VN Stock API MCP Server

by Long0308

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools have some clear distinctions, such as between price fetching, news retrieval, and API documentation, but there is notable overlap: get_api_documentation_urls and search_vn_stock_api both handle API documentation, and get_api_endpoints is closely related, which could cause confusion for an agent. The Doji analysis tool is distinct but may not integrate well with the others.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., get_stock_price_fireant, list_vn_stocks, search_vn_stock_api), with clear and descriptive names. However, analyze_doji_pattern uses 'analyze' instead of 'get' or 'list', and includes underscores inconsistently with compound terms, slightly deviating from the overall pattern.
Tool Count4/5
With 7 tools, the count is reasonable for a server focused on Vietnam stock market data and API access. It covers multiple aspects like prices, news, symbols, and documentation without being overly bloated, though it could be slightly refined to reduce redundancy in the API documentation tools.
Completeness3/5
The server covers key areas like stock listing, price fetching, news, and API documentation, but there are gaps: it lacks tools for historical data, portfolio management, or trading operations, which are common in stock APIs. The Doji analysis tool feels isolated without complementary technical analysis tools, limiting overall domain coverage.
Average 3.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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 provided, so the description carries the full burden of behavioral disclosure. It describes what the tool does (detects Doji patterns) and lists pattern types, but lacks critical behavioral details: it doesn't specify output format, whether it's read-only or mutative, error handling, rate limits, or authentication needs. 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 sentence states the core purpose, followed by explanatory context. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., separating pattern types into a list). Overall, it's efficient and clear.
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 (technical analysis tool with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of detected patterns with details), error conditions, or practical usage constraints. For a tool with no output schema, the description should compensate by detailing return values, but it doesn't.
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 all parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'threshold' relates to pattern detection in practice). With high schema coverage, the baseline is 3, as the description doesn't compensate with additional semantic value.
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: 'Analyze Doji candlestick patterns in stock price charts.' It specifies the verb ('analyze') and resource ('Doji candlestick patterns in stock price charts'), and distinguishes it from sibling tools by focusing on technical analysis rather than data retrieval or news. However, it doesn't explicitly differentiate from potential similar analysis tools (none in the sibling list), so it's not a perfect 5.
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 mentions that 'Doji patterns indicate market indecision and potential trend reversals,' which implies usage for technical analysis, but offers no explicit when/when-not instructions, prerequisites, or comparisons to sibling tools like get_stock_price_fireant or search_vn_stock_api. This leaves the agent with minimal context for tool selection.
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 returns detailed endpoint information, but it does not describe key behavioral traits such as whether it requires authentication, has rate limits, handles errors, or if the data is static or dynamic. This leaves significant gaps for a tool that interacts with API endpoints.
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, consisting of two clear sentences that state the tool's purpose and return value without unnecessary details. However, it could be slightly more concise by combining ideas, but it efficiently communicates the core 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?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return information but lacks details on behavioral aspects, error handling, or output structure, which are important for API-related tools. This results in a score that reflects partial completeness with notable gaps.
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%, so the schema already documents both parameters ('provider' and 'category') with descriptions and an enum for 'provider'. The description adds no additional meaning or context beyond what the schema provides, such as examples of categories or provider details, resulting in a baseline score of 3.
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 specific verbs ('Get') and resources ('specific API endpoints for a provider'), and it specifies what information is returned ('detailed endpoint information including URLs, methods, and descriptions'). However, it does not explicitly distinguish this tool from sibling tools like 'get_api_documentation_urls' or 'search_vn_stock_api', which might have overlapping or related functionality, preventing a score of 5.
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, such as sibling tools like 'get_api_documentation_urls' or 'search_vn_stock_api'. It lacks explicit context, exclusions, or prerequisites, offering only a basic functional statement without usage instructions.
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 the return content ('links to official API documentation, guides, and GitHub repositories') but doesn't cover critical aspects like whether it's a read-only operation, error handling, rate limits, or authentication needs. For a 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 front-loaded and efficient, using two sentences that directly convey the purpose and return value without unnecessary details. Every sentence earns its place by adding value, making it appropriately sized for the tool's complexity.
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 low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the purpose and return types but lacks details on behavioral traits and usage context. With no output schema, it doesn't explain return values beyond high-level types, leaving room for improvement in completeness.
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 'provider' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the 'all' option or provider specifics. Baseline 3 is appropriate since the 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 action ('Get documentation URLs') and resource ('for API providers'), specifying what the tool does. It distinguishes from siblings like 'get_api_endpoints' by focusing on documentation links rather than operational endpoints. However, it doesn't explicitly differentiate from all siblings, such as news or stock price tools, though the purpose is distinct enough.
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, context for selecting providers, or compare with sibling tools like 'get_api_endpoints' for operational vs. documentation needs. Usage is implied by the purpose but lacks explicit 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 limited behavioral insight. It mentions scraping via Firecrawl API, implying external data fetching, but doesn't disclose rate limits, authentication needs, potential failures, or what 'comprehensive' entails. For a web scraping tool with zero annotation coverage, this leaves significant gaps in understanding operational behavior.
