@harurow/j-quants-doc-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have clearly distinct purposes, but describe_endpoint and fetch_spec_page overlap in retrieving endpoint information. The descriptions clarify the difference (structured details vs. raw specification pages), so ambiguity is limited to this pair.
Naming Consistency5/5
All tool names follow a consistent verb_noun_pattern using snake_case (e.g., get_pattern, search_endpoints, fetch_spec_page). The exception health_check is a standard compound noun but does not disrupt the overall consistency.
Tool Count5/5
With 7 tools, the server is well-scoped for its documentation purpose. Each tool covers a distinct function (health, patterns, search, details, raw pages, migration, general info) without redundancy or excessive specialization.
Completeness4/5
The toolset provides comprehensive coverage for exploring and using J-Quants API documentation: search, detail, raw specs, patterns, migration, and general questions. A minor gap is the lack of an explicit 'list all endpoints' function, but search_endpoints may handle this implicitly.
Average 3.8/5 across 7 of 7 tools scored. Lowest: 3.1/5.
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
- 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.
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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?
No annotations are present, so the description carries the burden of behavioral disclosure. It only states it 'gets' information, implying read-only, but provides no details on output format, behavior for invalid endpoints, or any side effects.
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 a single concise sentence with no redundancy. It is efficiently front-loaded with the essential purpose.
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?
The tool has a simple read operation, but the absence of an output schema means the description should clarify what 'detailed information' includes. It does not, leaving some ambiguity in the expected result 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?
The schema provides 100% coverage with descriptions for both parameters, including the api_version enum and examples for endpoint_name. The description adds no parameter semantics beyond the schema, so 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 tool retrieves detailed information about a specified endpoint, using a specific verb and resource. It is distinguishably about describing an endpoint, but does not explicitly differentiate from sibling tools like get_info.
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?
No usage guidance is provided. The description does not explain when to prefer describe_endpoint over search_endpoints, fetch_spec_page, or get_info, and there are no exclusions or alternative references.
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 are provided, so the description must carry the behavioral transparency burden. It only states the search action and does not disclose return format, pagination, side effects, or any constraints. For a search tool, this is limited context.
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 a single, front-loaded sentence that directly states the tool's purpose without any filler or redundancy. It is appropriately sized for the simple functionality.
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?
With no output schema and no annotations, the description lacks information about the response structure or usage nuances like pagination. However, the schema thoroughly documents all parameters, making the tool minimally viable for invocation. The description could be more complete, but it covers the core purpose.
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% for all three parameters, including keyword, category, and api_version with an enum and default value. The description adds no extra parameter semantics beyond what the schema already provides, so the 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 'エンドポイントをキーワードとカテゴリで検索する' (Search endpoints by keyword and category), specifying the action (search), target (endpoints), and scope (keyword and category). This distinguishes it from sibling tools like describe_endpoint or fetch_spec_page.
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 finding endpoints, but provides no explicit guidance on when to use this tool vs alternatives, nor any exclusions or prerequisites. It is a straightforward description without usage context.
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?
There are no annotations, so the description must carry the full burden of behavioral disclosure. It fails to state whether the tool is read-only, safe, or what side effects (if any) exist. Given it is a health check, the lack of explicit safety or non-mutating assurance is a significant gap.
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, consisting of two short sentences in Japanese. It front-loads the tool's identity ('simple tool for health check') and states its purpose without any fluff or 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 zero parameters and no output schema, the description is minimal but adequate. However, it does not explain what the agent should expect as a result (e.g., success criteria, response format), leaving some ambiguity about what 'normal' means. It is not completely inadequate but has clear gaps.
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 tool has zero parameters, which the schema confirms. Per rubric, zero parameters baseline is 4. The description adds no parameter information, but none is needed since the schema is empty and coverage is vacuously high.
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 a specific verb and resource: 'confirms whether the server is operating normally' (サーバが正常に動作しているかを確認する). This uniquely identifies the tool as a health check, distinguishing it from sibling tools focused on patterns, endpoints, specs, and migration.
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 by naming it a health check tool, but it does not explicitly state when to use it or when to prefer an alternative. No exclusions or sibling comparisons are provided, leaving the agent to infer the use case.
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 carries the full burden. It discloses that the tool provides a guide and directs users to the V2 spec page, implying an informational, non-mutating operation. However, it does not describe output format, error handling, or any side effects.
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 purpose and follow with supporting detail. No redundant or unnecessary information.
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?
