Markmap MCP Server vs Tableau Graphql MCP
A side-by-side look at the Markmap MCP Server 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 Markmap MCP Server 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 Markmap MCP Server 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
markdown_to_mindmap, list_mindmaps, get_mindmap.
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 | 68/100 · 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 | 289 | 1 |
| Directory activity | 5 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 4 | 7 |
| Full listing | View Markmap MCP Server | View Tableau Graphql MCP |
Tools & capabilities
Markmap MCP Server tools 4
markdown_to_mindmapConvert structured Markdown (headings # and nested lists -) into an interactive mind map HTML file, with optional server-side PNG/JPG/SVG export. Use when the user wants a visual mind map/outline of structured content (architecture, plans, notes, hierarchies), or asks to visualize / mindmap / diagram. Do not use for flat unstructured text (restructure first), or to list/read existing outputs (use list_mindmaps / get_mindmap). Behavior: - WRITES files under the configured output dir. HTML is always written; image formats write an extra file. Reusing filename overwrites. - html is fast (<1s). png/svg/jpg launch headless Chromium via Playwright (5–15s; requires: npm install playwright && npx playwright install chromium). - Local only — no external APIs or third-party keys. Open mode=never: The server never opens the browser. - Offline=false (server config). Response: - JSON {"htmlFilePath":"<path>","filePath":"<path>"} — htmlFilePath is always the HTML source; filePath is the primary artifact (HTML or image). - Return mode=path: Paths JSON only — no inline content. - Errors: isError:true with {"error","message"}.list_mindmapsList markmap* files (html/png/jpg/jpeg/svg) in the output directory, newest first. Read-only — no side effects. Missing/empty dirs return {"files":[]}. Returns: {"outputDir","files":[{"name","filePath","size","mtimeMs","mtime"}]} (mtime is ISO 8601). Use before get_mindmap to discover paths, or before cleanup_mindmaps to preview targets. Not for generating new mind maps (use markdown_to_mindmap).get_mindmapRetrieve a generated mind map file by absolute path. Read-only — no side effects. Only paths inside the configured output directory are allowed (path traversal denied). Returns JSON {"filePath","mimeType","size"}. For text/html or image/svg+xml under 200KB, also appends a text content block. PNG/JPG return metadata only (no image block) — re-export via markdown_to_mindmap with format=png|jpg if the agent needs pixels. On error: isError:true with {"error":"Failed to retrieve mind map","message"}. Use list_mindmaps first to obtain a valid filePath. Prefer this over regenerating when the file already exists.cleanup_mindmapsPermanently delete markmap* files from the output directory. DESTRUCTIVE and irreversible when dryRun is false. dryRun defaults to false — omitting it WILL delete. Always call once with dryRun=true to preview, then again with dryRun=false to commit. Prefer list_mindmaps beforehand. Returns {"deleted":["<path>",...],"kept":<n>}; dryRun responses also include dryRun:true and do not delete. Use instead of manual file deletion when pruning old generated mind maps.
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.
Markmap MCP Server
{
"mcpServers": {
"jinzcdev-markmap-mcp-server": {
"command": "npx",
"args": [
"-y",
"@jinzcdev/markmap-mcp-server"
]
}
}
}
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
Markmap MCP Server 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 Markmap MCP Server 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.
Markmap MCP Server 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 jinzcdev-markmap-mcp-server and tableau-graphql-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Markmap MCP Server has a quality signal of 68/100 (Great) with 5 views, 0 installs and 289 GitHub stars. Tableau Graphql MCP has 57/100 (Good) with 2 views, 0 installs and 1 GitHub stars.