flags. Use the occurrence key from gbif_search_occurrences results to fetch full detail.
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  • 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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  • 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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  • List tables and column schemas on a DataCanvas staged by fema_search_nfip. Call this before fema_dataframe_query to discover the exact table name, column names, and DuckDB data types needed to write valid SQL. Row count reflects what was actually staged — check truncated in the fema_search_nfip response to know whether the canvas holds the full matching set.
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  • List the canvas tables (faostat_xxxxxxxx) staged by faostat_query_observations and faostat_commodity_profile, each with its source tool, the query parameters that produced it, creation/expiry timestamps, row count, and column schema. Call this before faostat_dataframe_query to discover the exact table and column names to reference in SQL.
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