MCP Server Chart vs Weathersight

A side-by-side look at the MCP Server Chart and Weathersight MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

MCP Server ChartData Visualization · Local subprocess (stdio)A Model Context Protocol server for generating visual charts using AntV.71GreatAuth: no auth required

WeathersightEnvironment & NatureHistorical weather and climate: 100+ years of station data, normals, extremes, and trends.51GoodAuth: oauth 2.0

Verdict: choose MCP Server Chart for data visualization work as a local process; choose Weathersight for environment & nature. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

Choose MCP Server Chart when

You need dedicated capabilities in the Data Visualization domain.
You prefer a local stdio subprocess transport.
Its auth model fits you: No auth required.
You want tools like generate_area_chart, generate_bar_chart, generate_boxplot_chart.

Explore MCP Server Chart

Choose Weathersight when

You need dedicated capabilities in the Environment & Nature domain.
Its auth model fits you: OAuth 2.0.
You want tools like anom, anomalies, bestplace.

Explore Weathersight

Feature & specification comparison

Rows where the two servers differ are marked.

Spec	MCP Server Chart	Weathersight
Category	Data Visualization	Environment & Nature
Quality signal	71/100 · Great	51/100 · Good
Install command	Confirmed	Not confirmed yet
Transport	Local subprocess (stdio)	—
Auth	No auth required	OAuth 2.0
Pricing	Free	Freemium
Required env vars	None listed	None listed
Verified owner	Community listing	Community listing
GitHub stars	4,402	0
Directory activity	10 views 0 installs 0 upvotes	0 views 0 installs 0 upvotes
Tools	27	25
Full listing	View MCP Server Chart	View Weathersight

Tools & capabilities

MCP Server Chart tools 27

generate_area_chartGenerate a area chart to show data trends under continuous independent variables and observe the overall data trend, such as, displacement = velocity (average or instantaneous) × time: s = v × t. If the x-axis is time (t) and the y-axis is velocity (v) at each moment, an area chart allows you to observe the trend of velocity over time and infer the distance traveled by the area's size.
generate_bar_chartGenerate a horizontal bar chart to show data for numerical comparisons among different categories, such as, comparing categorical data and for horizontal comparisons.
generate_boxplot_chartGenerate a boxplot chart to show data for statistical summaries among different categories, such as, comparing the distribution of data points across categories.
generate_column_chartGenerate a column chart, which are best for comparing categorical data, such as, when values are close, column charts are preferable because our eyes are better at judging height than other visual elements like area or angles.
generate_district_mapGenerates regional distribution maps, which are usually used to show the administrative divisions and coverage of a dataset. It is not suitable for showing the distribution of specific locations, such as urban administrative divisions, GDP distribution maps of provinces and cities across the country, etc. This tool is limited to generating data maps within China.
generate_dual_axes_chartGenerate a dual axes chart which is a combination chart that integrates two different chart types, typically combining a bar chart with a line chart to display both the trend and comparison of data, such as, the trend of sales and profit over time.
generate_fishbone_diagramGenerate a fishbone diagram chart to uses a fish skeleton, like structure to display the causes or effects of a core problem, with the problem as the fish head and the causes/effects as the fish bones. It suits problems that can be split into multiple related factors.
generate_flow_diagramGenerate a flow diagram chart to show the steps and decision points of a process or system, such as, scenarios requiring linear process presentation.
generate_funnel_chartGenerate a funnel chart to visualize the progressive reduction of data as it passes through stages, such as, the conversion rates of users from visiting a website to completing a purchase.
generate_histogram_chartGenerate a histogram chart to show the frequency of data points within a certain range. It can observe data distribution, such as, normal and skewed distributions, and identify data concentration areas and extreme points.
generate_line_chartGenerate a line chart to show trends over time, such as, the ratio of Apple computer sales to Apple's profits changed from 2000 to 2016.
generate_liquid_chartGenerate a liquid chart to visualize a single value as a percentage, such as, the current occupancy rate of a reservoir or the completion percentage of a project.
+15 more on the listing page

