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Tableau Knowledge MCP

List sources

list_sources
Read-only

Retrieve published data sources and workbooks connected to a Tableau knowledge graph, optionally filtered by node type, to see what the graph was built from.

Instructions

List the published data sources and workbooks connected to the knowledge graph, i.e. what the graph has been built from. node_type "PDS" or "WORKBOOK" narrows it; omit for both.

Input Schema

NameRequiredDescriptionDefault
graph_idNo
node_typeNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile needs no restating. The description adds useful framing that the results are the provenance of the graph, but says nothing about ordering, volume, or what an empty graph returns. With annotations carrying the risk profile, this is adequate but thin.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste, and the core purpose is front-loaded with the filtering detail after it. The backticked literal values make the parameter guidance scannable at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, annotations cover safety, and an output schema exists so return values need no explanation. The one real gap is graph_id — an agent cannot tell from the description what happens if it is omitted or whether it is required in practice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry the burden, and it does explain node_type's accepted values ("PDS" or "WORKBOOK") and the omitted-means-both default — information the schema lacks since no enum is declared. However, graph_id is never mentioned, leaving half the parameters undocumented in both places.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ("List the published data sources and workbooks") and adds a scoping gloss ("what the graph has been built from") that tells an agent this is inventory, not traversal. It does not name any sibling such as lineage or search_nodes, so the differentiation from the closest alternatives is left implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The only guidance is parameter-level: use node_type to narrow, omit for both. There is no statement of when to reach for list_sources versus lineage, search_nodes, or graph_health, even though those siblings sit adjacent to the same graph-inventory concept. Usage is implied by the phrasing rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.