Market Index MCP

by tamappe

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: compare_symbols for multi-symbol comparison, get_best_days/worst_days for ranking, get_price_on_date/range for price data, get_yearly_summary for annual overview, and list_symbols for available symbols. The descriptions reinforce these distinct roles, eliminating any ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with clear, descriptive naming (e.g., compare_symbols, get_price_on_date, list_symbols). The verbs are appropriately chosen for their actions (compare, get, list), and the nouns specify the target data, making the set highly predictable and readable.
Tool Count5/5
With 7 tools, this server is well-scoped for market index analysis, covering key operations like data retrieval, comparison, ranking, and listing. Each tool earns its place without redundancy, and the count aligns with typical MCP server ranges (3-15 tools), avoiding bloat or thinness.
Completeness4/5
The tool set provides strong coverage for market index data analysis, including retrieval (price, summary), comparison, ranking, and listing. A minor gap exists in update or management operations (e.g., adding custom symbols), but this is reasonable for a read-only data server, and agents can work effectively with the provided tools.
Average 3/5 across 7 of 7 tools scored. Lowest: 2.4/5.
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
- Last stable release on December 2, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only lists parameters and doesn't disclose any behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or what the summary includes. It fails to provide essential context for safe and effective use.
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 concise with a clear title-like statement followed by parameter details in a structured format. It avoids unnecessary words, though it could be more front-loaded by explicitly stating the tool's purpose upfront. Overall, it's efficient but not perfectly optimized.
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?
Given the complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the yearly summary contains, how results are formatted, or any behavioral aspects. For a tool with no structured support, this leaves significant gaps in understanding its functionality and use.
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 description adds minimal semantics by providing an example for the 'year' parameter ('例: 2020'), which clarifies the expected format. However, with 0% schema description coverage and 2 parameters, it doesn't fully compensate by explaining the 'symbol' parameter meaning or constraints. The baseline is 3 since the schema lacks descriptions, but the description only partially addresses this gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states '指定年の年間サマリー' (yearly summary for specified year), which indicates the tool retrieves summary data for a given year. However, it doesn't specify what type of summary (financial, performance, etc.) or what resource it operates on, and it doesn't differentiate from siblings like get_price_range or get_best_days. The purpose is vague but not tautological.
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 guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, exclusions, or compare to sibling tools like get_price_range or get_best_days. It only lists parameters without context for usage.
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 carries full burden for behavioral disclosure. It mentions the tool retrieves a ranking but doesn't describe what 'best rising days' means (e.g., based on percentage gain, absolute price increase), how results are sorted, whether data is real-time or historical, or any rate limits/authentication needs. This leaves significant gaps for a tool that likely queries financial data.
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 efficiently structured with a purpose statement followed by an 'Args:' section listing parameters. Each line adds value, though the purpose statement could be slightly more specific (e.g., 'Retrieve top N days with highest price increases for a stock in a given year').
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?
For a tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the output format (e.g., list of dates with metrics), what 'best' criteria are used, data sources, or error conditions. This is inadequate given the complexity of financial ranking queries.
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 0%, so the description must compensate. It lists all three parameters with brief explanations: 'symbol: 銘柄コード' (stock code), 'year: 年 (例: 2020)' (year, e.g., 2020), and 'limit: 取得件数 (デフォルト5)' (number to retrieve, default 5). This adds basic meaning beyond the schema's titles but lacks details like symbol format, year range validity, or limit constraints.
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's purpose: '指定年の上昇日ベストランキング' (Best rising days ranking for a specified year). It specifies the verb ('get ranking') and resource ('rising days'), though it doesn't explicitly differentiate from sibling tools like 'get_worst_days' or 'get_yearly_summary' beyond the 'best' vs 'worst' distinction in names.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_worst_days' for opposite rankings, 'get_yearly_summary' for broader annual data, or 'get_price_range' for price history, leaving the agent to infer usage from tool names alone.
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 carries full burden for behavioral disclosure. While 'compare' implies a read operation, it doesn't specify what gets compared (prices? returns? volatility?), the comparison format, or any limitations like rate limits or data availability constraints.
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 appropriately sized with a clear purpose statement followed by parameter explanations. The bilingual approach (Japanese purpose, English parameter details) is efficient, though the structure could be more front-loaded with the most critical 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?
For a 3-parameter tool with no annotations and no output schema, the description provides adequate parameter semantics but lacks crucial behavioral context. It doesn't explain what comparison results look like, what metrics are compared, or any constraints on date ranges or symbol counts.
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 adds significant value beyond the input schema, which has 0% description coverage. It explains that 'symbols' are stock codes with examples, and clarifies that start_date and end_date use YYYY-MM-DD format. This compensates well for the schema's lack of documentation.
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's purpose as '複数銘柄を比較' (compare multiple symbols), which is a specific verb+resource combination. However, it doesn't distinguish this tool from sibling tools like get_price_range or get_yearly_summary, which also involve symbol data analysis.
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?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this comparison tool is appropriate versus using individual price tools like get_price_range or summary tools like get_yearly_summary for similar data.
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 carries the full burden of behavioral disclosure. While it states the tool retrieves price data, it doesn't describe what the return value looks like (e.g., open/close/high/low prices), error conditions (e.g., invalid date or symbol), rate limits, or authentication needs. For a read operation with zero annotation coverage, this is a significant gap in transparency.
