MCP Server Chart vs Tableau Graphql MCP
A side-by-side look at the MCP Server Chart and Tableau Graphql MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose MCP Server Chart for data visualization work as a local process; choose Tableau Graphql MCP for data visualization as a local process. 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.
Choose Tableau Graphql MCP when
- You need dedicated capabilities in the Data Visualization domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
TABLEAU_SERVER, TABLEAU_SITE_CONTENT_URL, TABLEAU_PAT_NAME, TABLEAU_PAT_SECRET, TABLEAU_AUTH_TOKEN, TABLEAU_COOKIE, TABLEAU_TIMEOUT, TABLEAU_API_VERSION. - You want tools like
graphql_query, introspect_schema, lineage_examples.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Visualization | Data Visualization |
| Quality signal | 71/100 · Great | 57/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | TABLEAU_SERVERTABLEAU_SITE_CONTENT_URLTABLEAU_PAT_NAMETABLEAU_PAT_SECRETTABLEAU_AUTH_TOKENTABLEAU_COOKIETABLEAU_TIMEOUTTABLEAU_API_VERSION |
| Verified owner | Community listing | Community listing |
| GitHub stars | 4,402 | 1 |
| Directory activity | 10 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 27 | 7 |
| Full listing | View MCP Server Chart | View Tableau Graphql MCP |
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
Tableau Graphql MCP tools 7
graphql_queryRun ANY read-only GraphQL query against the Tableau Metadata API. This is the general-purpose tool; use it for any lineage question. Returns {"data": ..., "errors": ...}. The Metadata API is a GraphQL graph of Tableau content (workbooks, sheets, dashboards, datasources, fields) and, with the Data Management add-on, physical assets (databases, tables, columns) and their upstream/downstream lineage. How to write a correct query: - Filters are EXACT and case-sensitive: `filter: {name: "X"}` or `filter: {nameWithin: ["X","Y"]}` (nameWithin is the only multi/OR match; there is NO substring or regex). - Every list field has a `<name>Connection` variant with `first`/`offset`/`after` + `pageInfo` (page size max 1000). Keep one query under ~20,000 nodes; narrow filters and page. - Fields is an interface; branch with `__typename` and inline fragments (`... on ColumnField { columns { name } }`, `... on CalculatedField { formula }`). - Reach a field's owning workbook via `datasource { ... on EmbeddedDatasource { workbook { name } } }`. - `downstream*` fields (downstreamWorkbooks/Owners, external tables/columns) need Data Management (Tableau Catalog) and are often empty otherwise; then resolve via core lineage (referencedByFields -> sheets -> workbook), or just call the `where_used` tool. Entry points include: workbooks, sheets, dashboards, publishedDatasources, embeddedDatasources, fields, columnFields, calculatedFields, columns, databaseTables, customSQLTables, databases, flows, tableauUsers, dataQualityWarnings. Call `lineage_examples` for ready-made query templates and a schema cheat-sheet, or `introspect_schema` to inspect any type's exact fields. Read-only: mutation and subscription operations are rejected. If a query exceeds the ~20,000-node limit, the response is flagged with `partial_results: true` and a `warning` (it does NOT auto-page an arbitrary query) so you never mistake a truncated result for a complete one.introspect_schemaIntrospect the live Metadata API GraphQL schema (introspection is enabled). With no argument: returns every Query entry point (with its args) and the full list of type names. With `type_name` (e.g. "Column", "Workbook", "DatabaseTable", "CalculatedField"): returns that type's fields, their result types, and args, so you can write a correct query against exactly what this server exposes.lineage_examplesReturn a schema cheat-sheet and a library of curated lineage questions with their correct GraphQL queries (+ example variables). Read this before composing a `graphql_query`. Categories: impact, provenance, calc, datasource, search, inventory, governance, ownership. Pass one to filter; omit to get them all. Each example has: question, graphql, variables, notes.where_usedFind which workbooks (and published datasources) USE the given names, the common 'where is this used / impact analysis' question, resolved robustly. `names` are EXACT, case-sensitive names of any of: a Snowflake/DB column, a Tableau field or alias, or a database table. Pass several to check them in one call. Results group by workbook, showing how each matched (column / field / whole table, with schema and source table) and which worksheets use it. This uses CORE lineage (referencedByFields -> field.sheets -> workbook, and field.datasource -> workbook), so it works even without the Data Management add-on where `downstreamWorkbooks` is empty. For other shapes of question, use `graphql_query`.impact_analysisFull transitive (MULTI-HOP) downstream impact of a column, field, or table. Returns every field that directly OR indirectly depends on it, and all affected sheets, dashboards, workbooks, plus the de-duplicated set of OWNERS to notify before a change. This is the "what breaks if I change/drop this?" tool. Unlike `where_used` (one core-lineage hop), it follows the whole dependency chain — a calc built on a calc built on the column is included. `name` is exact and case-sensitive. Workbooks are derived from downstream sheets/dashboards (the flat downstreamWorkbooks edge is unreliable). If it returns nothing on a site without Data Management, the transitive lineage may not be indexed there — use `where_used` for the direct references.search_contentFind content whose NAME contains `term` (case-insensitive SUBSTRING). Use this when you only know part of a name, since every other tool and the Metadata API filter are exact-match. Searches workbooks, published datasources, and database tables by default. Pass `types` to choose from: "workbook", "datasource", "table", "field", "column". Returns matches grouped by type. It pages through content client-side, so on a very large site it scans the first ~1200 of each type and says so in `note`; once you know the exact name, prefer `graphql_query` or `where_used`.server_infoReport the connected Tableau environment: server URL, site, product & REST API version, which Metadata API endpoint is in use, the auth method, and whether external-asset (Data Management / Catalog) lineage appears available. Good first call to confirm the connection and understand what lineage depth to expect.
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": {
"antv-mcp-server-chart": {
"command": "npx",
"args": [
"-y",
"@antv/mcp-server-chart"
]
}
}
}
Tableau Graphql MCP
{
"mcpServers": {
"tableau-graphql-mcp": {
"command": "uvx",
"args": [
"tableau-graphql-mcp"
],
"env": {
"TABLEAU_SERVER": "YOUR_TABLEAU_SERVER_HERE",
"TABLEAU_SITE_CONTENT_URL": "YOUR_TABLEAU_SITE_CONTENT_URL_HERE",
"TABLEAU_PAT_NAME": "YOUR_TABLEAU_PAT_NAME_HERE",
"TABLEAU_PAT_SECRET": "YOUR_TABLEAU_PAT_SECRET_HERE",
"TABLEAU_AUTH_TOKEN": "YOUR_TABLEAU_AUTH_TOKEN_HERE",
"TABLEAU_COOKIE": "YOUR_TABLEAU_COOKIE_HERE",
"TABLEAU_TIMEOUT": "YOUR_TABLEAU_TIMEOUT_HERE",
"TABLEAU_API_VERSION": "YOUR_TABLEAU_API_VERSION_HERE"
}
}
}
}
Frequently asked questions
MCP Server Chart is categorized under Data Visualization and uses a local stdio subprocess. Tableau Graphql MCP belongs to Data Visualization and uses a local stdio subprocess. Pick MCP Server Chart for data visualization work and Tableau Graphql MCP for data visualization.
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.
MCP Server Chart is listed as free. Auth: no auth required. Tableau Graphql MCP has no declared pricing — check the project's own site. 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 tableau-graphql-mcp 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. Tableau Graphql MCP has 57/100 (Good) with 2 views, 0 installs and 1 GitHub stars.