wgea-mcp

Server Quality Checklist

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Latest release: v0.6.13
Disambiguation5/5
Each tool has a clearly distinct purpose: describe_dataset for metadata, get_data for raw queries, latest for latest year convenience, list_curated for dataset inventory, search_datasets for discovery, and top_n for ranked results. No functional overlap.
Naming Consistency4/5
Most tools follow verb_noun snake_case (describe_dataset, get_data, list_curated, search_datasets). 'latest' and 'top_n' deviate slightly but are predictable and in line with common conventions for convenience tools.
Tool Count5/5
Six tools is well-scoped for a specialized dataset query server. Each tool addresses a distinct workflow step: discovery, metadata inspection, data retrieval, latest-year convenience, and top-N ranking.
Completeness5/5
The tool set covers the full exploration-to-query lifecycle: list available datasets, search, describe, query with filters, get latest year, and get top/bottom rows. No obvious gaps for a read-only public data API.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- Last stable release on July 27, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the fuzzy-search behavior, output fields (id, name, description, update_frequency, is_curated), and that results are ranked. It does not mention rate limits or authentication but given the read-only nature, the transparency is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with paragraphs and examples, but the list of seven datasets could be moved to a separate reference. It is informative but slightly verbose, earning a score of 3.
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?
With an output schema implied (DatasetSummary fields mentioned), the description covers return values and ranking. It lacks details on pagination or error handling, but given the tool's simplicity, this is sufficient.
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% description coverage, so the schema already documents both parameters. The description adds context about the dataset catalog content but does not significantly enhance parameter meaning beyond what the schema 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 it is a fuzzy-search tool for the curated WGEA dataset catalog, specifying the verb and resource. It lists the seven curated datasets, distinguishing it from siblings like list_curated, which likely lists all datasets without search.
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?
Examples illustrate typical use cases (e.g., 'parental leave', 'women in management'), and results are described as ranked by relevance. While it does not explicitly state when not to use it, the context with sibling tools implies that list_curated is for browsing all datasets and describe_dataset for details on a specific one.
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 full burden of behavioral disclosure. It transparently describes the return structure, including fields like row_count, download_url, and fuzzy hints. It also mentions fuzzy matching on employer_name and wildcard support. However, it does not discuss error handling, rate limits, or idempotency.
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 well-structured with separate sections for examples and return information. It is front-loaded with the core purpose. While it is somewhat lengthy, every sentence serves a purpose, and the organization helps readability without sacrificing clarity.
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 complexity (6 parameters, no annotations, but with output schema), the description provides sufficient context. It covers practical usage through examples, parameter semantics, and return details. The agent can effectively decide when and how to invoke this tool without ambiguous gaps.
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?
Input schema has 100% coverage, establishing a baseline of 3. The description adds value beyond schema by providing detailed examples for filters and start_period format, explaining fuzzy matching, and clarifying the default and maximum for max_rows. This enriches the semantic understanding for the agent.
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: 'Query a curated WGEA dataset and return observations.' It uses specific verbs and resources, and provides multiple concrete examples covering different datasets and filters, leaving no ambiguity about what the tool does.
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 examples showing typical usage contexts but does not explicitly state when to use this tool versus alternatives like search_datasets or describe_dataset. There is no guidance on when not to use this tool or when a sibling would be more appropriate.
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 full burden. It discloses key behavior: trimming to latest reporting_year, and mutual exclusivity of limit/max_rows. However, it does not mention potential side effects (e.g., read-only nature) or performance considerations, which are minor gaps.
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 well-organized with sections, examples, and parameter notes. Each part serves a purpose, but the length could be slightly trimmed without losing clarity. The use of code examples aids understanding.
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?
While the description covers most aspects, there is an inconsistency: the schema for 'limit' has default null but the description states 'Default 2000'. This could confuse an AI agent. Additionally, given the presence of an output schema, the description could reference it. Siblings are acknowledged but not fully compared.
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%, but the description adds value by clarifying preferences (limit over max_rows), labeling max_rows as legacy, and explaining mutual exclusivity. This goes beyond the schema's descriptions, providing practical usage guidance.
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 returns rows from the most recent WGEA reporting year for a dataset. It uses a specific verb ('Return rows') and resource ('from the most recent WGEA reporting year for a dataset'), and distinguishes itself from sibling tools like get_data by focusing on the latest year.
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 context for when to use this tool (e.g., 'what's the current gender breakdown at CBA?') and includes examples with filtering. It implicitly contrasts with get_data for all years. However, it does not explicitly state when not to use or direct to alternatives for non-latest data.
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?
The description mentions the sorted return order, which adds behavioral context beyond the schema. No annotations exist, so the description carries the burden; it communicates that the tool is a read-only listing with no side effects, sufficiently transparent for a no-parameter 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 two clear sentences: first states the action, second describes the return. Every word is necessary and front-loaded, with no superfluous content.
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 no parameters and a simple output (sorted list of IDs), the description fully covers its purpose and return. The presence of an output schema is mentioned in context, but the description already sufficiently explains the return format.
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 and 100% schema coverage, the baseline is 4. The description adds no additional parameter semantics, which is acceptable as there are no parameters to document.
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 lists every curated dataset ID, specifying the version context. It distinguishes itself from sibling tools like search_datasets (which likely filters) and top_n (which limits) by emphasizing 'every' and 'sorted', making its purpose 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?
No explicit usage guidance or differentiation from sibling tools (e.g., when to use list_curated vs search_datasets). The description is adequate for a simple listing tool but lacks explicit alternatives or when-not scenarios, which are present in sibling tools.
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, so description carries full burden. It discloses ranking across one reporting year, default behavior, sorting, and return format. No contradictions. Could mention read-only nature but implied.
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 purpose, context, examples, and return info. Examples are helpful but make it slightly longer. Still clear and front-loaded.
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 6 parameters and output schema exists, description covers concept, parameters, examples, and return. Could mention error handling but complete enough for typical use.
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?
Input schema covers 100% of parameters with descriptions. Description adds value by explaining measure meaning per dataset, reporting year format, and providing examples that illustrate usage.
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 returns the N rows with largest/smallest measure value. It distinguishes from sibling tools like get_data by explaining server-side rank-and-slice, and provides concrete examples.
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 indicates this is the most common agent workflow for top N queries and contrasts with fetching full tables. It defaults to latest year but doesn't explicitly state when not to use it, though the context 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 exist, so description carries full burden. It implicitly discloses read-only behavior by stating it describes and returns metadata, but does not explicitly confirm no side effects. The return structure is detailed, adding transparency.
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?
Concise two-paragraph structure with front-loaded purpose and immediate usage guidance. Every sentence adds value; no wasted words.
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?
Complete description for a simple tool. Covers purpose, usage, return structure, and links to sibling tools. Output schema exists, so detailed field descriptions are not needed in the description.
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% with one parameter (dataset_id) documented with examples and description. Description adds usage context but no additional semantic detail for the parameter 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?
Clearly states it describes a dataset's filterable dimensions, measures, units, and source. Includes concrete examples of filter keys and measure aliases, distinguishing it from siblings like get_data.
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 instructs to use before calling get_data on a new dataset, providing clear when-to-use context and indicating it's a preparatory step.
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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