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nhanngo-fastboy

MCP Gold Price Server

by nhanngo-fastboy
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools are clearly distinct: get_gold_prices provides multiple market prices, get_gold_price targets a single code, and get_gold_price_history covers historical data. While the plural vs. singular names are close, the descriptions make the purpose of each unambiguous.

    Naming Consistency5/5

    All tool names follow the get_gold_* prefix with a consistent pattern: plural for bulk, singular for single, and 'history' for historical. The naming is logical and predictable across the set.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose. Each tool covers a core need (current bulk, current single, historical), and the count is within the ideal range for a focused domain.

    Completeness4/5

    The server covers the essential gold price queries: current prices for multiple types, individual prices, and historical data. The only minor gap is the lack of a tool to discover all available gold codes, but common codes are listed in the descriptions, so agents can work around this.

  • Average 4.1/5 across 3 of 3 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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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden. It discloses the return format (daily buy/sell prices and day-over-day changes), but does not mention rate limits, timezone, data freshness, or explicitly confirm a read-only side-effect profile. The read-only nature is implied by 'Get'.

    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?

    Three front-loaded sentences: action, return details, and examples. No redundant or filler content; every sentence contributes to understanding.

    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?

    With no output schema, the description adequately explains return contents (buy/sell prices, day-over-day changes) and provides common codes. It does not cover details like date handling or error scenarios, but given the low complexity (2 simple params), it is largely complete.

    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?

    Schema coverage is 100%, providing baseline 3. The description adds value by mapping common code examples to human-readable names (e.g., XAUUSD = World Gold, SJL1L10 = SJC 9999), enriching the code parameter semantics beyond the raw schema list.

    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 historical gold prices), the resource (gold type), and the time scope (over a number of days). It distinguishes itself from sibling tools (get_gold_prices, get_gold_price) by emphasizing 'historical' and 'day-over-day changes'.

    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 term 'historical' implies use for past data, and sibling names suggest current-price tools, but no explicit when-to-use or when-not-to-use guidance is given. No alternatives are mentioned.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden. It discloses the return values (buy/sell prices, changes) and default behavior, but does not mention freshness, limitations, or error handling. This is adequate for a simple read operation but not comprehensive.

    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 purpose and uses only four concise sentences. Every sentence adds information about scope, returns, defaults, or filtering, with no redundant text.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a two-parameter read tool without an output schema, the description covers purpose, returns, defaults, and filter options. It lacks explicit details on the response structure, but the mention of buy/sell prices and changes provides sufficient context.

    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 coverage is 100%, so the description adds only marginal value by summarizing the filters. The default code list is repeated, but the schema already documents it well, keeping this at baseline.

    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 'Get current gold prices' with a specific resource (Vietnamese and international markets) and output details. It distinguishes from sibling 'get_gold_price_history' by emphasizing 'current' prices, and from 'get_gold_price' by showing multi-code default behavior.

    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 implies use for current prices, which distinguishes from the history sibling. However, it does not explicitly name alternatives or provide exclusion criteria, so it falls short of a 5.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description is the only source of behavioral info. It uses 'Get', indicating a read-only operation, and lists examples. However, it does not discuss data freshness, error handling, or other potential caveats.

    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?

    Two sentences: the first states the purpose and input, the second lists common codes. Every sentence earns its place; there is no fluff.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple one-parameter tool, the description covers the core purpose and input. It does not explain the return format, but 'current price' conveys the essential output. Given the low complexity, this is fairly complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema covers the 'code' parameter with examples, so the baseline is 3. The description adds value by mapping codes to friendly names (e.g., XAUUSD = World Gold), which helps the agent select the right code.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states a specific action: 'Get the current price for a specific gold type by its code.' This distinguishes it from siblings like get_gold_prices (plural) and get_gold_price_history (history).

    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 implies usage when a specific gold type code is known, and it provides common codes to guide the agent. However, it does not explicitly mention when to use sibling tools, so it misses the top score.

    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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