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sherajdev

Twelve Data MCP Server

by sherajdev

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation2/5

    Tools get_price and get_exchange_rate have overlapping functionality for currency pairs, and get_quote also provides current price. This creates ambiguity about which tool to use for simple price queries.

    Naming Consistency5/5

    All tools use a consistent 'verb_noun' pattern with snake_case (get_price, get_quote, get_time_series, convert_currency, get_exchange_rate), making it easy to predict functionality.

    Tool Count5/5

    With 5 tools, the server covers essential financial data operations without being overly cluttered. The count is well-scoped for the domain.

    Completeness4/5

    Covers major needs: real-time price, detailed quote, historical data, currency conversion. Missing advanced features like symbol search or indicators, but core workflows are supported.

  • Average 4.3/5 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
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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

  • Behavior4/5

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

    Annotations already indicate read-only, idempotent behavior. The description adds context about real-time rates and supported asset types (fiat, metals, crypto), which is useful beyond 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 concise, well-structured with sections, and includes examples that are immediately helpful. Every sentence adds value.

    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?

    The description explains the return (converted amount with exchange rate) and provides examples, but lacks explicit structure of the output. Still sufficient for the tool's purpose.

    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%, but the description adds examples and clarifies valid inputs (e.g., XAU, BTC), enhancing understanding beyond the schema alone.

    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 tool converts an amount between currencies using real-time rates, and it distinguishes itself from siblings like get_exchange_rate by including an amount parameter.

    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 for currency conversion but does not explicitly mention when to use it versus alternatives like get_exchange_rate, or any exclusions.

    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?

    Annotations already cover readOnlyHint, idempotentHint, and destructiveHint. The description adds that it is the fastest endpoint and supports multiple asset types, which provides valuable behavioral context beyond annotations.

    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?

    Well-structured with sections and bullet lists. Each sentence adds value, though the examples section could be slightly more compact. Overall no wasted 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?

    Given the tool simplicity and rich annotations, the description adequately covers purpose, parameters, usage examples, and return value. Could mention potential errors or rate limits, but not critical for a read-only, idempotent tool.

    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% (baseline 3), but the description adds numerous real-world examples of symbol formats and usage patterns, which meaningfully guides parameter usage beyond the schema alone.

    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?

    Clearly states it gets the current real-time price for any trading symbol, and distinguishes itself by claiming to be the fastest endpoint for current prices. Examples of supported asset types reinforce the purpose.

    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?

    Explicitly describes when to use it (fastest endpoint for current prices) and provides many example symbols. However, it does not explicitly mention when to consider sibling tools like get_quote or get_time_series.

    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?

    Annotations already declare readOnlyHint, idempotentHint, openWorldHint. Description adds return structure (OHLC candles with volume) and constraints (outputsize 1-5000). No contradiction.

    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?

    Well-structured with sections for Args, Returns, and Examples. Front-loaded with purpose. Every sentence adds value, though Args section slightly redundant with schema.

    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?

    Covers all 6 parameters, output format, and use cases. Given no output schema, the description adequately explains return data. No missing critical information.

    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 covers 100% of parameters with descriptions. The description adds examples and context (e.g., 'Trading symbol', '1-hour gold candles') that enhance understanding beyond schema.

    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 explicitly states 'Get historical OHLC candlestick data' with a specific verb and resource. It distinguishes from siblings like twelvedata_get_price by focusing on historical multi-timeframe data.

    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?

    Clearly states use cases: 'chart analysis, backtesting, and historical price research.' Provides examples to guide selection, but does not explicitly state when to avoid or mention alternative tools.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, establishing safety. The description adds that the tool returns a 'Current exchange rate with timestamp,' which is useful behavioral context beyond the schema. No contradictions with 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 extremely concise, structured with Args, Returns, and Examples sections. Every sentence adds value with no redundancy. The main purpose is front-loaded in the first line.

    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 2-parameter tool with rich annotations and 100% schema coverage, the description is complete. It covers purpose, parameters, return format, and examples. There is no output schema, but the description explains the return value adequately. Slight deduction for lacking error handling or edge cases.

    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% with good descriptions for both parameters. The description adds a returns section and concrete examples (e.g., 'XAU/USD' for gold), which adds value beyond the schema by clarifying usage patterns. This justifies above baseline (3).

    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 the current exchange rate for a currency pair' with a specific verb and resource. It uses examples like 'EUR/USD' and 'XAU/USD' to clarify the resource scope, effectively distinguishing from sibling tools like twelvedata_get_price which likely fetches stock prices.

    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 examples and expected input formats, giving clear context for when to use the tool. However, it does not explicitly state when not to use it or mention alternatives like twelvedata_convert_currency for conversion with amounts, so it narrowly misses a 5.

    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?

    Annotations already declare readOnlyHint=true, so the description doesn't need to reiterate safety. It adds value by detailing the return fields (OHLC, change, volume, 52-week range, market status) and notes the tool is more detailed than get_price. No contradictions or additional behavioral issues mentioned.

    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 concise yet comprehensive: a brief summary, bullet points for args and returns, and two examples. Information is front-loaded and every sentence adds value.

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

    Completeness5/5

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

    Given only 2 parameters, no output schema, and strong annotations, the description fully covers tool behavior, usage, and output. It explains when to use, what parameters are, and what the return data contains, making it easy for an agent to invoke correctly.

    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%, so baseline is 3. The description adds concrete examples for symbol (e.g., 'XAU/USD') and clarifies the meaning of response_format options ('markdown' for human-readable). This extra context merits a 4.

    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 explicitly states 'Get comprehensive quote data including OHLC, change, volume, and 52-week range.' It uses a specific verb and resource, and immediately distinguishes from sibling 'get_price' by noting it is more detailed.

    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 versus alternatives by saying 'More detailed than get_price' and includes example queries that demonstrate proper symbol formatting. It lacks explicit when-not-to-use guidance, but the differentiation is sufficient.

    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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