tableau-graphql-mcp

by tdries

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct role: general-purpose GraphQL querying, schema introspection, example retrieval, direct lineage lookup, transitive impact analysis, fuzzy name search, and environment info. The overlap between where_used and impact_analysis is explicitly explained, eliminating ambiguity.
Naming Consistency3/5
Tool names are all lowercase snake_case but mix verb-first (introspect_schema, search_content), noun-first (graphql_query, lineage_examples, impact_analysis, server_info), and the unconventional where_used. There is no consistent verb_noun pattern, making the naming conventions mixed but still readable.
Tool Count5/5
Seven tools is a well-scoped number for a Tableau Metadata API server. It includes a general-purpose query tool plus specialized helpers that earn their place, neither sparse nor overwhelming.
Completeness5/5
The tool set comprehensively covers the domain: raw GraphQL access, schema inspection, example templates, direct and transitive lineage queries, fuzzy search, and environment diagnostics. graphql_query fills any niche gaps, leaving no obvious dead ends.
Average 4.8/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 14 commits in the last 12 weeks
- Last stable release on July 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It reveals the tool reports environment information and hints at read-only nature through the verb 'report,' but it does not explicitly state that it makes no changes, has no side effects, or specify any other behavioral characteristics. The description adds useful context about lineage availability but lacks definitive safety statements.
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?
The description is two sentences, front-loaded with the purpose ('Report the connected Tableau environment') followed by the specific data points and a clear usage recommendation. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with an output schema (even if not shown), the description is complete. It specifies what information is returned, the intended first-call use case, and what the user can infer about lineage depth. No additional context is needed for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty (0 parameters), giving a baseline of 4 per the rubric. The description does not mention parameters because there are none, which is appropriate. No additional parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports the connected Tableau environment, listing specific details (server URL, site, versions, auth method, lineage availability). This specific verb+resource clearly distinguishes it from sibling tools like graphql_query or where_used, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly recommends this as a 'good first call to confirm the connection and understand what lineage depth to expect,' which provides clear context for when to use the tool. However, it does not explicitly mention when not to use it or name alternatives, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It discloses the parameter behavior (pass one to filter, omit to get all) and the structure of each example (question, graphql, variables, notes). It also indicates the categories available, which adds behavioral context beyond the bare schema. There is no contradiction with annotations since none exist.
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?
The description is compact and well-structured, with the main purpose in the first sentence, followed by category list and example structure. Every sentence adds value, and there is no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one optional parameter and an output schema exists (which defines the returned structure), the description covers all essential aspects: what it returns, how to filter, and what each example contains. It is sufficiently complete for an agent to invoke correctly without further clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the category parameter. It does so thoroughly by listing all valid categories (impact, provenance, calc, datasource, search, inventory, governance, ownership) and explaining that passing one filters while omitting returns all. This fully explains both the meaning and usage of the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'Return' and specifies the resource: a schema cheat-sheet and a curated library of lineage questions with GraphQL queries and variables. It distinguishes itself from sibling tools like graphql_query, which executes queries, and where_used/impact_analysis, which are specific lineage analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Read this before composing a `graphql_query`', providing clear when-to-use guidance. It also explains the optional category filter and the ability to omit it, giving clear usage context. However, it does not explicitly mention when not to use it or contrast with other siblings beyond graphql_query.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that introspection is enabled, and details exactly what is returned in each mode (Query entry points with args, full type list, or type fields with result types and args). This gives the agent a precise understanding of the tool's behavior.
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?
The description is compact and front-loaded, with the core action in the first sentence. The second sentence efficiently covers both invocation modes and ties the purpose to writing correct queries. Every clause earns its place; there is no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter) and the presence of an output schema, the description is complete. It covers both usage scenarios, explains the data returned, and provides examples of type names. No important details are missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the parameter name 'type_name' with no description (0% coverage). The description compensates fully by explaining the no-argument default, what happens when type_name is provided, and giving concrete examples of valid values. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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: 'Introspect the live Metadata API GraphQL schema', which clearly distinguishes this from sibling tools like graphql_query. It then elaborates with two concrete usage modes (no argument vs. with type_name), making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool: before writing a query, to understand exactly what the server exposes. It explains both invocation patterns but does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses critical runtime behaviors: read-only enforcement, exact case-sensitive filters, page size max 1000, ~20k node limit, partial_results flag, no auto-paging, and Data Management dependency — all beyond the absent annotations.
