M5 Petit Relations
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a distinct purpose: get_relations reads data, update_relation modifies it, and clear_relation_field removes specific fields. There is no overlap in their primary actions, making selection unambiguous.
Naming Consistency4/5All three tools follow a verb_noun pattern (get, update, clear). However, 'get_relations' uses a plural noun while 'update_relation' and 'clear_relation_field' are singular, showing a minor inconsistency.
Tool Count4/5Three tools is a minimal but reasonable set for a simple relationship management server. It covers read, update, and clear operations without unnecessary bloat.
Completeness4/5The tool set covers the core operations for managing relationship data. A notable gap is the lack of a delete-relation operation, but the ability to clear all fields partially compensates, and the domain may not require full CRUD.
Average 4.1/5 across 3 of 3 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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 Apache 2.0.
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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 bears full responsibility for disclosing behavioral traits. It says what the tool does but not what 'clearing' means (e.g., sets to null, removes the field), side effects, or permission requirements. The field value list is useful parameter information but not behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence followed by a clear field list. Every word adds value, and the structure is front-loaded with the action. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with an output schema, the description provides enough to perform the core action, but it lacks usage guidance and behavioral details. Given the presence of sibling tools and the absence of annotations, more context would be needed for fully informed invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description compensates for the 0% schema coverage by enumerating allowed values for the 'field' parameter and indicating that 'target_id' refers to a specific person. However, it does not explain the meaning of each field or the format/type of target_id beyond what the schema provides, so it is not fully complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('clear a specific field for a specific person') and enumerates the valid field values. The verb 'clear' is specific, and the resource (relation field) is unambiguous. It implicitly distinguishes from sibling tools like update_relation and get_relations by focusing on clearing a single field.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the sibling tools (get_relations, update_relation). There is no mention of alternatives or exclusions, leaving the agent to infer usage from the name alone.
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 the disclosure burden. It explicitly states the read-only nature (returns knowledge) and that others' relations are public information, which implies no special access for reading those. It does not mention potential side effects, but none are expected for a getter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences, front-loaded with the primary function, and every clause provides distinct information (own data, return contents, public access to others). No extraneous wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with an output schema, the description covers the essential scope and access rules. It lacks explicit mention of what 'relations' entails or typical use cases, but the output schema likely addresses return structure, making the description sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is already fully self-descriptive. The description adds no parameter-level detail, but none is needed; per the rubric, a baseline of 4 applies for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves relationship data ('自分の関係性データを取得する'), specifies the scope (own and others' public relations), and is distinct from the sibling update/clear tools. The verb '取得' plus the resource and scope make the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the tool name and siblings (get vs update/clear), and the description notes that other characters' relations are public. However, there is no explicit guidance on when to use this tool versus alternatives, such as when to use update_relation or clear_relation_field.
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 explicitly discloses append vs. overwrite behavior for each field (likes/dislikes/important are appended; feeling/closeness/notes overwritten) and enumerates valid target_id values. This goes beyond simple parameter naming, though it does not mention error cases, idempotency, or return values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a compact bulleted list of parameters with concise Japanese explanations. Every line provides necessary information; no filler or redundancy. It is front-loaded with the purpose statement, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter mutation tool with no annotations, the description covers the main behavior well, including append/overwrite distinctions and target_id scope. However, it does not specify what happens if the target does not exist (whether it creates or errors), and omits output/error behavior, though an output schema exists. Overall, nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains every parameter: target_id's allowed values, that likes/dislikes/important are lists that get appended, feeling/closeness/notes are overwritten, and closeness range (0.0–1.0). This adds meaningful semantics absent from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Update relationship information for a specific target' (特定の相手...への関係性情報を更新する), naming the verb (update), resource (relationship info), and target scope. It distinguishes itself from siblings: get_relations (read) and clear_relation_field (field-specific clearing) by focusing on full updates with append/overwrite semantics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clarifies when to use the tool: to update relationship info for self, owner, or other character IDs. It implicitly differentiates from siblings (get_relations is read, clear_relation_field is field clearing) but does not explicitly state exclusions or when to prefer an alternative. Context is clear enough for an agent.
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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