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 three sentences: purpose, scope, and implementation method. It's front-loaded with the core function. While efficient, the third sentence about Firecrawl API could be considered slightly extraneous but still relevant for transparency.
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 for a scraping tool. It doesn't explain return values (e.g., article structure, fields like title/date/content), error handling, or data freshness. For a tool with 3 parameters and behavioral complexity, more context is needed to guide effective 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?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds no additional parameter semantics beyond implying news filtering via 'search' and output formatting via 'format', which are already covered. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
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 verb ('Get'), resource ('latest stock market news from CafeF'), and scope ('comprehensive market news, analysis, and updates from Vietnam's leading financial news website'). It distinguishes itself from siblings by focusing on news scraping rather than stock prices, patterns, or API documentation.
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 explicit guidance on when to use this tool versus alternatives is provided. While it mentions scraping from CafeF, it doesn't specify scenarios where this is preferable over other news sources or sibling tools like get_stock_price_fireant. The description lacks usage context or exclusions.
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 the tool returns 'information about available APIs, endpoints, and documentation URLs', but lacks details on rate limits, authentication needs, error handling, or response format. For a search tool 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 concise and front-loaded in a single sentence, efficiently stating the tool's purpose and return value. Every sentence earns its place, though it could be slightly more structured by separating provider details from 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?
Given the tool's moderate complexity (search with filtering), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks details on behavioral traits, response structure, or error handling, leaving gaps for an AI agent to operate effectively.
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 fully documents both parameters. The description adds marginal value by mentioning 'search query to filter results' and listing providers, but doesn't provide additional syntax, format details, or examples beyond what the schema specifies. Baseline 3 is appropriate when the schema handles parameter 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: 'Search for API documentation and endpoints from VNDirect, FireAnt, or SSI' with the specific verb 'search' and resources 'API documentation and endpoints'. It distinguishes from siblings like 'get_api_documentation_urls' and 'get_api_endpoints' by emphasizing search functionality with filtering, though it doesn't explicitly contrast them.
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 listing providers and mentioning filtering with a query, but it doesn't explicitly state when to use this tool versus alternatives like 'get_api_documentation_urls' or 'get_api_endpoints'. No exclusions or prerequisites are provided, leaving usage context inferred rather than clearly defined.
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 data source (FireAnt API/web scraping) and real-time nature, but doesn't address important behavioral aspects like rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. The description provides some context but leaves significant gaps for a tool that presumably makes external API calls.
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 that each add value. The first sentence states the core purpose, and the second provides implementation details and market context. There's no wasted verbiage, though it could be slightly 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and that presumably makes external API calls, the description is incomplete. It doesn't address response format, error handling, rate limits, or authentication requirements. While it specifies the market focus and data source, important contextual information about how to interpret results or handle failures is missing.
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 fully documents the single parameter. The description adds minimal value beyond the schema by providing example symbols (VIC, VNM, VCB) and mentioning 'Vietnam stock market symbols', but doesn't explain parameter semantics beyond what's in the schema description. This meets the baseline expectation when schema coverage is complete.
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 specific action ('Get real-time stock price'), identifies the resource ('from FireAnt'), and distinguishes from siblings by specifying the data source (FireAnt API/web scraping) and market focus (Vietnam stock market symbols). It goes beyond just restating the name by detailing the implementation method and geographic scope.
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 context by mentioning 'Vietnam stock market symbols' and providing examples, but doesn't explicitly state when to use this tool versus alternatives like 'list_vn_stocks' or 'search_vn_stock_api'. No guidance is given about when-not-to-use or specific prerequisites for operation.
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 states the tool returns a 'comprehensive list' and specifies the exchanges covered, adding useful context. However, it does not disclose key behavioral traits like whether the list is paginated, real-time, cached, or includes metadata beyond symbols, which are important for a listing tool without annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by additional context in a second sentence. It is concise with no wasted words, efficiently conveying essential information (action, resource, scope, and similarity reference) in two sentences that each earn their place.
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 (simple listing with two optional parameters), high schema coverage (100%), and no output schema, the description is mostly complete. It covers the purpose, scope, and similarity to another tool, but lacks details on output format (e.g., structure of the returned list) and behavioral aspects like performance or limitations, which would enhance completeness for a listing 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 has 100% description coverage, clearly documenting both parameters (exchange with enum values and search as optional filter). The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
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 specific action ('List all available Vietnam stock symbols') and resource ('stock symbols traded on Vietnamese stock exchanges'), distinguishing it from siblings like get_stock_price_fireant or search_vn_stock_api by focusing on listing rather than price retrieval or searching. It explicitly mentions the scope (HOSE, HNX, UPCOM exchanges) and references a similar tool (list_assets in coincap-mcp) for context.
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 (to list Vietnam stock symbols) and implicitly distinguishes it from siblings by not overlapping with their purposes (e.g., analyze_doji_pattern for pattern analysis, get_stock_price_fireant for price data). However, it lacks explicit guidance on when not to use it or direct alternatives among siblings, such as search_vn_stock_api for filtered searches.
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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