Finance MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping or unclear purposes. dashscope_search, mock_search, and tavily_search all appear to perform internet searches with similar descriptions, creating significant ambiguity. Additionally, execute_code and execute_shell both handle code execution but in different environments, which could confuse agents about when to use each. The finance-specific tools (extract_entities_code, history_calculate) are more distinct but the overall set has poor separation.
Naming Consistency3/5
The naming follows mixed conventions. Most tools use snake_case (crawl_url, dashscope_search, execute_code, etc.), which is good, but there's inconsistency in verb usage and structure. Some names describe actions (crawl_url, execute_code), others describe functions (extract_entities_code, history_calculate), and react_agent uses a different pattern entirely. While readable, there's no consistent verb_noun pattern throughout the set.
Tool Count4/5
With 9 tools, the count is reasonable for a finance server that appears to combine web research, code execution, and financial analysis. However, the presence of three nearly identical search tools (dashscope_search, mock_search, tavily_search) suggests some redundancy that inflates the count unnecessarily. The scope feels slightly bloated rather than tightly focused.
Completeness2/5
For a 'Finance MCP', the tool surface has significant gaps. While it includes some financial entity extraction and A-share stock analysis, there's no coverage of core financial operations like portfolio management, transaction execution, market data retrieval beyond specific stocks, or integration with financial APIs. The tools are more generic (web crawling, code execution, search) than finance-specific, leaving the domain poorly served despite the server name.
Average 2.9/5 across 9 of 9 tools scored. Lowest: 1.7/5.
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 16, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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}
```
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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 states it 'answers user queries' without explaining how it works, what kind of answers it provides, whether it has limitations, rate limits, authentication needs, or what happens when invoked. This is completely inadequate for a tool with no annotation coverage.
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?
The description is extremely concise - a single sentence. However, this conciseness comes at the cost of being under-specified rather than efficient. While it's front-loaded with the core function, the sentence doesn't earn its place by providing necessary context or differentiation from sibling tools.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and competes with multiple query-related sibling tools, the description is completely inadequate. It doesn't explain what makes this tool unique, how it behaves, what it returns, or when to use it. For a tool in this complex context, the description fails to provide essential information.
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 has 100% description coverage with one parameter 'query' described as 'query'. The description doesn't add any meaningful information about this parameter beyond what the schema already provides. Since schema coverage is high, the baseline score of 3 is appropriate - the description neither compensates nor adds value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'A React agent that answers user queries' is tautological - it essentially restates the tool name 'react_agent' with minimal elaboration. While it mentions answering queries, it doesn't specify what type of queries, what domain it operates in, or how it differs from other query-answering tools like dashscope_search or tavily_search among the siblings. The purpose is vague rather than specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. With sibling tools like dashscope_search and tavily_search that also handle queries, there's no indication of what makes this React agent unique or appropriate for certain contexts. No explicit or implied usage scenarios are mentioned.
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. It mentions that results can be printed using `print`, but doesn't disclose critical behavioral traits such as execution environment, safety risks (e.g., code injection), permissions needed, timeouts, or output handling. This leaves significant gaps for a tool that executes arbitrary code.
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 is reasonably concise and front-loaded with the main action. However, it could be more structured by separating usage guidance from behavioral details, and the phrasing 'can be used in scenarios such as' is slightly verbose without adding much clarity.
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 of executing arbitrary code, no annotations, and no output schema, the description is incomplete. It lacks essential context such as execution environment, error handling, security implications, and what the return values or outputs look like beyond printing. This is inadequate for a tool with significant behavioral implications.
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 has 100% description coverage, with the 'code' parameter documented as 'code to be executed'. The description adds minimal value by implying Python code and the use of `print`, but doesn't provide additional syntax, format details, or constraints beyond what the schema already states. Baseline 3 is appropriate given high schema coverage.
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 the tool 'Execute python code' and mentions scenarios like 'analysis or calculation', which provides a general purpose. However, it doesn't clearly distinguish this from sibling tools like 'execute_shell' or 'history_calculate', and the phrasing is somewhat vague rather than specific about what exactly gets executed or how.
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 mentions scenarios such as 'analysis or calculation' but provides no explicit guidance on when to use this tool versus alternatives like 'execute_shell' or 'history_calculate'. There's no mention of prerequisites, exclusions, or specific contexts that would help an agent choose correctly among siblings.
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 mentions 'retrieves and formats web page content,' which implies a read-only operation, but doesn't specify details like rate limits, authentication needs, error handling, formatting specifics, or whether it follows redirects. For a web crawling tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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: 'Web content parsing tool: retrieves and formats web page content based on the provided URL.' It is front-loaded with the tool's purpose and contains no redundant information, making it appropriately sized and easy to parse.