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?
Long but efficiently structured with front-loaded purpose, then actionable guidelines and caveats. Every paragraph earns its place, and the organization makes the density navigable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return format, error handling, partial results, Data Management prerequisites, entry points, and helper tools, making it fully self-contained for a complex API with no output schema shown.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description compensates with detailed GraphQL query construction guidance (filters, paging, inline fragments, connections). However, the optional 'variables' parameter is not explicitly explained, leaving a minor gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Run ANY read-only GraphQL query against the Tableau Metadata API' and identifies itself as the general-purpose lineage tool, distinguishing from specialized siblings like where_used and lineage_examples.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly directs when to use it ('use it for any lineage question'), when to call where_used for downstream lineage without Data Management, and points to lineage_examples and introspect_schema for templates and schema inspection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but description discloses exact case-sensitive matching, transitive multi-hop behavior, derivation of workbooks from downstream sheets/dashboards, and site-level Data Management limitation. It also notes the flat downstreamWorkbooks edge is unreliable, which is valuable behavior beyond schema.
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?
The description is longer than typical but every sentence adds unique operational context: main behavior, comparison, matching rules, workbook derivation, and fallback. Front-loaded with the core purpose and no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no annotations and minimal schema, the description covers output contents, owner de-duplication, caveats about Data Management, and relationship to sibling tool. It provides enough context for correct invocation without needing additional documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema only has 'name' with no description. Description adds that `name` is exact and case-sensitive and identifies the target as a column, field, or table, giving the parameter actionable meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'Full transitive (MULTI-HOP) downstream impact' and clarifies 'what breaks if I change/drop this?' It specifically distinguishes from `where_used`, making sibling differentiation explicit. This is a strong purpose statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly frames when to use ('what breaks if I change/drop this?') and contrasts with `where_used` ('one core-lineage hop'). Also provides a fallback direction when transitive lineage may not be indexed ('use `where_used` for the direct references').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals default search scope (workbooks, published datasources, database tables), optional `types` filtering, grouping of matches by type, client-side pagination with a ~1200 item scanning limit per type, and the presence of a `note` field indicating this limitation. This goes beyond the schema and makes hidden behaviors explicit.
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?
The description is compact yet information-dense, front-loading the core purpose in the first sentence. Every subsequent sentence adds essential detail—usage context, defaults, side effects, and alternatives—without redundancy or padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (so return structure need not be spelled out), the description covers all invocation-relevant aspects: what it searches, how to narrow results, how results are organized, pagination limitations, and when to choose an alternative. It is complete for an agent to select and call the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It fully explains both parameters: `term` as a case-insensitive substring matched against the NAME field, and `types` with the allowed values (workbook, datasource, table, field, column) and default behavior when not passed. This adds all necessary meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Find') and resource ('content whose NAME contains `term`') and clarifies it performs a case-insensitive substring match. It also distinguishes this tool from siblings by stating that every other tool and the Metadata API filter are exact-match, making its purpose and scope immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('Use this when you only know part of a name') and contrasts it with alternatives, saying to prefer `graphql_query` or `where_used` once the exact name is known. This provides clear decision guidance for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and excels: it discloses exact case-sensitive matching, grouping of results by workbook, and the underlying core lineage mechanism, setting clear expectations for the agent.
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?
The description is concise yet dense, with each sentence serving a distinct purpose: purpose, parameter details, result behavior, and alternative tool. There is no waste or redundancy, and important details are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description doesn't need to explain return values. It fully covers the use case, parameter semantics, behavioral nuances, and alternatives, making it complete for a query tool with minimal parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description thoroughly explains the `names` parameter: what types of names are accepted (column, field, alias, table), exact and case-sensitive matching, and support for multiple names in one call. This fully compensates for the lack of schema-level parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds workbooks and published datasources that use given names, with a specific verb and resource. It explicitly names graphql_query as an alternative for other question shapes, distinguishing from at least that sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit context for when to use this tool for 'where is this used / impact analysis' questions and says to use graphql_query for other shapes. It also explains that it works without the Data Management add-on, giving clear usage exclusions and conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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