Weathersight tools 25

anomReturns observations with 1991–2020 baseline comparisons at daily, weekly, or monthly granularity for a location and period. When to use: Graph or tabulate recent anomalies at a location for a specific period — e.g. 'how did last month compare to normal?' Date format: YYYYMMDD for ymd; use groupby (day/week/month) to control granularity. For 'last week' use YYYYMMDD of 7 days ago. For 'last month' use YYYYMM of prior month. Performance: Computed on demand; moderate speed. Prerequisites: Use /location to obtain a locid first. Investigate: Use after /anomalies or /breaking. Identifies a city of interest; provides per-day/week detail for drill-down. Combine groupby=DAY/FULL_DAY with a recent date range for 'recent days with departure from normal' queries. Augment: Provides the per-period anomaly series for prose ('temperatures were above normal for 12 of the past 14 days'). Returns: loc, ts, vals, N, vals_ts, baseline.
anomaliesReturns precomputed anomalous observations for locations in a given time period and scope. When to use: Survey anomalous weather at regional or global scale quickly without specifying individual cities. Date format: YYYY[MM[DD]]: YYYY for annual anomalies (scope=year), YYYYMM for monthly (scope=month), YYYYMMDD for weekly or daily (scope=week/day). Performance: Fast — retrieves precomputed data. Prerequisites: None; global or country-filtered. Use /countries for country IDs. Investigate: Good early call to identify which locations experienced anomalies before drilling into specifics with /anom or /dailycomp. Augment: Use for a first-pass global scan to identify story-worthy anomalies before fetching per-location detail. Returns: anomalies, anomaly.locid, anomaly.lat, anomaly.long, anomaly.obs_start, anomaly.obs_end, anomaly.obs_dur, anomaly.base_start, anomaly.base_end, anomaly.base_dur, anomaly.obs_period, anomaly.base_period, anomaly.outliers, outlier.obs_metric, outlier.percentile, outlier.obs_N, outlier.obs_X, outlier.base_N, outlier.base_X, outlier.obs_val, outlier.base_val, outlier.base_mean, outlier.base_stddev, outlier.min, outlier.max, outlier.max_key, outlier.min_key, outlier.obs_min_key, outlier.obs_max_key.
bestplaceGiven a single week, month, or day, ranks locations whose weather best matches a set of criteria (most optional criteria met first; all MUST criteria satisfied by every result). Filter by lat/lon+radius or countries. When to use: Answer 'where has the best weather for week/month/day X?' Date format: scope=day + time=YYYYMMDD (forecast); scope=week + time=MMDD; scope=month + time=MM. Defaults: week of today+14, month today+1, day today+1. Performance: Filter by area/countries to keep fast. Prerequisites: None; supply lat/lon+radius_km or a country list. Investigate: Rank places for a given time window. scope=day uses forecasts. Augment: Find the best destinations for a target week/month. Notes: Every criterion metric name is literal and fully qualified: the 'avg:' prefix, then the metric base (observation metrics keep their 'obs.' segment), then the attribute — e.g. avg:max_t.p50, avg:obs.rain.sum, avg:obs.is_rain.count. Dropping 'avg:' or 'obs.' is an error; the criteria parameter below lists every valid name. scope=day queries forecasts; week/month query the 1996-2025 historical baseline. Returns: scope, time, baseline, results, locid, location, criteria_met, criteria_met_count, record.
besttimeGiven a location, ranks the weeks or months whose historical climatology best matches a set of weather criteria (most optional criteria met first; all MUST criteria satisfied by every result). When to use: Answer 'when is the best time to visit X for weather like Y?' Choose when_type=week or month over the 1996-2025 baseline. Date format: when_type: week|month|day. Historical basis only for week/month. Performance: Sub-second per location. Prerequisites: Use /location to obtain a locid first. Investigate: Rank a location's calendar by weather suitability. For a specific upcoming day's forecast, use /api/anom with period_type=FUTURE_DAY. Augment: Find the typical best window for an activity at a place. Notes: Every criterion metric name is literal and fully qualified: the 'avg:' prefix, then the metric base (observation metrics keep their 'obs.' segment), then the attribute — e.g. avg:max_t.p50, avg:obs.rain.sum, avg:obs.is_rain.count. Dropping 'avg:' or 'obs.' is an error; the criteria parameter below lists every valid name. For daily forecasts use /api/anom (period_type=FUTURE_DAY). Weekly/monthly forecasts are planned. Returns: location, baseline, when_type, results, time, criteria_met, criteria_met_count, record.