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 of web crawling (e.g., handling different content types, errors, or formatting) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'formats' entails, what content is returned, or any behavioral constraints. For a tool with no structured data beyond a basic input schema, more context is needed to ensure reliable 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 input schema has 100% description coverage, with the 'url' parameter documented as 'the provided URL.' The description adds minimal value beyond this, only implying the URL is used to fetch content. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding beyond what the schema provides.
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: 'retrieves and formats web page content based on the provided URL.' It specifies the verb ('retrieves and formats'), resource ('web page content'), and mechanism ('based on the provided URL'). However, it doesn't explicitly differentiate from sibling tools like 'dashscope_search' or 'tavily_search', which might also retrieve web content through different mechanisms.
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 when to choose 'crawl_url' over sibling tools like 'dashscope_search' or 'tavily_search', nor does it specify prerequisites, exclusions, or optimal use cases. 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. It describes the extraction and code search behavior but lacks details on permissions, rate limits, error handling, or output format (e.g., structure of returned entities). For a tool with no annotations, this is a significant gap in behavioral disclosure.
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 and front-loaded, stating the core purpose in the first sentence. Both sentences earn their place by adding specific entity types and code search details. It could be slightly more structured but avoids redundancy and is efficiently sized.
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 no annotations and no output schema, the description is incomplete. It explains what the tool does but omits critical behavioral aspects like output structure, error conditions, or limitations. For a tool with 1 parameter and no structured support, more context is needed to be fully helpful to an AI agent.
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 description coverage is 100%, with the single parameter 'query' documented as a natural language query about financial entities. The description adds context by specifying the types of financial entities (e.g., stock, bond) and the code search for stocks/ETFs, but doesn't provide additional syntax or format details beyond what the schema implies. Baseline 3 is appropriate given the high schema coverage.
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: extracting financial entities from a query, identifying their types, and searching for codes for stocks/ETFs. It specifies the resource (financial entities) and verb (extract, search, return). However, it doesn't explicitly differentiate from sibling tools like dashscope_search or tavily_search, which might also process queries.
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 prerequisites, exclusions (e.g., when not to use it), or compare it to sibling tools like dashscope_search or tavily_search, which might handle similar queries. Usage is implied only by the purpose statement.
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 the full burden of behavioral disclosure. It mentions retrieving information but lacks details on rate limits, authentication needs, result format, pagination, or error handling. This is inadequate for a search tool that likely has operational constraints.
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 that front-loads the core functionality ('Use search keywords to retrieve relevant information') with no wasted words. It's appropriately sized for a simple tool.
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 search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of information is returned, how results are structured, or any limitations (e.g., number of results, sources). Given the complexity of internet search and lack of structured data, more context is needed.
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 description coverage is 100%, with the single parameter 'query' clearly documented as 'search keyword'. The description adds no additional parameter semantics beyond what the schema 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.
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 with a specific verb ('retrieve') and resource ('relevant information from the internet'), and distinguishes it from siblings like 'crawl_url' or 'mock_search' by focusing on search functionality. However, it doesn't explicitly differentiate from 'dashscope_search' which appears to be another search tool.
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 'dashscope_search' or 'mock_search', nor does it mention any prerequisites or exclusions. It only states the basic function without contextual usage information.
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 provided, the description carries the full burden of behavioral disclosure. It effectively explains key traits: the tool executes shell commands, the starting directory is fixed per invocation, and full paths are needed for multi-step operations. This adds valuable context beyond the basic schema, though it could mention security implications or output handling.
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 front-loaded with the core purpose and examples, followed by important behavioral notes. It uses two sentences efficiently, with no wasted words. However, the first sentence is slightly verbose ('A tool capable of executing shell commands can use...'), and it could be more streamlined without losing clarity.
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 (executing arbitrary shell commands) and lack of annotations or output schema, the description does a fair job. It covers the fixed starting directory and path advice, which are critical for usage. However, it misses details like error handling, security warnings, or output format, leaving gaps for a tool with significant behavioral implications.
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 description coverage is 100%, with the 'command' parameter well-documented in the schema. The description doesn't add specific syntax or format details for the parameter beyond implying it accepts shell commands like 'pwd' or 'cd'. Since the schema already covers the parameter adequately, a baseline score of 3 is appropriate.
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 the tool 'executes shell commands' and provides examples (pwd, cd, ls, scripts), which clarifies its purpose. However, it doesn't distinguish this tool from potential siblings like 'execute_code'—both could involve command execution, leaving the differentiation unclear. The purpose is understandable but lacks sibling-specific context.
