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Server Quality Checklist

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Latest release: v1.0.4
Disambiguation5/5
Each tool has a clearly distinct purpose: batch retrieval, catalog browsing, category listing, single problem retrieval, test listing, text-based search, and keyword search. No two tools overlap in functionality.
Naming Consistency5/5
All tools follow the consistent pattern 'sdamgia_<action>_<object>' or 'sdamgia_<action>_by_<method>'. The verbs are descriptive and maintain a uniform style.
Tool Count5/5
With 7 tools, the server covers discovery, search, and retrieval workflows without being overly numerous or sparse. Each tool serves a specific and necessary function for interacting with the problem database.
Completeness4/5
The tool set covers the main use cases: exploring catalog, searching by keyword or text, retrieving problems individually or in batches, and fetching by category or test. A minor gap is the lack of a tool to list all subjects or obtain test IDs, but the core retrieval workflow is fully supported.
Average 4.8/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
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- No high-severity vulnerability alerts
- No code scanning findings
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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?
Annotations already indicate read-only, non-destructive, and idempotent. The description adds details that the tool returns only structure, not problems, and that IDs are subject-specific and may change over time, which are useful beyond annotations.
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 well-structured with sections (PURPOSE, WHEN TO USE, KEY PARAMETERS, etc.), each sentence adds value, and it is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the response format, provides an example workflow, and lists use cases. It is complete for a tool with two parameters and moderate complexity.
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%, but the description explains both parameters: subject (required, with context about its role) and response_format (optional, with default). It adds meaning beyond the enum values in 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 explicitly states the tool retrieves the hierarchical catalog structure for a subject, with verb 'retrieves' and resource 'catalog structure'. It distinguishes from siblings by noting it is a prerequisite for sdamgia_get_category_problems.
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 when-to-use scenarios, such as 'first when exploring a new subject' and 'when you need category IDs for other tools'. It lacks explicit when-not-to-use statements but effectively implies 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?
Annotations already indicate readOnlyHint and idempotentHint. Description adds that entire batch fails on invalid ID, parallel fetching, and return format details, providing good context beyond annotations.
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?
Well-structured with sections, bullet points, example usage, typical workflow, performance benefits, constraints, error handling. Content is front-loaded and every sentence adds value for a batch tool.
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?
No output schema, but description thoroughly explains return structure, usage, constraints, error handling, performance, and workflow with sibling tools. Complete for the tool's complexity.
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 has 67% description coverage. Description enriches parameters with usage details (numeric strings, max 10, must be same subject) and explains return structure (problems with condition, solution, etc.) not in 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 it retrieves multiple complete problems in a single request, which distinguishes it from sibling tools like sdamgia_get_problem (individual) and search tools.
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' bullet points, mentions alternatives (individual get_problem for fewer, search for finding IDs), and includes constraints (max 10, same subject, batch fails on invalid IDs).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already show readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds significant behavioral context: returns only IDs, not content; valid category required; large categories; cross-categorization; empty array for unused categories; response format metadata.
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 sections like PURPOSE, WHEN TO USE, KEY PARAMETERS, etc. Front-loaded with purpose. However, it is verbose and repeats some information (e.g., 'only returns IDs' mentioned multiple times). Could be more concise but still clear.
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 no output schema, the description fully explains response format (array of IDs with metadata). Covers prerequisites, error conditions, example workflow, and use cases. All necessary information for an agent to use the tool correctly.
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 descriptions are minimal (subject, category_id, limit, response_format). Description adds detailed constraints, usage notes, and examples for each parameter. However, there is a contradiction: description says response_format default is 'json' while schema says 'markdown'. Also, description says limit returns all if unspecified, but schema default is 20. These inconsistencies reduce score.
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 retrieves problem identifiers for a category within a subject. It distinguishes from siblings by noting it only returns IDs, not content, and that prerequisite catalog call is needed.
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 conditions, prerequisites (call sdamgia_get_catalog first), and indicates it is for ID retrieval before full content fetch. Implicitly tells when not to use by stating it returns only IDs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the read-only and idempotent annotations, the description discloses that it returns only problem IDs (not content), explains test composition, limitations on availability, and time limits. No contradiction with annotations.
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?
Well-structured with clear sections and bullet points, but excessively verbose. Many sentences could be condensed without losing 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 no output schema, the description explains the response format (array of IDs, order, count) and covers edge cases (invalid IDs, availability). All aspects needed for an agent to use the tool correctly are addressed.
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?
Adds meaning beyond the schema by explaining the response_format parameter (output format), constraints on test_id (valid, numeric, availability), and the difference between test_id and category IDs. Schema coverage is 67%, and description compensates thoroughly.
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 the tool retrieves all problem identifiers for a specific test variant. Distinguishes from sibling tools, especially sdamgia_get_category_problems, by emphasizing curated test sets vs thematic collections.
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 scenarios and a dedicated 'DISTINCTION FROM CATEGORY QUERIES' section explaining when not to use this tool, with clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, destructiveHint, idempotentHint) already declare non-destructive behavior. The description adds process details (broad search, fuzzy matching), mentions computational intensity, and notes potential lower similarity due to formatting—no contradictions.
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 sections, bullet points, and an example. While thorough, it is not overly verbose; every section adds value, though slight trimming could improve conciseness.
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 no output schema, the description fully explains the return format (matches, similarity, total), similarity score ranges, best practices, notes, and process. It is complete for a complex search tool with 5 parameters.
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 80%, baseline 3. The description adds meaningful guidance: character range for condition_text, recommended threshold ranges, limit bounds, and response_format defaults, exceeding schema details.
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 finds problems by matching condition text using fuzzy similarity algorithms, distinguishing it from sibling tools like sdamgia_search_problems (keyword-based) and sdamgia_get_problem (fetch by ID).
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 'When to use' section lists specific scenarios, and the notes explicitly mention using sdamgia_search_problems for keyword-based searches, providing clear guidance on when to use this tool vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds search behavior details (text matching, relevance ranking, subject optimization) beyond the readOnly annotation, and discloses constraints like minimum query length and result limit.
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 clear sections and front-loaded purpose. Slightly verbose with some repetition (e.g., 'Returns' section could be tightened).
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 and annotations, the description covers all necessary aspects: usage, parameters, behavior, output format, and relationships to siblings. Complete for a search tool.
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 description explains each parameter with constraints and examples, adding value over the schema descriptions. Schema coverage is high (75%+), so a slight deduction for not fully compensating.
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 searches for problems using a text query, with a specific verb and resource. It distinguishes from siblings like sdamgia_search_by_text by noting exact matching.
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 provides explicit 'When to use' scenarios, a follow-up workflow, and mentions an alternative tool for exact matching, giving clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds constraints (numeric ID, existence prerequisite), content expectations (possible missing solutions), and behavior (similar problems included), going well beyond the annotations.
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?
Well-structured with clear headings and front-loaded 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.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Compensates for missing output schema by detailing return object structure, and covers edge cases and metadata; sibling differentiation is clear.
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 100%, but description adds examples, enum context, default explanation, and input example, enhancing understanding.
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 retrieves a complete problem by unique identifier, distinguishing it from sibling tools like batch retrieval 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'when to use' scenarios and an explicit exclusion ('not for searching'), with alternatives implied by sibling names.
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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