breakingReturns anomalous weather conditions (breaking news) across the world or specified countries in the very recent past (observations) or immediate future (forecast). When to use: First call when looking for current or upcoming notable weather events globally or by country. Date format: No date params — always returns the most recent ~3 days of observations (breaking_type=obs) or next 7 days of forecast (breaking_type=forecast). Performance: Fast — retrieves precomputed anomaly snapshots. Prerequisites: Use /countries to get country IDs for filtering. Investigate: Start here for any 'what's happening right now?' or 'what's forecast this week?' query; follow up with /anom or /dailycomp for per-location detail. Augment: Primary source for identifying breaking weather news stories globally; use ctryid to narrow scope by country. Returns: anomalies, anomaly.locid, anomaly.name, anomaly.city, anomaly.country, anomaly.lat, anomaly.lon, anomaly.geohash, anomaly.elevation, anomaly.obs_start, anomaly.obs_end, anomaly.obs_dur, anomaly.pt, anomaly.anomalies, outlier.obsm, outlier.comps, comp.obsm_f, comp.oq, comp.bq, comp.scope, comp.val, comp.bval, comp.min, comp.max, comp.med, comp.mean, comp.std, comp.min_dt, comp.max_dt, comp.intensity, comp.intensity_f, comp.rp, comp.summary, comp.period, comp.base_N, comp.N, comp.ut, comp.start, comp.end.
climatecompsReturns monthly distributions of selected metrics for locations compared against a baseline period. When to use: Compute normalized and absolute deviations from a baseline, inter-annual and intra-annual variations within months and seasons. Check base_N and obs_N to assess data completeness. Date format: obs_years as YYYY or YYYY-YYYY range; months as MM or MM-MM (e.g. '06', '06-08', 'All' for annual). Performance: Bulk query — can be slow for global or large-area queries. Filter with locids, lat_lon/radius_km, or countries. Prerequisites: Use /location to obtain locids first for point queries. Investigate: Use to compare a recent season or year against the long-term baseline across many locations simultaneously. Combine with /climatetrends for trend context. Augment: Identifies which locations had the most anomalous season compared to their historical distribution. Notes: Metric format: 'max_t' = distribution of daily Maximum Temperature over the period; 'max_t.means' = distribution of annual means of max_t (1 value per year); 'rain_sum' (accumulations) and 'obs.is_thunderstorm' (counts) follow the same pattern. Returns: results, comp.locid, comp.geohash, comp.lat, comp.lon, comp.elev, comp.area, comp.country, comp.src_type, comp.metric, comp.obs_years, comp.base_years, comp.month, comp.obs_N, comp.base_N, comp.obs, comp.base, comp.obs.{q} / comp.base.{q}.
climatetrendsReturns monthly timeseries of selected climate trend metrics for one or more locations, suitable for long-term trend analysis. When to use: Retrieve long-term monthly timeseries for statistical trend analysis across any number of locations including globally. Use num_years and avg_N to filter sparse records. Date format: years as YYYY- range (e.g. '1975-' for 1975 to present); months as MM or MM-MM range (e.g. '06' for June, '06-08' for June–August, 'All' for annual). Performance: Bulk query — can be slow for global or many-location queries. Filter with locids, lat_lon/radius_km, or countries to improve speed. Prerequisites: Use /location to obtain locids first for point queries. Investigate: Use for global or regional trend analysis. Metric name format: 'agg_func:metric_id.attribute' (e.g. 'avg:max_t.p50'). Check num_years to confirm data completeness before citing a trend. Augment: Backs trend claims with long-term data across many locations (e.g. 'temperatures rising across Southeast Asia'). Notes: Example metrics: avg:max_t.p50, avg:obs.temp.mean, sum:obs.is_rain.count. See /api/metrics for full list. Returns: results, trend.locid, trend.geohash, trend.lat, trend.lon, trend.elev, trend.area, trend.country, trend.src_type, trend.metric, trend.years, trend.month, trend.num_years, trend.avg_N, trend.mvals, trend.mvals.xvals, trend.mvals.yvals_by_metric.
compareCompares a location's observed weather period against the corresponding periods in a historical baseline distribution. When to use: Quantify how extreme a specific period was across all metrics at once — e.g. how anomalous was March 2025 at New York vs the 1991–2020 baseline. Date format: YYYY[MM[DD]]: YYYYMMDD for a specific day or short window (period_type DAY/WEEK), YYYYMM for a month (period_type MONTH), YYYY for an annual comparison (period_type YEAR). Performance: Computed on demand; fast for short periods. Prerequisites: Use /location to obtain a locid first. Investigate: Best for 'how unusual was [period] at [location]?' — returns percentile and z-score across all metrics simultaneously. Set baseline_offset=1 for the standard 1991–2020 baseline. Augment: Provides the precise statistical framing ('the hottest March in 30 years') before describing the anomaly in prose. Returns: comparisons, comparison.locid, comparison.lat, comparison.long, comparison.obs_start, comparison.obs_end, comparison.obs_dur, comparison.base_start, comparison.base_end, comparison.base_dur, comparison.obs_period, comparison.base_period, comparison.outliers, outlier.obs_metric, outlier.percentile, outlier.obs_N, outlier.obs_X, outlier.base_N, outlier.base_X, outlier.obs_val, outlier.base_val, outlier.base_mean, outlier.base_stddev, outlier.min, outlier.max, outlier.max_key, outlier.min_key, outlier.obs_min_key, outlier.obs_max_key.