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 includes a note about the starting directory being fixed and advises using full paths for multiple operations, which offers some usage context. However, it doesn't explicitly state when to use this tool versus alternatives like 'execute_code' or other siblings, nor does it provide exclusions or prerequisites. Guidance is minimal and not comparative.
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 mentions that the tool retrieves information from the internet but doesn't specify behavioral traits such as rate limits, authentication needs, response format, or error handling. For a search tool with no annotation coverage, this leaves significant gaps in understanding how it operates 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 concise with two sentences that directly address the tool's function and a usage note. It's front-loaded with the core purpose, and the second sentence adds necessary operational guidance without redundancy. However, it could be slightly more structured by explicitly separating purpose from instructions, but overall it's efficient with minimal waste.
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 of a search tool, no annotations, and no output schema, the description is incomplete. It lacks details on what the tool returns (e.g., format, pagination), error conditions, or how it differs from sibling tools. While it states the basic purpose, it doesn't provide enough context for an agent to use it effectively without trial and error, especially compared to other search-related tools in the sibling list.
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 has 100% description coverage, with the 'query' parameter documented as 'search keyword'. The description adds minimal value by restating 'search keywords' but doesn't provide additional semantics like examples, formatting tips, or constraints beyond what the schema already covers. Given the high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate with extra insights.
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: 'retrieve relevant information from the internet' using 'search keywords'. It specifies the verb ('retrieve') and resource ('information from the internet'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'dashscope_search' or 'tavily_search', which appear to be similar 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance by stating 'If you have multiple keywords, please call this tool separately for each one,' which implies a constraint on batching. However, it doesn't explain when to use this tool versus alternatives like 'dashscope_search' or 'tavily_search', nor does it mention any prerequisites or exclusions. The guidance is limited to operational details without broader 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving information from the internet but doesn't cover important behavioral aspects like rate limits, authentication needs, result format, pagination, or error handling. For a search tool with no annotations, this leaves significant gaps in understanding how it behaves.
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 and well-structured: two sentences that directly address purpose and usage guidelines. Every sentence earns its place with no wasted words or redundancy.
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 of an internet search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of information is returned, how results are formatted, whether there are limitations, or how it differs from sibling search tools. The description provides basic usage but lacks completeness for effective tool selection.
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 description coverage is 100%, with the single parameter 'query' documented as 'search keyword'. The description adds no additional parameter semantics beyond what the schema provides (it just repeats 'search keywords'). Since schema coverage is high, 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.
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: 'retrieve relevant information from the internet' using 'search keywords'. It specifies the action (retrieve) and resource (internet information). However, it doesn't explicitly differentiate from sibling tools like 'tavily_search' or 'mock_search', which appear to be similar 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?
The description provides explicit usage guidance: 'If you have multiple keywords, please call this tool separately for each one.' This gives clear instructions on when and how to use the tool versus alternatives (like batching queries). No misleading guidance is present.
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 key behavioral traits: it generates and executes code automatically (users don't need to write code), it's limited to the given data structure, and it returns results. However, it lacks details on potential limitations like rate limits, error handling, or authentication needs. No contradiction with annotations exists since none are provided.
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, starting with the purpose and data structure, followed by usage notes. It uses bullet points for clarity. However, some parts could be more concise (e.g., the data table might be verbose for a description), and the second note repeats information about not writing code.
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 no annotations, no output schema, and a tool that generates/executes code (implying complexity), the description is moderately complete. It covers purpose, data constraints, and usage guidelines, but lacks details on output format, error cases, or performance characteristics. For a code-execution tool, more behavioral context would be helpful.
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 schema already documents both parameters ('code' and 'query') adequately. The description adds some context by specifying 'A-share stock code' and giving query examples, but doesn't provide significant additional meaning beyond what's in the schema (e.g., no format details for 'code' beyond 'A-share', no constraints on 'query' length). Baseline 3 is appropriate when schema does the heavy lifting.
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 analyzes historical A-share stock price data based on a specific data structure and generates/executes code to answer questions about stock performance. It specifies the resource (A-share stocks) and verb (analyze historical data), though it doesn't explicitly distinguish from sibling tools like 'execute_code' or 'dashscope_search' that might also process queries.
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 for when to use this tool: for A-share stocks only (not other markets), based on the specified data structure, and for questions about historical performance. It includes explicit 'do not' guidance (e.g., 'other markets...请不要使用此工具', '请勿提出需要超出该数据范围信息的问题'). However, it doesn't explicitly mention when to use alternatives among the sibling tools.
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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