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 appropriately concise with two sentences: a purpose statement followed by parameter documentation. It's front-loaded with the core functionality. However, the Japanese-to-English mix might cause minor parsing issues, and the structure could be slightly improved by integrating the parameter details more seamlessly.
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 moderate complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameters well but lacks output format details, error handling, and differentiation from siblings. For a data retrieval tool, the absence of output information is a notable gap, though the parameter documentation helps compensate partially.
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 explicitly documents both parameters in the 'Args' section: 'symbol: 銘柄コード' (stock code) and 'date: 日付 (YYYY-MM-DD)' (date in YYYY-MM-DD format). With 0% schema description coverage, this fully compensates by providing clear semantic meaning beyond the bare schema. The date format specification is particularly valuable.
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's purpose: '特定日の価格データを取得' (Get price data for a specific date). It specifies the verb ('取得' - get) and resource ('価格データ' - price data) with temporal scope ('特定日' - specific date). However, it doesn't explicitly differentiate from siblings like get_price_range or get_yearly_summary, which also retrieve price data but with different temporal scopes.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like get_price_range (for date ranges) or get_yearly_summary (for annual data), nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and description alone.
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 carries the full burden of behavioral disclosure. It only states what the tool does ('get price range') without explaining behavioral traits such as whether it's read-only, requires authentication, has rate limits, returns specific data formats, or handles errors. For a tool with no annotations, this is a significant gap in transparency.
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 appropriately sized and front-loaded: the first line states the purpose clearly, followed by a structured 'Args' section with parameter details. There's no wasted text, and the information is organized efficiently. However, the use of Japanese might reduce accessibility in some contexts, slightly affecting structure.
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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameters but lacks context on behavior, output format, error handling, or sibling differentiation. Without annotations or output schema, the description should do more to compensate, but it meets a basic threshold.
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 adds meaningful semantics beyond the input schema. The schema has 0% description coverage, providing only titles and types. The description explains each parameter: 'symbol' as a ticker code with examples (^GSPC, ^NDX), 'start_date' and 'end_date' as dates in YYYY-MM-DD format. This compensates well for the low schema coverage, though it doesn't cover all potential nuances like date validation.
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's purpose: '指定銘柄・期間の価格範囲を取得' (Get price range for specified symbol and period). It specifies the verb '取得' (get) and resource '価格範囲' (price range), making the purpose understandable. However, it doesn't explicitly differentiate from siblings like 'get_price_on_date' or 'get_yearly_summary', which prevents a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_price_on_date' (for single dates) or 'get_yearly_summary' (for annual data), nor does it specify use cases or exclusions. The agent must infer usage from the tool name and parameters alone.
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 carries full burden for behavioral disclosure. It implies a read-only operation by retrieving ranking data, but doesn't specify if it requires authentication, has rate limits, returns paginated results, or details the output format. The description adds minimal behavioral context beyond the basic purpose.
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 appropriately concise with a clear purpose statement followed by parameter explanations. Each sentence adds value: the first states what the tool does, and the Args section explains parameter meanings. The structure is logical and front-loaded with the main 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?
For a tool with 3 parameters, no annotations, and no output schema, the description provides adequate basic information about purpose and parameters but lacks important context about output format, error conditions, and when to use versus sibling tools. It's minimally viable but leaves gaps in behavioral and strategic guidance.
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 provides clear semantic meaning for all three parameters: 'symbol' as stock code, 'year' as year with example, and 'limit' as number of items to retrieve with default value. With 0% schema description coverage, the description fully compensates by explaining what each parameter represents, though it doesn't provide format constraints or validation rules.
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's purpose: '指定年の下落日ワーストランキング' (worst declining days ranking for a specified year). It specifies the verb 'get' and resource 'worst days ranking', though it doesn't explicitly distinguish from siblings like 'get_best_days' beyond the 'worst' vs 'best' distinction in the name.
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?
The description provides no guidance on when to use this tool versus alternatives like 'get_best_days' or 'get_yearly_summary'. It lacks context about use cases, prerequisites, or exclusions, offering only basic parameter information without strategic direction.
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 carries full burden but offers minimal behavioral insight. It implies a read operation ('取得' - get) but doesn't disclose rate limits, authentication needs, pagination, sorting, or what 'available' means (e.g., active symbols only). For a tool with zero annotation coverage, this is insufficient.
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 in Japanese with zero waste. It's front-loaded and appropriately sized for a simple list operation, earning full marks for conciseness.
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?
Given the tool's simplicity (0 params, no output schema) and lack of annotations, the description is incomplete. It doesn't explain return values (e.g., format, fields), behavioral constraints, or how it fits with siblings. For a list tool in a financial context, more context on scope and output is needed.
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 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't add parameter details (none needed), but it implies no filtering is required, which aligns with the empty schema. Baseline is 4 for zero parameters.
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 '利用可能な銘柄一覧を取得' (Get available symbol list) clearly states the tool's purpose with a specific verb ('取得' - get) and resource ('銘柄一覧' - symbol list). It distinguishes from siblings like get_price_on_date or get_yearly_summary by focusing on listing rather than price analysis. However, it doesn't explicitly differentiate from compare_symbols which might also involve symbols.
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 guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions. For example, it doesn't clarify if this should be used before price-related tools or instead of compare_symbols for basic symbol discovery.
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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