gx-mcp-server

Server Quality Checklist

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Latest release: v2.0.3
Disambiguation5/5
Each tool addresses a distinct concern: health/version, data loading, suite/expectation management, and validation execution/result retrieval. There is no overlap between run_checkpoint and get_validation_result, as one performs the validation and the other retrieves its output.
Naming Consistency5/5
All tools follow a snake_case verb_noun pattern (run_checkpoint, get_validation_result, load_dataset, create_suite, add_expectation, get_version), with 'ping' as the only exception but it is a standard health-check name. The convention is uniform and predictable.
Tool Count5/5
Seven tools is well within the ideal range for a focused MCP server. Each tool serves a necessary function for the core workflow of building and running data validation, with no redundancy or scope creep.
Completeness3/5
The set covers the primary workflow (load data, create suite, add expectation, run checkpoint, get result), but lacks read operations for existing suites or expectations, and has no update/delete capabilities. Agents cannot discover or manage existing validation assets without extending the surface.
Average 3.7/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on August 6, 2025
- 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.
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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?
With no annotations, the description must disclose behavioral traits, but it only states the obvious action. It omits details on whether the operation is synchronous, if it creates background jobs, side effects, or what the output contains. The schema notes checkpoint_name is unused, but the description doesn't clarify this.
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 a single, front-loaded sentence with no filler. Every word contributes to stating the tool's basic function.
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, the description lacks workflow context and behavioral details. It fails to mention that checkpoint_name is unused, background_tasks behavior, or how this relates to sibling tools like create_suite and get_validation_result. A more complete description would clarify the operation's nature and placement in the pipeline.
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 75%, so most parameters are already documented. The description adds minimal semantic context by mapping 'expectation suite' to suite_name and 'dataset' to dataset_handle, but it doesn't explain the undocumented background_tasks parameter or the 'unused' checkpoint_name.
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 identifies the tool's action as running a validation checkpoint on a dataset with an expectation suite. It uses a specific verb and resource, distinguishing it from siblings like get_validation_result (retrieval) and create_suite (creation).
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?
No guidance is provided on when to use this tool versus alternatives or how it fits into the workflow. It does not mention prerequisites (e.g., suite must exist) or contrast with get_validation_result.
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 behavior. It mentions that data is loaded into memory and that a handle is returned, which are key behavioral traits. However, it fails to note whether the operation is read-only, whether network access is needed for URLs, or potential memory implications. It is not misleading, but lacks depth.
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 a single, well-formed sentence that efficiently conveys the core functionality without unnecessary detail. It is front-loaded with the action and ends with the return value, making it easy to parse.
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?
The tool has an output schema (though not shown) and comprehensive parameter descriptions, so return values are likely covered. However, the description does not explain what the 'handle' is or how it should be used with sibling tools like run_checkpoint. The lack of usage context makes it less complete for an agent unfamiliar with the workflow.
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%, and the schema already documents all parameters with descriptions. The description adds minimal semantic value by enumerating sources (CSV string, URL, local file) which aligns with the source_type parameter, but this is also captured in the schema's 'source' description. No additional parameter guidance is provided.
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 that the tool loads data from CSV string, URL, or local file into memory and returns a handle. This is a specific verb (load) with a clear resource (data) and enumerates the source types, effectively distinguishing it from sibling tools that operate on checkpoints or validation results.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention prerequisites, such as needing to load data before running a checkpoint, nor does it reference any alternative loading mechanisms. It simply states what the tool does without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It reveals that the tool may create a suite, but does not disclose whether expectations are appended or overwritten, how errors are handled, or any side effects. This leaves significant ambiguity.
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 a single, clear sentence with no wasted words. It front-loads the primary action and includes the key behavioral nuance about creating the suite.
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 low complexity, rich schema, and presence of an output schema, the description addresses the core purpose and the create-if-missing behavior. It is slightly thin on edge cases but sufficient for a tool of this simplicity.
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 schema covers all three parameters with descriptions and an example for kwargs. The tool description adds no additional parameter detail, so it meets the baseline but does not exceed it.
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 verb 'add' and the resource 'expectation to a suite', and even clarifies the behavior when the suite doesn't exist ('or create it'). This distinguishes it from siblings like create_suite, which would only create the suite.