countriesReturns countries with their canonical country IDs used in all other API calls. When to use: Resolve country names to canonical IDs, or obtain the full list of supported countries. This list rarely changes. Performance: Fast — static data; safe to cache across calls. Prerequisites: None. Investigate: Call once if country-level filtering is needed; cache the result for the session. Augment: Fetch country IDs before making country-filtered calls to /anomalies or /breaking. Returns: countries, country.ctryid, country.name, country.regions.
dailycompReturns per-day observed values with percentiles and z-scores vs a sliding N-day window over a historical baseline. When to use: Graph heatwaves, cold spells, and other streaks by showing daily departures from normal. Also compare two separate periods or locations using z-scores or percentiles for normalized severity. Date format: YYYYMMDD for ymd_start and ymd_end. Keep ranges ≤30 days for fast response; up to 90 days is feasible. Performance: Computed on demand; slower for long date ranges. Prerequisites: Use /location to obtain a locid first. Investigate: Best for detailed day-by-day anomaly investigation once approximate dates and location are known. Use separate calls for two periods to compare heatwave severity via z-score. Do NOT use for ranking top-N events across years — call /events with criteria for that and post-process the response. Augment: Provides the daily series needed to describe the arc of a heatwave or cold snap in a news narrative. Returns: locid, lat, lon, dates, num_years, avg_coverage, metrics, metrics.{key}.values, metrics.{key}.pcts, metrics.{key}.sigmas, metrics.{key}.p1 … p99, metrics.{key}.bmin / bmax, metrics.{key}.mean, metrics.{key}.stddev, metrics.{key}.coverage.
dailycountbylocHow often a compound daily weather condition happens, per location: for every location in an area, the average number of days per year that met all the criteria, with the count, the days that had data, and the percentage. When to use: Map where a condition is common or rare, e.g. 'where are summer days between 20 and 30 C most frequent' or 'where did heatwave nights occur in the last five summers'. Date format: Either ymd_start and ymd_end (YYYYMMDD, at most 31 days) or year_start, year_end with optional mm_dd_start and mm_dd_end for a season across years (at most 5 years). Defaults to the last 30 days. Performance: Full counts need a geo scope (ctryid, latlon, or a bbox of at most continent size) or a date range of at most 30 days. Anything wider is sampled automatically to about 200 locations (see sample), because an open-ended multi-year count takes over a minute; pass sample=0 to be refused instead. With a geo scope, year windows up to 5 years run fast. Prerequisites: None; without a geo scope, windows over 30 days are sampled. Investigate: Use min_count_pct to keep only locations where the condition is frequent, or min_count 0 with max_count to find where it is rare or never happens. Augment: Support statements about how common a condition is across a region with avg_count and count_pct. Notes: avg_count is matched days divided by years in the window; count_pct divides by the days that actually had data, so station gaps do not read as absence. Returns: criteria_desc, time, total_days_per_year, num_years, total_days_found, count, truncated, results, locid, lat, lon, city_name, country, avg_count, days_with_data, count_pct, sample, note, error.
dailycountbyyearFor one location, the number of days per year that met all the criteria within a month-day window, for every year with data, plus a trend line (days per decade) with R squared and p-value. When to use: Answer 'is this condition becoming more frequent here?', e.g. hot summer days or frost nights per year over decades. Date format: The window is mm_dd_start and mm_dd_end (MMDD), or the month-days of ymd_start and ymd_end; years are not a filter, every year with data is returned. A window crossing the year end (1201 to 0228) belongs to the year it starts in. Defaults to the month-days of the last 30 days. Performance: Fast: filtered to one location in the index. Prerequisites: locid from /api/location (ICAO and WMO ids also resolve). Investigate: Compare the trend across nearby locations, or across absolute and percentile criteria for the same condition. Augment: Back 'more frequent than it used to be' claims with slope_per_decade and its p_value. Notes: Years with under 80% of the window covered are listed (in_trend false) but excluded from the trend. Returns: locid, location, criteria_desc, mm_dd_start, mm_dd_end, total_days_per_year, years, counts, days_with_data, in_trend, trend, note, error.
+13 more on the listing page

