vizro-mcp

Official

by mckinsey

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v1.0.3
Disambiguation5/5
Each tool has a clearly distinct purpose: data loading, planning, sample info, dashboard validation, chart validation, and schema retrieval. There is no functional overlap.
Naming Consistency4/5
Tools follow a verb_noun pattern with some variation (load_and_analyze, get, validate). While mostly consistent, the use of compound verbs like 'load_and_analyze' differs from single verbs, but overall the pattern is clear.
Tool Count5/5
6 tools is well-scoped for the domain of Vizro chart/dashboard creation. Each tool serves a necessary step in the workflow, neither too few nor too many.
Completeness4/5
The tool set covers the main workflow: data loading, planning, validation, and schema access. However, it lacks tools for editing or deleting existing dashboards, which are minor gaps for a creation-focused server.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 35 of 40 community issues answered or closed in the last 6 months
- 74 commits in the last 12 weeks
- Last stable release on August 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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}
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only states validation and optional browser opening, but omits details like whether code is executed, what constitutes invalid code, side effects, or authentication requirements. The return type is mentioned but not elaborated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with two sentences plus a return note. It front-loads the primary action. However, it could slightly expand on key behaviors without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (mentioned), the description lacks context about when validation is needed, error handling, or workflow integration with sibling tools. It omits important contextual details for an effective agent invocation.
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?
The input schema has 100% coverage with detailed descriptions for all parameters, including nested objects. The tool description adds no extra meaning beyond the schema, so it scores the baseline of 3. It does not clarify how parameters relate to validation logic.
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 validates chart code and optionally opens a PyCafe link in a browser. It uses a specific verb ('Validate') and resource ('chart code'), and the purpose is distinct from sibling tools like 'get_vizro_chart_or_dashboard_plan' and 'validate_dashboard_config'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites, exclusions, or context. It fails to differentiate usage scenarios among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavioral traits. It clearly indicates a read operation (no destructive side effects), but does not mention error behavior, rate limits, or permissions. It is adequate for a simple read tool.
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 extremely concise: two sentences, no fluff. It front-loads the purpose and adds a relevant version note. Every word earns its place.
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?
Given that an output schema exists (so return values are documented elsewhere) and the tool has only one parameter, the description covers the essential information. It lacks some depth on usage context, but is largely complete for a simple schema retrieval tool.
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?
The input schema covers all parameters with detailed descriptions (coverage 100%). The description adds no additional parameter information beyond the schema, which already explains the 'model_name' parameter with examples. Baseline 3 is appropriate.
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 ('Get') and resource ('JSON schema') and distinguishes the tool from siblings like 'load_and_analyze_data' and 'validate_dashboard_config', which serve different purposes. The purpose is 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool vs. alternatives, nor does it provide any context on prerequisites or when not to use it. The mention of the server version is helpful but insufficient as guidance for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears the burden of behavioral disclosure. It states the action (load and analyze), supported formats, and return type, but does not disclose whether the operation is read-only, potential file size/performance implications, or any side effects. This is adequate but could be more thorough.
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 extremely concise: three lines covering purpose, requirement, formats, and return type. It is front-loaded with the main purpose and includes no redundant information. Every sentence earns its place.
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?
Given the simplicity of the tool (one parameter, output schema exists), the description covers the essential aspects: purpose, required input format, supported file types, and return type. It may lack details about remote access authentication or error handling, but overall it is sufficiently complete for an agent to use the tool correctly.
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 has 100% coverage for the single parameter (path_or_url) with a good description. The additional description adds critical usage constraints (absolute paths/URLs) and lists supported file formats, which significantly enhances the parameter semantics beyond what the schema provides.
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 states the tool is used to 'understand local or remote data files' and specifies supported formats and return type. While the verb 'understand' is somewhat vague, the combination with the tool name and listed formats makes the purpose clear. It is distinguishable from sibling tools that handle schema, validation, and chart planning.
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 description includes the critical usage constraint 'Must be called with absolute paths or URLs' but provides no explicit guidance on when to use this tool versus alternatives. The siblings are clearly different in function, so it's implicitly appropriate for data loading, but a brief comparison would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses success outputs (Python code, py.cafe link, auto-open behavior) and mentions return type. However, it does not explain what validation entails (syntax checks, data consistency), leaving process details ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief (two sentences plus a return line) and front-loads the primary purpose. It avoids unnecessary details, though the second paragraph could be considered an aside. It is appropriately sized for a tool with a comprehensive schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (nested objects, output schema implied), the description mentions return behavior and auto-open but lacks details on validation scope (e.g., what checks are performed, prerequisites). Output schema existence reduces the need to explain return values, but process details are missing.
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 coverage is 100%, so baseline is 3. The description only adds value by noting the auto_open parameter's effect and the return type, which is marginal. It does not elaborate on the required parameters beyond the 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 explicitly states 'Validate Vizro model configuration' with a clear verb and resource. It differentiates from siblings like 'validate_chart_code' which focuses on individual charts, making the tool's purpose distinct.
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 advises 'Run ALWAYS when you have a complete dashboard configuration', providing strong contextual guidance. While it doesn't explicitly exclude other scenarios, the sibling tools cover individual parts (e.g., chart validation, data loading), implying this is for full config validation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, so description bears full burden. It discloses return type ('Data info object') and implies read-only behavior, but lacks details on permissions, rate limits, or side effects. Adequate for a simple retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with a bullet list for dataset descriptions. Condition ('If user provides no data') is front-loaded. Slightly verbose but every sentence adds value.
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?
Given the existence of an output schema (not shown), description correctly focuses on input semantics and use case. It adequately covers the tool's role among siblings, which handle user-provided data or code validation.
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?
Schema coverage is 100% (single parameter with enum). Description adds significant meaning beyond schema by explaining each dataset's suitability for different visualizations (e.g., 'iris: mostly numerical... good for scatter, histogram').
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's purpose: to get sample data information when no data is provided. It lists four specific datasets (iris, tips, stocks, gapminder) and their characteristics, distinguishing it from sibling tools that handle user-provided data or schemas.
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 states when to use ('If user provides no data'). Provides guidance on dataset selection based on chart types (scatter, bar, line, etc.). Does not explicitly exclude alternatives but usage condition is clear.
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?
No annotations provided, but description explains the two-step calling behavior and the nature of the return (instructions). Some details like auth or rate limits are absent, but the core behavior is clear.
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?
Very concise, front-loaded with purpose, uses bullet points for clarity, every sentence adds value without 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?
With output schema present and all parameters described, the description covers the needed context for using the tool correctly, including the call sequence.
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?
Schema coverage is 100%, and description adds valuable context for advanced_mode parameter, explaining when to use it. This goes beyond what the schema alone provides.
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 'Get instructions for creating a Vizro chart or dashboard' and specifies it should be called first, making the purpose unambiguous and distinct from sibling tools.
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?
Provides explicit guidance: 'Must be ALWAYS called FIRST with advanced_mode=False, then call again with advanced_mode=True if the JSON config does not suffice anymore.' This tells when to use and how to iterate.
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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