mcp-finance
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct resource: price, company info, historical data, news, and market indices. There is no overlap in purpose, making selection unambiguous.
Naming Consistency5/5All tool names follow the consistent 'get_' prefix followed by a clear resource noun (e.g., get_stock_price, get_company_info). The pattern is uniform and predictable.
Tool Count5/5With 5 tools, the server is well-scoped for a finance data provider. Each tool covers a core data category without being bloated or insufficient.
Completeness4/5The server covers the primary financial data needs: current price, company info, historical trends, news, and market indices. Minor gaps exist (e.g., no financial statements or symbol search), but core workflows are well-supported.
Average 3.1/5 across 5 of 5 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
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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the full burden of behavioral disclosure. It only restates that the tool fetches info, without mentioning output format, data sources, potential errors, or the read-only nature beyond what is obvious from the verb.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no filler, making it easy to scan. However, it is so brief that it borders on under-specification, which slightly detracts from the conciseness advantage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and absence of an output schema, the description fails to communicate what 'basic company info' includes or what the response will look like. It also omits parameter semantics, leaving the agent under-informed for a tool that has only one parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one required parameter, 'symbol', with 0% description coverage. The description does not explain what 'symbol' refers to (e.g., ticker format, exchange, case sensitivity) or any constraints, so it adds no meaning beyond the parameter name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Fetch' and names the resource 'company info', which distinguishes it from sibling tools that handle stock price, historical data, news, or indices. However, 'basic company info' is vague about what exactly is included.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives like get_stock_price or get_stock_news. There are no exclusions or contextual hints, leaving the agent to infer usage from the tool name 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, the description must convey behavioral traits. It mentions the external API but omits response format, filtering behavior, rate limits, or error handling. The 'latest' scope is vague and unqualified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It is concise, though under-specification in content is penalized elsewhere.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a 2-parameter tool with no annotations and no output schema, the description is insufficient. It lacks critical context about how the symbol affects results, default behavior, and what the returned news payload contains.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain either parameter. The required `symbol` and optional `count` are completely undocumented, adding no semantic value beyond the schema's types and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches latest news and names the MarketAux API. It distinguishes from siblings like get_stock_price and get_company_info, though it could explicitly mention that news is for a specific stock symbol.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Sibling tools exist but no comparative context is given, leaving the agent without usage criteria.
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 available, the description carries the full burden of behavioral disclosure. It only says 'Fetch historical stock data' which implies a read operation, but doesn't state any limitations, the nature of the returned data (e.g., OHLCV), or whether any rate limits or permissions apply. There is no explicit statement that it is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: a single lead sentence plus a straightforward parameter list. Every sentence earns its place, and the structure front-loads the core purpose before detailing arguments. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description adequately explains the main purpose and all parameters. However, it lacks any mention of what the returned data looks like, any usage constraints, or relationship to sibling tools. Given the minimal complexity, it is borderline adequate but leaves room for more helpful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context beyond the schema by explaining 'symbol' as 'Stock ticker symbol' and 'period' with concrete examples of valid values ('1d, 5d, 1mo, 3mo, 1y'). Since the schema has no descriptions for properties, this fills a critical gap and helps the agent construct valid calls.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches historical stock data, which is a specific verb and resource. It distinguishes from siblings like get_stock_price (current price) and get_company_info by the 'historical' qualifier, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as get_stock_price for current quotes or get_market_indices for broader market data. The description only states what it does, not when it should be preferred.
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 for behavioral disclosure. It only indicates a read operation ('Fetch') and the recency ('latest'), but omits important details like response format, error handling, or data source reliability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It immediately states the action and target, making it easily scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one param, no annotations, no output schema), but the description does not mention return values or edge cases. While the purpose is clear, the missing output details make it only partially complete for an agent deciding on use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It mentions 'symbol' and implies it is a stock ticker, but does not elaborate on format or examples. For a single parameter, this is minimally adequate, though it adds little beyond the schema's title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and resource ('latest stock price') with a clear scope ('for a symbol'). It distinguishes itself from sibling tools like get_historical_data and get_stock_news by focusing on the current price.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when the latest stock price is needed, but provides no explicit guidance on when to choose this tool over alternatives. It does not mention exclusions or name sibling tools, leaving usage context implicit.
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. 'Fetch the latest data' implies a read operation but doesn't disclose what data fields will be returned, whether the data is real-time or delayed, or any rate limits. The description is too thin to convey behavioral traits beyond the basic fetch action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence delivers the essential purpose without any filler. Every word is meaningful, and the description is well-sized for a tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (zero parameters, no output schema), the description adequately names the resource and action. However, it could be improved by listing example indices (e.g., S&P 500, Dow, Nasdaq) or clarifying that it returns current values only. Still, for a simple list tool, the description is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides no properties to explain. The description adds a slight semantic hint by specifying 'major US market indices,' which implicitly defines the scope of the return set. With no parameters, the description doesn't need to compensate, so the baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Fetch the latest data for major US market indices' clearly identifies the action (fetch) and the resource (major US market indices), distinguishing it from sibling tools that target individual stocks, company info, historical data, or news. The scope is specific enough for an agent to select it for index-related queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus the sibling tools (e.g., 'Use for market indices; for individual stocks use get_stock_price'). The description only states what it does, so the agent must infer usage context from the tool name and sibling list, which is insufficient.
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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