Thailand NSO MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: discovery, structure, constraints, data retrieval, code lookups, and diagnostics. No overlap exists; even get_codelist_description and get_constraints serve different needs.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case (e.g., discover_dataflows, get_constraints, get_data), making it predictable for agents.
Tool Count5/5
With 8 tools, the set is well-scoped for a statistical data server, covering discovery, structure, query building, data retrieval, and code lookups without unnecessary tools.
Completeness5/5
The tool surface covers the full workflow: discover dataflows, get structure and constraints, fetch data, and resolve codes. No obvious gaps for a public statistics API.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 16 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
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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. It mentions 'fast path' but fails to detail authentication, side effects, rate limits, or return format, leaving significant gaps.
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 core purpose and immediately adding usage context. Every phrase earns its place without redundancy.
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 simplicity and lack of output schema, the description covers the basic return value (dimensions and codelists) but omits details on error cases, pagination, or data format, leaving it adequate but not thorough.
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% for the single parameter, so the description adds minimal extra meaning beyond the schema's example. The 'fast path' hint provides context but not parameter-specific 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 gets the data structure (DSD) for a given ID, specifying it returns ordered dimensions and codelists. This distinguishes it from siblings like get_codelist_description or 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Fast path when you already know the codes' implies a specific usage scenario, but it does not explicitly state when not to use the tool or mention alternatives like discover_dataflows for exploring structures.
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 indicates a read operation ("Get") with no side effects, which is appropriate. It does not contradict any annotations (none provided). However, it lacks details on response format or behavior for invalid input.
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?
Single sentence, front-loaded with verb and resource, no 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 tool with one parameter and no output schema, the description is mostly complete. It explains the resource and provides an example. Minor gap: does not specify whether the response includes codes or just labels, but the context is 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?
Schema coverage is 100% with a clear parameter description. The tool description adds an example but no additional semantic 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 the tool gets labels for every code in a codelist, with an example. It distinguishes from sibling tools like get_structure or get_concepts which serve different purposes.
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?
While the description implies usage for retrieving code labels, it does not explicitly state when to use or not use this tool, nor does it mention alternatives. No guidance on prerequisites or error conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure burden. It explains the output format and the effect of omitting periods/dimensions, but lacks details on error handling, permissions, rate limits, or the structure of the returned TSV/URLs. The disclosure is adequate but not comprehensive.
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 relatively concise with three sentences that front-load the main purpose. It uses clear structure and avoids redundancy, though a minor improvement could be to separate usage notes more clearly.
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 complexity (6 parameters, nested objects, no output schema), the description covers key behaviors but omits details about the detail parameter, dimension_at_observation, and how the reproducible URLs are structured. It is sufficient but not fully comprehensive.
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 description coverage is 67%, so baseline is 3. The description adds value by clarifying that dimension_filters can be a string (JSON-encoded), the effect of omitting dimensions, and the Buddhist Era year format. This goes beyond the schema's 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 'fetch', the resource 'observations for a dataflow', and the output format 'TSV table'. It also mentions reproducible URLs, making the tool's purpose specific and distinguishable from sibling tools like discover_dataflows or get_structure.
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 explicit usage guidance: how to omit dimensions, pass multiple codes, and the default period behavior. It includes an important note about Buddhist Era years. However, it does not explicitly state when to avoid using this tool or suggest alternatives.
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 full burden. It explains the level mapping and substring filtering behavior, but does not disclose the return format, case-sensitivity, limits, or error handling. This is adequate but incomplete for a lookup 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 succinct sentences with no wasted words. The purpose is front-loaded, and the essential details (levels, filtering, usage hint) are efficiently conveyed.
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 being a simple tool with 2 optional parameters and no output schema, the description omits the return structure. It states 'look up...codes' but does not explain if results are a list, map, or include additional metadata. For a lookup tool, this is a notable gap in completeness.
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 50% (name described, level not). The description adds significant meaning to the level parameter by mapping each enum value to a code list identifier (CL_AREA, CL_CWT, CL_AMPHOE) and providing context (77 changwat). It reinforces the name parameter as accepting Thai or English substrings, adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up Thai geography codes used by specific dimensions (AREA/CWT) and lists three levels with their code list identifiers. This provides a specific verb-resource pair and distinguishes it from sibling tools like get_data or get_codelist_description.
