Schwaizer BFS MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Tools are mostly distinct, but some overlap exists between get_dataset_dimensions, get_dataset_metadata, and get_dataset_info as they all provide metadata about datasets. Descriptions help differentiate, but agents might be confused about which to use for dimension exploration.
Naming Consistency5/5
All tools follow a consistent 'verb_noun' pattern with snake_case (e.g., list_themes, search_datasets, get_sse_data). No deviations or mixed conventions.
Tool Count5/5
8 tools is well-scoped for a statistical data server covering two APIs (PXWEB and SSE) and basic discovery. Not too few to be incomplete, and not overwhelming.
Completeness4/5
The tool surface covers discovery, metadata retrieval, and data extraction for both APIs. Minor gaps include lack of a tool to list all datasets directly (search is needed) and no tool for SSE-specific search, but overall the domain is reasonably covered.
Average 3.8/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It explicitly states the tool retrieves information and lists returned metadata, indicating a read-only operation. However, it does not disclose any additional behavioral traits such as idempotency, response size, or data freshness, which are moderately important for a data 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose and then listing return fields. Every word is meaningful; no filler or repetition. It is optimally concise for the complexity of the tool.
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 absence of an output schema, the description compensates by listing the types of metadata returned (title, description, languages, date, links). This provides sufficient context for an agent to understand the return value. However, it could be improved by noting the response format or mentioning that the tool only works with BFS datasets specifically.
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 three parameters with detailed descriptions, achieving 100% schema coverage. The description adds limited value by mentioning 'using its BFS number or asset number' but does not enhance semantic understanding beyond what the schema already provides. Baseline 3 is appropriate.
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 clearly states the verb 'Get detailed information' and the resource 'specific BFS dataset' using either a BFS number or asset number. It lists the type of metadata returned. However, it does not explicitly differentiate from sibling tools like get_dataset_metadata or get_dataset_dimensions, which may have overlapping functionality.
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 other sibling tools such as get_dataset_dimensions or search_datasets. There is no mention of preconditions, exclusions, or alternatives, leaving the agent to infer usage from the tool name and description alone.
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 provided, the description carries the full burden of behavioral disclosure. It only mentions returning a list of datasets with metadata, omitting details such as whether the operation is read-only, any rate limits, pagination behavior, or side effects. This is insufficient for a complex search 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?
The description is concise at three sentences and front-loads the primary purpose. While efficient, it could be more structured (e.g., bullet points for parameters) but remains easily readable.
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 tool's 7 optional parameters, no required params, and no output schema, the description gives a high-level overview but lacks guidance on default behavior when no query is provided, how filters combine, or how to handle large result sets. It does not reference sibling tools to aid selection.
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 each parameter is documented. The tool description adds little beyond summarizing that search is by 'keywords, themes, spatial divisions, and other criteria,' which maps directly to the schema. It does not provide additional examples or deeper semantics.
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: searching for statistical datasets in the BFS catalog with various filters. It specifies the resource (datasets), action (search), and result (list with BFS numbers and metadata), effectively distinguishing it from siblings like get_dataset_info or list_themes.
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 explanation implies usage for searching by keywords, themes, etc., but does not explicitly state when to use this tool versus alternatives like get_dataset_info (for specific dataset details) or list_themes (for theme selection). No exclusions or when-not-to-use guidance is provided.
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 cover behavior. It describes the return (dimension codes and values) but does not explicitly state read-only nature or limitations.
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?
Three sentences, each adding value: defines the action, states usefulness, and describes return. No redundancy.
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 two simple parameters and no output schema, the description adequately covers purpose and return, but could be improved by specifying output format or structure.
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% and the description adds no new meaning beyond the schema's parameter descriptions; it only restates the same information.
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 ('Get') and resource ('simplified view of available dimensions and their values for a PXWEB dataset'), and distinguishes itself from siblings by focusing on dimensions for filtering, not metadata or data.
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 use for understanding filters before querying ('useful for quickly understanding what filters you can apply'), but does not explicitly state when not to use or provide alternatives among siblings.
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 present, so the description carries the full burden. It describes the API as 'modern SDMX-based' and 'works well', but does not disclose behavioral traits like rate limits, response format, or read-only nature. It is adequate but minimal.