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 implies the tool is used to add an expectation and optionally create the suite, but does not explicitly contrast with alternatives or provide when/when-not guidance. The context is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose side effects, error behavior, idempotency, or what happens if the suite already exists. The only transparency is the schema's note that dataset_handle is unused, but the description itself offers little beyond the basic create action.
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?
A single clear, front-loaded sentence conveys the core purpose without wasted words. It is appropriately sized for a simple create operation.
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, all parameters are fully described in the schema, and an output schema exists. The description is sufficient for basic usage, though it could mention behavioral outcomes or prerequisites for completeness.
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 100%, so the baseline is 3. The description adds 'optionally profiled from a dataset,' but this is already reflected in the profiler parameter. It does not clarify the deprecation or the unused nature of dataset_handle beyond what the schema already states.
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 uses a specific verb ('Create') and resource ('named ExpectationSuite'), clearly distinguishing from siblings like add_expectation. It also mentions optional profiling, which adds useful scope.
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 implies the tool is for creating a new suite, optionally with profiling, but offers no explicit guidance on when to use it versus alternatives like add_expectation or run_checkpoint. There is no exclusion or alternative tool mention.
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 are provided, so the description carries the full burden. It correctly implies a read-only operation ('fetch'), but does not disclose potential edge cases such as behavior for invalid/missing validation_id, data freshness, or whether the tool has side effects. For a simple retrieval tool this is minimally adequate.
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 a single, front-loaded sentence that conveys the essential purpose with no filler. Every word contributes meaning.
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 (one parameter, one clearly defined action) and an output schema is present, so the description does not need to detail return values. The description is sufficient for the tool's complexity, though it could have briefly mentioned that this is the follow-up to run_checkpoint.
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 already provides full coverage for the single parameter, including an explicit note that validation_id is 'ID returned from run_checkpoint().' The description adds no further parameter-level meaning, so the baseline score of 3 applies.
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 ('Fetch') and identifies a clear resource ('detailed validation results') scoped to 'a prior validation run.' This clearly distinguishes it from sibling tools like run_checkpoint, which presumably initiates the run.
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 phrase 'for a prior validation run' implies this tool should be used after run_checkpoint has been called. No explicit exclusions or alternative tools are named, but the context is clear enough for an agent to infer when to use it.
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 are provided, so the description must convey behavior. The verb 'Return' implies a safe read operation with no side effects, but the description does not explicitly mention that it is non-destructive or disclose any error handling or return format. However, the existence of an output schema mitigates the need to describe the return value.
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 a single sentence with six words, front-loaded and free of any irrelevant information. Every word earns its place.
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 utility tool that simply returns a version, the description is fully complete. The presence of an output schema covers the return format, and the distinguishing sibling tools are all different in purpose, so no additional context is needed.
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 tool has zero parameters, and the schema is empty with 100% coverage. The description does not need to add parameter details. Per the rubric, a baseline of 4 applies when there are no parameters.
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 function: 'Return the API version for MCP server.' It uses a specific verb ('Return') and a specific resource ('API version') and is easily distinguishable from sibling tools such as ping or get_validation_result.
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 implies the tool is used to retrieve the server's API version, but provides no explicit when-to-use guidance or alternatives. Given the simplicity of the tool and lack of similar siblings, the implied usage is sufficient, but no exclusions are stated.
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 disclosure. It clearly states the action but doesn't explicitly mention side effects (or lack thereof) or any read-only nature. For a ping/health check, this is implicit, but the description is sparse.
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 a single, front-loaded sentence with no wasted words. Every word is meaningful and directly conveys the purpose.
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 simple health-check tool with no parameters and an output schema, the description is complete. It fully captures what the tool does, and the output schema handles return value details.
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?
With zero parameters, the baseline is 4. The description correctly has no parameter details, as there is nothing to explain. This is fully 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 'Return basic health status' uses a specific verb and resource, clearly distinguishing it from siblings like run_checkpoint and get_validation_result. The purpose is immediately apparent and unambiguous.
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 implies usage (when you want a health check) but provides no explicit context, exclusions, or alternatives. Given the simplicity of the tool, this is acceptable, but it doesn't offer guidance beyond what might be assumed.
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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