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 to use this tool before calling get_data, providing direct guidance on when to invoke it. However, it does not mention any when-not-to-use scenarios or alternatives among siblings.
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, so the description carries the full burden. It discloses that debug mode reveals host-sensitive info (cache path and keys), and the default mode checks health without sensitivity. This is good transparency for a diagnostic 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 consists of two concise sentences that front-load the primary purpose and then add the debug detail. There is no redundant information, and every word contributes to 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?
Given the tool's simplicity (one parameter, no output schema), the description is adequate but could be more complete. It does not specify what the output looks like or provide more detailed usage scenarios beyond 'debugging'. A score of 3 reflects this gap.
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 schema description covers the debug parameter fully, but the tool description adds the 'host-sensitive' warning, which provides valuable semantic context beyond the schema. This justifies a score above the baseline of 3.
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 'Check' and the resource 'persistent-cache health' with specific attributes (exists, size, writability). It distinguishes the tool from siblings like discover_dataflows and get_concepts, which focus on different data aspects.
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 for debugging cache issues with 'For debugging.' but does not provide explicit when-not-to-use scenarios or alternatives. However, siblings do not overlap directly, so ambiguity is minimal.
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 provided, the description must carry the full burden. It discloses caching behavior and that it searches all TNSO concept schemes. Missing details like error handling, return format, or what happens if concept_id is not found, but the core behavior is clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that efficiently convey purpose, scope, and a usage hint. No unnecessary words, and the key information is 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?
The tool has no output schema, and the description implies the return is the resolved name. For a simple lookup, this is fairly complete, but mentioning the return type (string) or possible error responses would improve completeness.
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 schema describes concept_id with an example but lacks description for lang. The description adds 'Use lang='th' or lang='en'.', which clarifies the lang parameter's enum values and usage. This adds value beyond the schema, especially for the undocumented lang 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's purpose: resolving a TNSO SDMX concept id to its Thai or English name. It specifies the scope (all TNSO concept schemes) and distinguishes from sibling tools like get_codelist_description, which deals with codelists.
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 clear context on when to use the tool (for concept resolution) and includes a usage hint for the language parameter. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.
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, so the description bears the full burden. It discloses that it returns dimension values with Thai/English labels, time range, and explains the Buddhist Era conversion. It could mention whether the operation is read-only or idempotent, but overall good disclosure.
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 that are front-loaded and concise: first states what it does, second adds usage guidance and a conversion note. No wasted 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?
Given no output schema, the description adequately explains the return value (dimension values, labels, time range) and provides a conversion note. It does not detail the output structure but is sufficient for an agent to understand its purpose. Slight improvement could be made by hinting at the output format.
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 a parameter description and example. The description adds context about the output but does not further explain the dataflow_id parameter beyond what the schema provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets 'all valid dimension values (with Thai/English labels) and the available time range for a dataflow', specifying verb and resource. It distinguishes from siblings like get_data (which retrieves actual data) and discover_dataflows (which lists dataflows).
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 tool is used to retrieve constraints needed to build a get_data query, providing clear context. However, it does not explicitly state when not to use it or mention alternatives, though the context 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 exist, so description carries full burden. It mentions discover and optional filtering but does not disclose whether the tool is read-only, has side effects, or any rate limits. While a discovery tool is likely safe, the omission leaves 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?
Two concise sentences front-load the purpose and action, with zero redundancy. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one optional parameter and no output schema, the description is mostly complete. It covers purpose, filtering behavior, and next step. However, it could explicitly state the return format (e.g., 'returns a list of dataflows with id, name, description') for full clarity.
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%, baseline 3. The description adds value by explaining that keywords match against id, Thai/English name, and description—information not in the schema, which only gives examples.
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 clearly states it discovers TNSO dataflows with specific verb 'discover' and resource 'dataflows'. It further distinguishes itself from siblings by advising to 'start here' and then call get_constraints, showing unique positioning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance ('Start here to find a dataset') and next-step instruction ('then call get_constraints'), effectively differentiating from sibling tools without saying 'not for'.
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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