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?
Three sentences efficiently convey purpose, API context, and usage guidance. The mention of 'modern SDMX-based' adds slight value but is not essential. Well-structured 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 the 5 parameters (1 required), no output schema, and no annotations, the description adequately covers the tool's role and prerequisite steps. It could elaborate on return values, but the context is reasonably complete.
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 the baseline is 3. The description adds general context about filtering by dimensions and time periods but does not provide additional meaning beyond the schema descriptions for each 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 clearly states the tool retrieves time-series data from the Swiss Stats Explorer API, specifying a verb and resource. It distinguishes from siblings by recommending get_sse_metadata first, clarifying its role.
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 using get_sse_metadata first to explore dimensions, providing clear context for when to use this tool. It lacks explicit exclusions or alternatives beyond that, but the guidance is sufficient.
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 provided; the description mentions default return format (json-stat) but does not cover other behavioral traits like error handling, rate limits, or required permissions.
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, no wasted words. Purpose and key usage hint are front-loaded.
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?
Adequate for a simple retrieval tool with a required parameter, but lacks details on output structure or potential errors, which would improve 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 coverage is 100%, and the description adds little beyond what the input schema already provides (e.g., filtering via query). Baseline score 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 clearly states the tool retrieves statistical data from a BFS dataset using the PXWEB API, with optional filtering. This distinguishes it from sibling tools which handle metadata listing or 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?
Explicitly advises to use get_dataset_metadata first to discover dimensions, providing clear context for when to use this tool. No contraindications given, but usage is well-scoped.
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 states what the tool returns but does not disclose any behavioral traits such as side effects, authorization requirements, or rate limits. For a simple read-only operation, this is adequate but not 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?
Two concise sentences with front-loaded purpose. Every word adds value; no redundancy.
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 no output schema, the description adequately explains the return content (dimensions, codes, values) and usage context. It is sufficient for a metadata retrieval tool with only two well-documented parameters.
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 both parameters are well-described in the schema. The description adds no new semantic information beyond what the schema already provides; it only rephrases the purpose.
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 'Get', the resource 'complete metadata structure for a BFS dataset', and specifies what is returned (dimensions, codes, possible values). It distinguishes from sibling tools like get_dataset_dimensions by emphasizing completeness and pre-query usage.
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 advises to use this before querying data to understand filters, providing clear context. However, it does not mention when not to use it or compare directly to siblings like get_dataset_info.
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 provided; description carries burden. States it lists all themes, which implies read-only. No mention of side effects or limits, but for a simple listing this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, first defines purpose, second adds output details and usage hint. No waste, 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?
No output schema, but description explains output fields and how to use them. Adequate for a simple listing tool with one parameter.
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?
Only one parameter 'language' with schema covering 100%. Description adds context about output fields (name, prodima, theme code) but not new parameter details beyond schema's description.
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?
Clear verb 'list' and resource 'themes'. Specifies output components (name, prodima number, theme code). Differentiates from sibling tools like search_datasets which filter datasets.
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 advises using the prodima number with search_datasets for filtering, providing clear usage context. Does not state when not to use, but implied by 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?
With no annotations, the description carries full burden. It transparently describes it as a read-only metadata retrieval operation returning dimensions and values. No destructive effects or additional behaviors are implied, which is appropriate for this function.
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 concise sentences with no wasted words. The action verb 'Get' is front-loaded, and the important usage guidance is included efficiently.
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 two parameters, no output schema, and multiple sibling tools, the description sufficiently explains the output (dimensions and values) and the tool's role. It could elaborate on language parameter effects but overall complete.
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 descriptions already defining numberBfs and language. The description adds workflow context but no new semantic detail beyond the schema. Baseline of 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 clearly states it 'Gets metadata for a Swiss Stats Explorer (SSE) dataset' and specifies it 'Returns available dimensions and their possible values.' It differentiates itself from siblings like get_sse_data by framing itself as a prerequisite step.
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 advises 'Use this before calling get_sse_data to understand what filters you can apply,' providing clear context and sequence. However, it does not mention when not to use it or alternatives like get_dataset_dimensions.
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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