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lmdat

@lmdat/cafef-financial-mcp

by lmdat
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool retrieves a distinct financial statement (balance sheet, cash flow, income statement) with no overlap. Agents can clearly differentiate based on statement type.

    Naming Consistency5/5

    All tools follow the consistent pattern 'get_<statement_name>', using snake_case and clear verbs. No naming anomalies or mixed conventions.

    Tool Count5/5

    Three tools perfectly match the domain of financial statement retrieval. The count is focused and appropriate without being too few or too many.

    Completeness4/5

    The three core financial statements are covered, which is comprehensive for most analyses. Minor gaps like statement of changes in equity are acceptable for this scope.

  • Average 4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 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.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 exist, so the description carries the full disclosure burden. It reveals that data is normalized and missing periods are detected, but lacks information on authentication, rate limits, or side effects. The description is adequate but not thorough.

    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 two sentences, front-loaded with purpose and followed by details about returned data. It is concise but could benefit from structuring into bullet points for easier scanning.

    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?

    The tool has 3 parameters and no output schema. The description lists returned data items but does not clarify the output format (e.g., array of objects). It also omits authentication or usage constraints, making it incomplete for a multi-period data 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, so the baseline is 3. The description adds context about overall output but does not enhance parameter-specific meaning beyond what the schema already provides.

    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 retrieves income statement data for a Vietnamese stock ticker from the cafef.vn API. It specifies the data items (revenue, gross profit, net profit) and mentions normalization and missing period detection. The purpose is distinct from sibling tools (balance sheet, cash flow).

    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 does not explicitly guide when to use this tool versus alternatives. Sibling names imply differentiation, but no direct instruction or context for selection is provided.

    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?

    No annotations are provided, but the description discloses the data source (cafef.vn), standardization of numbers, detection of missing periods, and that it returns multiple periods. This adds useful behavioral context beyond a bare read tool.

    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, clear, front-loaded paragraph with no unnecessary words. It immediately conveys the tool's purpose and key features.

    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 no annotations or output schema, the description provides sufficient context: source, features (normalization, missing period detection), and basic behavior. Lacks mention of output format or error handling, but adequate for a 3-parameter 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?

    Schema coverage is 100% with descriptions for all three parameters. The description adds context about the tool's output (standard indicators) but does not enhance understanding of individual parameter semantics beyond the schema. Baseline 3 is appropriate.

    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 it retrieves balance sheet data for a Vietnamese stock ticker from cafef.vn, listing standard indicators and features like number normalization and missing period detection. This distinguishes it from sibling tools for cash flow and income statement.

    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 balance sheet data but does not explicitly state when to use this tool versus alternatives. No 'when not to use' or exclusion criteria are provided.

    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, description carries full burden. It explains data source, normalization, missing period detection, and that it returns multiple periods. It does not mention side effects, which is appropriate for a read operation.

    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?

    Description is a single coherent paragraph that front-loads the purpose and then adds details. It could be slightly more structured but is not overly long.

    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 no output schema, description covers key output aspects (returned metrics, multiple periods, normalization, missing period detection). It does not detail exact structure but provides sufficient context for an AI agent to understand the tool's function.

    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 parameter descriptions. The description adds value by explaining that code prefixes differ from other statements, aiding interpretation of results. However, parameter details like pageSize and typeTime are already fully described in 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 clearly states the tool retrieves Cash Flow Statement data for a Vietnamese stock from cafef.vn API, lists specific returned metrics (cash flow from operations, cash equivalents), and distinguishes from siblings by noting different code prefixes.

    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 for cash flow analysis and contrasts with Balance Sheet/Income Statement by mentioning different code prefixes, but does not explicitly state when to use this tool versus alternatives.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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