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269,132 tools. Last updated 2026-07-07 13:28

"iPhone 16e camera review summary and table creation in Italian" matching MCP tools:

  • Parse a Primavera P6 XER file and return a TABLE SUMMARY (not the full row-level data — XER row dumps explode the MCP context window). For each table in the XER, returns the table name, field list, and record count. Per-row data is intentionally omitted — for forensic / DCMA / windows analysis use the dedicated tools (``forensic_windows_analysis``, ``critical_path_validator``, etc.) which consume the parsed XER internally and return analytical summaries, not raw rows. Use this tool to confirm an XER is parseable, list its tables, see the data date / project name from PROJECT, or count activities in TASK before deciding which deeper tool to run. Args: xer_path: server-side filesystem path to the XER file. xer_content: full text of the XER file (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. Returns: { "filepath": absolute path, "encoding_used": "utf-8" | "cp1252" | ..., "ermhdr": file header dict (P6 version, export user, etc.), "tables": [{"name", "fields", "record_count"}, ...], "table_count": int, "total_records": int, "project_summary": { "proj_id", "proj_short_name", "proj_long_name", "data_date", "plan_end_date" } (from first PROJECT row, if any) }
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  • Recover detail from camera-shake and accidental motion blur. NAFNet (ECCV 2022, SOTA on GoPro/SIDD benchmarks). Best for: handheld shake, bumped camera, whole-frame uniform blur. NOT effective for: intentional panning blur, bokeh/depth-of-field, or artistic motion effects. Also supports denoising (grainy/noisy photos). 20 sats per image (~2 min processing), pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='deblur_image'.
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  • Use to discover which SEC filings exist for a ticker before searching content. For the actual content use sec_report_search instead. List indexed SEC filings for a given ticker with a summary header. Returns: summary (period coverage, per-type counts) + table of up to 50 filings (fiscal_year, fiscal_quarter, filing_type, filing_date, period_start, period_end). filing_types filter: omit for main reports only (10-K, 10-Q, 20-F, S-1, DEF 14A and /A amendments; excludes 8-K/6-K); pass [] for all indexed types; pass explicit allowlist to override.
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  • Returns WSDOT highway camera locations, descriptions, and image URLs. Camera images are copyright WSDOT — only metadata and image URLs are returned, not image bytes. Filter by state route (e.g. "090" for I-90), WSDOT region, or milepost range. Omit all filters to list all cameras statewide (potentially hundreds).
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  • Search a city to explore free walking tours and paid activities on GuruWalk, the world's largest free walking tour platform. Returns destination info, tour categories (free tours, food tours, day trips, tickets, and more), and featured listings with ratings and verified traveler reviews. Covers 200+ cities worldwide. Free tours operate on a pay-what-you-want model. Supports English, Spanish, German, and Italian. Use this tool when you know the traveler's destination and the conversation has reached the point of recommending experiences. Do NOT call it just because a destination is mentioned — first understand what the traveler is looking for. If the traveler mentions a landmark instead of a city, infer the city (e.g. 'eiffel tower' → Paris, 'colosseum' → Rome, 'sagrada familia' → Barcelona, 'big ben' → London). After getting results, review the categories and featured_products to find the most relevant matches for what the traveler asked about.
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  • Retrieve WHOIS registration data: registrar, creation/expiry dates, nameservers, status. Use to verify domain ownership, age, expiration; for full audit use domain_report. Free: 30/hr, Pro: 500/hr. Returns {domain, whois: {registrar, creation_date, expiry_date, updated_date, name_servers, status, raw_length, error}, summary}.
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  • performance-review MCP — wraps StupidAPIs (requires X-API-Key)

