LINDAS MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: discovery (list_cubes, search_datasets), schema exploration (get_cube_structure, get_dimension_values, get_cantons), counting (count_observations), querying (query_observations), and geography resolution (resolve_geography). No two tools overlap in function.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., count_observations, get_cube_structure, list_cubes). The naming is predictable and easy to parse.
Tool Count5/5
With 8 tools, the server is well-scoped for its purpose of exploring and querying Swiss open data cubes. Each tool earns its place, covering discovery, schema analysis, and data retrieval without unnecessary redundancy.
Completeness5/5
The tool set covers the full workflow for a read-only data cube explorer: discovering cubes, understanding structure and dimensions, filtering with valid values, counting results, and fetching observations. The inclusion of geography resolution and text search fills common needs.
Average 4/5 across 8 of 8 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
No annotations exist, so description carries burden. It accurately describes a non-destructive list operation but lacks details on rate limits or authentication needs, acceptable for a simple 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?
Two sentences, zero waste, front-loaded with purpose and immediate next-step guidance—optimal conciseness.
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?
No output schema; description states returned data (IRIs and names) but not structure or format. Adequate for a simple list but could specify response shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description omits the 'language' parameter entirely, failing to explain its role or values, leaving the agent without needed context.
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 tool lists all 26 Swiss cantons with IRIs and names, which is specific and distinguishes it from siblings like query_observations by hinting at downstream use.
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?
Provides explicit usage hint to use returned IRIs in query_observations, but no when-not-to-use or alternatives, leaving gaps for an AI agent.
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 provided, so description must fully disclose behavior. It only states 'list' (read-only), but omits details on pagination, ordering, authorization, or data freshness. The limit/offset parameters imply pagination but are not explained.
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 with no wasted words. First sentence states purpose, second provides actionable next step. Highly efficient.
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?
For a simple list tool, it gives basic purpose and next steps but lacks output format details (e.g., fields returned, error handling, empty results). Moderate completeness given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the 'limit' or 'offset' parameters at all, leaving the agent without guidance on how to control pagination or row count.
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 'List available data cubes on LINDAS with their titles and descriptions' and positions it as a discovery tool, distinguishing it from siblings like get_cube_structure which is for understanding dimensions.
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 using this for discovery and suggests next step: 'Call get_cube_structure next to understand a cube's dimensions.' It provides clear context on when to use but no exclusions.
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 bears full responsibility for behavioral disclosure. It only states the search scope and does not mention whether the operation is read-only, any authentication requirements, rate limits, pagination, or result format. This is insufficient for an agent to understand the tool's full 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 two sentences with no extraneous information. The first sentence declares the core function, and the second provides usage guidance. Every word is useful, and it is front-loaded with the key action.
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 low complexity (2 params, no output schema), the description provides the core purpose and usage guidance. However, it lacks detail about the return format, pagination, or how results are ranked. Without an output schema, more information about what the agent should expect would improve completeness. Score 3 is adequate but with gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 50% (query has 'Text to search for', limit has no description). The tool description adds no meaning to either parameter beyond what the schema provides. It mentions 'by title and description' but that describes the search scope, not the parameter semantics. For a tool with less than 80% coverage, the description should compensate, but it does not.
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 'Full-text search across LINDAS cubes by title and description', providing a specific verb, resource, and scope. It distinguishes itself from sibling tools like list_cubes (which lists all cubes without search) and query_observations (which queries data). The examples ('population', 'forest', 'unemployment') further clarify the tool's purpose.
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 states when to use the tool: 'Use this when looking for datasets about a specific topic'. This provides clear context. However, it does not explicitly mention when not to use it or name alternative tools, though siblings are listed. This aligns with the '4=clear context, no exclusions' level.
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 indicates read-only behavior (getting values) and mentions human-readable labels, but lacks details on response format, pagination, or error conditions. 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 sentences, concise and front-loaded with purpose. Every sentence adds value without 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 and limited annotations, description effectively covers purpose, usage, and relationship to sibling. Lacks return value details but sufficient for core task.
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 at 25% (only dimension_path described). Description adds context for dimension_path (use path from get_cube_structure) but doesn't elaborate on cube_uri, limit, or language. Compensates partially for low coverage.
