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fastmcp-me

ANSES Ciqual MCP Server

by fastmcp-me

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly stated as executing SQL queries on the food composition database.

    Naming Consistency5/5

    The sole tool is named 'query', a simple and unambiguous name. While there is no pattern to compare, the lack of multiple tools means there is no inconsistency in naming conventions.

    Tool Count3/5

    The server exposes only one tool, which feels thin for the broad scope of a food composition database. Although the tool is powerful and can execute arbitrary SQL, a single tool places a heavy burden on the agent and lacks dedicated conveniences.

    Completeness5/5

    The generic SQL query tool can access all tables, perform joins, filters, and full-text searches, covering every potential query needed. The provided schema, examples, and nutrient codes ensure agents have enough information to retrieve any data, making the surface functionally complete.

  • Average 4.7/5 across 1 of 1 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.

  • This repository includes a README.md file.

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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It explains FTS behavior (OR between words), how compound dishes are handled (not available as recipes), and includes a stop condition to avoid excessive searching. However, it does not explicitly state whether the tool is read-only or how errors are handled, but the read-only nature is strongly implied by the focus on SELECT queries.

    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 lengthy but well-structured with clear headings, emojis, and code blocks. Every section provides practical value—workflow steps, nutrient codes, schema documentation. While not minimal, the length is justified by the complexity of the domain; however, a few lines could be condensed without losing essential information.

    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?

    The description is comprehensive for a SQL query tool: it provides the database schema, key nutrient codes, example queries, and guidance on FTS behavior and compound dishes. With an output schema present, the description does not need to explain return values, but it still gives enough context to use the tool effectively without prior knowledge.

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

    Parameters5/5

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

    Schema description coverage is 0%, with only the 'sql' parameter defined as a string. The description compensates by providing a complete schema, example queries, and nutrient codes, effectively explaining exactly what the sql parameter should contain and how to use it. This exceeds the baseline expectation for low schema coverage.

    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's function: 'Execute SQL query on ANSES Ciqual French food composition database.' It specifies the resource (ANSES Ciqual database) and the action (execute SQL query). Even without siblings, the purpose is unambiguous and well-defined.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides a detailed 2-step workflow with example queries, explicitly instructing users to first search, then retrieve nutrients, and to stop after finding a matching food. It also covers compound dishes and quick calorie lookups, giving clear usage context and guidance on how to structure queries efficiently.

    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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