The tool has one optional parameter and no output schema. The description clarifies purpose and usage but does not describe the return value structure or any edge cases, which is a gap given the absence of an output schema. Still, it provides sufficient context for a simple guide tool.
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 fully describes the single optional parameter v1_endpoint with examples and behavior when omitted (100% coverage). The description adds no additional parameter information, so the schema does the heavy lifting, warranting the baseline 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 the tool's function: providing a migration guide from V1 to V2 API, including endpoint correspondence and changes. It uses a specific verb ('提供する') and resource, distinguishing it from sibling tools like describe_endpoint or fetch_spec_page.
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 specifies when to use this tool: when users of V1 API are migrating to V2 API. It does not mention alternatives or exclusions, but the 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, so the description carries the full burden. It discloses the tool's capability to fetch both endpoint detail pages and reference data pages, which is useful context. However, it does not mention side effects, authentication needs, rate limits, or the return format. For a read-only fetch tool, this is acceptable but not exemplary.
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 long and front-loaded with the primary action. It conveys everything necessary without any redundant or vague wording. The structure is clean and efficient.
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 tool's simplicity (single parameter, no output schema, no annotations), the description is mostly complete. It explains the core function, the breadth of pages it can fetch, and a concrete use case. It could be slightly more complete by noting what happens for invalid paths or the return format, but these are minor gaps for a straightforward fetch tool.
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%; the sole 'path' parameter is well-documented with a type and an example. The description does not add meaningful extra information about the parameter beyond what the schema already provides, so the baseline score 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 the tool fetches a Specification page at a given path, using a specific verb (取得する) and resource (ページ). It further distinguishes itself from siblings by mentioning it can also fetch reference data pages (e.g., holiday categories, market codes) and is intended for traversing links in Markdown, which sets it apart from tools like describe_endpoint.
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 tells when to use this tool: 'Markdown内のリンクを辿って追加情報を取得する際に使用してください' (use when following links in Markdown to fetch additional information). It implies a clear scenario but does not explicitly mention alternatives or when not to use it, so it stops short of a 5.
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 carries the full burden of behavioral disclosure. It discloses the key conditional behavior (differing output based on whether pattern_name is supplied), but it does not mention error handling, output format details, or side effects. For a read-only retrieval tool, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no filler. Each clause conveys essential information: what is retrieved and the conditional list behavior. It is front-loaded and perfectly sized.
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 optional parameter and no output schema, the description covers the core behavior and usage scenarios. It lacks details on the contents of pattern information or error behavior, but given the low complexity and full schema coverage, it is sufficiently 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 description coverage is 100%, so the baseline is 3. The description largely mirrors the schema's parameter description (if not specified, returns all patterns) without adding new syntax or format details. It does not meaningfully enrich the parameter semantics 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 uses a specific verb (取得する/retrieve) and resource (実装パターン情報/implementation pattern information), clearly stating what the tool does. It also mentions the optional parameter behavior, which helps distinguish it from sibling tools focused on health checks, endpoints, spec pages, and migration.
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 two usage modes: omitting pattern_name returns all patterns, while specifying it returns a specific pattern. This provides clear context on when to use each mode, though it does not explicitly name alternative tools or list exclusions.
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 burden. It discloses the tool's scope (answers questions outside API spec) and lists covered topics, but does not mention response format, limitations, error behavior, or any side effects. For a simple Q&A tool, this is adequate but not rich.
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 a single sentence that front-loads the core purpose and then lists examples with separators. It is efficient and structured, though the enumerated list is slightly long. 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 the tool's low complexity (1 parameter, no output schema), the description adequately covers purpose, scope, and when to use. It lacks an explicit statement about the return value format, but the verb '回答する' implies a natural-language answer, making it sufficient for the agent.
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 provides a description for 'query' with examples (e.g., 'Premiumプランの料金は？'). The tool description adds semantic context by enumerating categories like plans, pricing, contracts, and support, helping the agent form appropriate queries beyond the schema's generic 'ユーザーの質問内容'.
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 'API仕様書の範囲外の質問に回答するためのツール', using a specific verb (回答する) and resource (questions outside API spec). It distinguishes from sibling tools that handle API spec operations (search_endpoints, describe_endpoint, etc.) by clearly delimiting its scope.
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?
It provides clear when-to-use guidance by listing specific categories (pricing, contracts, login, support) and says '使用してください' (use when receiving such questions). It implies that within-scope questions should go to other tools, though it does not explicitly name alternatives.
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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