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 'get distinct values' with specific resource 'dimension of a cube' and hints about human-readable labels. Includes examples and links to sibling tool get_cube_structure, distinguishing it from other 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?
Explicitly states use case: 'after get_cube_structure to discover what values you can filter on.' Instructs to pass the 'path' value. No explicit when-not-to-use, but context is clear.
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, placing the full burden on the description. The description only states the tool works for specific place types and gives a usage scenario, but does not disclose behaviors like error handling, case sensitivity, or whether it returns multiple matches. 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?
The description is extremely concise with two sentences: the first states the purpose and scope, the second gives usage guidance. Every word adds value, and the structure is front-loaded with the core function.
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?
For a simple lookup tool, the description provides essential context about the types of places and the use case. However, it lacks details about error conditions and multiple results, which would improve completeness for an agent. Given the low complexity, the description is mostly adequate.
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 one parameter (name) with a description; language has enum and default but no description (50% coverage). The description does not add meaning beyond what the schema provides for the language parameter, nor does it compensate for the missing parameter 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?
The description clearly states the tool resolves a place name to a LINDAS IRI, specifying it works for cantons, municipalities, and districts. This distinguishes it from sibling tools like get_cantons or query_observations.
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 to use this tool when a user mentions a Swiss place name and needs its IRI to filter cube observations. It does not explicitly mention when not to use it, but the context of sibling tools provides implicit guidance.
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 adequately describes the tool's behavior (count with optional filters). It doesn't mention return type or side effects, but for a read-only count operation, the transparency is sufficient.
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 efficiently cover purpose and usage guidelines. No redundant or unnecessary words.
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?
For a simple count tool with no output schema and no annotations, the description is fairly complete. It covers when to use and basic behavior. Lacks explicit return format but is otherwise adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and description adds minimal parameter detail beyond 'optionally filtered'. It does not explain cube_uri or the filter structure (dimension, value, operator). The description fails to compensate for the lack of schema 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 verb 'Count' and the resource 'observations in a cube', and notes optional filtering. It distinguishes from sibling query_observations by implying it returns a count, not the 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 advises using this tool before query_observations to check result size, and suggests actions if count is large (use filters or smaller limit). Provides clear when-to-use and alternatives.
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?
Without annotations, the description carries full burden. It explains the output includes a 'path' field used as property URI in other tools, which is valuable behavioral context. However, it does not explicitly state read-only nature or absence of side effects, but being a 'get' operation implies safety.
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 gives usage guidance and key output detail. Every sentence adds value, no redundancy. Front-loaded with core action.
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 single-parameter tool without output schema, the description fully explains its role in the workflow, what it returns, and how the output is used. No additional context needed for correct selection and invocation.
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 sole parameter cube_uri is described in schema as 'The URI of the cube (from list_cubes results)'. The description adds context that the URI comes from list_cubes, placing the parameter in the overall workflow. With 100% schema coverage, baseline is 3, but the extra guidance pushes it to 4.
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 the structure of a data cube, specifying dimensions, measures, datatypes, and constraints. It distinguishes itself from sibling tools like get_dimension_values and query_observations by outlining its role as a preliminary discovery tool.
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 mandates calling this tool before query_observations to understand available dimensions and measures. It provides a clear directive on when to use it, effectively guiding the agent in workflow sequencing.
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 are provided, but the description discloses the need to learn cube structure beforehand and the use of dimension/measure paths. It could mention output format or side effects, but queries are inherently safe.
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 (3 sentences) with key instructions front-loaded. Every sentence is informative, 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 could explain the return format. However, it sufficiently covers usage and prerequisites, making it largely complete for a query tool.
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 coverage is only 29% (measures and dimensions have descriptions), but the description adds crucial context: dimensions and measures should be paths from get_cube_structure, and filters should use values from get_dimension_values.
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 queries observations from a data cube with optional filtering and pagination. It distinguishes from siblings by referencing related tools like get_cube_structure and get_dimension_values.
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 explicitly says 'ALWAYS call get_cube_structure first' and instructs to use get_dimension_values for valid filter values, providing clear when-to-use and alternative tools.
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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