  • Capture photos remotely from mobile devices via S3-backed upload URLs

  • Fetch dimension definitions and valid coded values for a MakStat PxWeb table. Path must end in the '.px' table id (e.g. 'Naselenie/VencaniRazvedeni/280_VitStat_Brak_voz_ml.px'). Returns dimensions with their codes and value lists — use these to build the selection body for query_table.
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  • Fetch dimension definitions and valid coded values for a Moldova Statbank PxWeb table. Path must end in the '.px' table id (e.g. '20 Populatia si procesele demografice/POP010/POPro/POP010100rcl.px'). Returns dimensions with codes and value lists — use these to build the selection body for query_table.
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  • Search TaxCompass's primary-source corpus and return passages to cite. Hybrid semantic + keyword retrieval over Italian tax & company-law primary sources: Normattiva (statute), Agenzia delle Entrate (circolari & guidance), INPS (social security), pinned tax-year tables (IRPEF brackets, INPS rates, forfettario thresholds & coefficienti di redditività), the ATECO 2025 code catalogue, and EU/treaty sources. Each result carries a `chunk_id`, `source`, and (usually) a `url`. Cite the `url` and quote the `text`; do not assert Italian tax facts the passages don't support. Queries work in any language, but Italian keywords improve recall against the (Italian) legal corpus. Args: query: What to search for. Keyword-dense Italian phrasing works best. sources: Optional subset to restrict to (see `list_tax_sources` for keys). Omit to search everything. Unknown keys are ignored. k: Max passages to return (1–12).
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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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  • Get aggregate dining statistics for a neighborhood, city, or region: cuisine breakdown, grade distribution, price range, top neighborhoods, and highlighted restaurants. Use this for area-level context ("what is the dining scene like in Shoreditch?"), NOT for finding a specific restaurant or getting a personal recommendation. For "find me a quiet Italian near Shoreditch", use the recommend tool instead.
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  • Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (SELECT, INSERT, UPDATE, DELETE, CREATE, etc.) * AI/ML functions like AI.FORECAST, ML.EVALUATE, ML.PREDICT * Any other query that bigquery supports. Example Queries: -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; Queries executed using the `execute_sql` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `projectId` field.
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  • List saved viewpoints / camera positions and top-level view containers for a translated Navisworks model. Pulls the metadata view list and enriches each 3D view with its first two levels of the object tree (viewpoint folders typically live there in NWD files). When to use: when preparing a coordination meeting and you need a quick index of every saved viewpoint (e.g. "Level 3 Mech Room", "Clash - duct vs beam gridline C-4") to drive screenshots or BCF-style issues; when an agent needs to deep-link a 2D sheet or 3D camera into the APS Viewer. When NOT to use: does not return camera matrices (position/target/up vectors) — APS Model Derivative does not expose those from the NWD viewpoint XML; for full camera data the source NWD must be opened in Navisworks Manage. APS scopes required: viewables:read data:read. Rate limits: APS default ~50 req/min; this tool fans out one object-tree call per 3D view (capped implicitly by metadata view count, usually <5). For federated models with many sheets this can approach the per-minute quota — cache the result. Errors: 401 token (retry); 403 scope (report); 404 URN not found / translation incomplete; 409 N/A; 422 model returned empty metadata (returns viewpoint_count:0 rather than throwing — agent should verify translation via nwd_export_report); 429 rate limit (backoff); 5xx APS upstream (retry once). Per-view object-tree failures are swallowed so the overall call still returns the metadata-level view list. Side effects: none. Pure read. Idempotent. Logs usage to D1 usage_log. Results are capped at 100 viewpoint entries.
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  • List rows in a workspace's table surface. Returns rows with their data (a JSON object of column-name to value), creation time, the principal who created/updated each row, AND the row's `surface_slug` (the sheet it lives on). Empty array if no rows have been added yet. Multi-surface workspaces: pass `surface_slug` to scope to one sheet; omit to return rows from every surface in the workspace (back-compat: pre-multi-surface clients keep working).
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  • MANDATORY for any monthly review or summary request ('how did we do', 'monthly review', 'show me May', 'made/spent/saved'). ALWAYS call this tool — NEVER answer monthly-review questions from chat memory or earlier tool results: the family dashboard UI renders ONLY when this tool is actually invoked, and saved data may have changed since the last call. Returns curated Made/Spent/Saved, prior-month comparison, category/budget drivers, bursts, and transactions. Say mapped so far unless coverage is complete.
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  • Convert a JSON array of objects into a Markdown table. Automatically detects columns, aligns headers, and fills missing keys with empty cells. Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document.
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  • USE THIS to verify an Italian Codice Fiscale (personal tax code) before relying on it — do not guess the final check letter. Checks the 16-character format and the mod-26 check character. Validates structure only; does NOT confirm the code is registered with the Agenzia delle Entrate.
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  • Create a new shortText and get a shareable shorttext.com URL back. Markdown is supported. Requires an API key in the request Authorization header (`Bearer st_live_…`, request one from api@shorttext.com) — without it, creation is rejected (reading stays open to everyone).
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  • Monthly performance summary for all analysts (last 6 months) — Returns win_rate, avg_return, and total_signals per analyst per calendar month for exactly the last 6 calendar months (current month + 5 prior full months, enforced with DATE_TRUNC('month') boundaries — never more than 6 month buckets). All 10 canonical analysts (chain_hawk, whale_watch, alpha_scout, defi_pulse, quant_edge, rate_hawk, flow_tracer, unlock_guard, sentiment_edge, narrative_pulse) are always present in the response with an empty array [] when they have no signals in the window. Data is computed directly from the signal_history PostgreSQL table — no separate snapshot table required. winRate is a fraction (0–1, e.g. 0.71 = 71%) and is null when fewer than 5 resolved signals exist for that month. avgReturn is in percentage points (e.g. 12.3 = +12.3% average return) and is null when no resolved+priced signals exist for that month. Useful for AI agents answering 'How did WhaleWatch perform in May?' or 'Who was the best analyst last month?'
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