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.

MCP Server Chart

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "antv-mcp-server-chart": {
      "command": "npx",
      "args": [
        "-y",
        "@antv/mcp-server-chart"
      ]
    }
  }
}

Weathersight

No confirmed install command for Weathersight yet — follow the setup steps linked from its listing page rather than a guessed package name.

Frequently asked questions

MCP Server Chart is categorized under Data Visualization and uses a local stdio subprocess. Weathersight belongs to Environment & Nature and uses an install path we have not confirmed yet. Pick MCP Server Chart for data visualization work and Weathersight for environment & nature.

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Weathersight has no confirmed install command yet — follow its own setup instructions.

MCP Server Chart is listed as free. Auth: no auth required. Weathersight is listed as freemium — a free tier with paid plans. Auth: oauth 2.0. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Yes. MCP clients support multiple servers: add antv-mcp-server-chart and weathersight as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, MCP Server Chart has a quality signal of 71/100 (Great) with 10 views, 0 installs and 4,402 GitHub stars. Weathersight has 51/100 (Good) with 0 views, 0 installs and 0 GitHub stars.

Choose MCP Server Chart when

Choose Weathersight when

MCP Server Chart tools 27

Weathersight tools 25

MCP Server Chart

Weathersight

Frequently asked questions

What is the main functional difference between MCP Server Chart and Weathersight?

How do I install MCP Server Chart or Weathersight in Claude Desktop, Cursor, or Windsurf?

Are MCP Server Chart and Weathersight free to use, or do they require API keys?

Can I run both MCP Server Chart and Weathersight at the same time in my AI client?

Which MCP server has higher directory engagement and quality ratings?

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