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Symfomany

Recettes MCP Server

by Symfomany

query_comments

Retrieve user comments from a culinary recipes database to analyze feedback, improve dishes, and understand community preferences.

Instructions

Queries the 'comments' collection of the 'recipies' MongoDB database.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:382-385 (registration)
    Registration of the 'query_comments' tool using the @mcp.tool decorator, specifying name and description.
    @mcp.tool(
        name="query_comments",
        description="Queries the 'comments' collection of the 'recipies' MongoDB database.",
    )
  • main.py:386-395 (handler)
    The main handler function for the 'query_comments' tool. It connects to the local MongoDB instance, queries the 'comments' collection in the 'recipies' database with the provided query dictionary (or empty), converts results to JSON-serializable format using _to_jsonable, and returns the list of dictionaries.
    async def query_comments(query: Optional[Dict] = None) -> List[Dict]:
        """Interroge la collection 'comments' de la base de données MongoDB 'recipies'."""
        client = MongoClient('mongodb://localhost:27017/')
        db = client['recipies']
        collection = db['comments']
        query = query or {}
        results = list(collection.find(query))
        client.close()
    
        return [_to_jsonable(doc) for doc in results]
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 of behavioral disclosure. While it indicates this is a query operation (implying read-only), it doesn't specify authentication requirements, rate limits, pagination behavior, error conditions, or what happens with null queries. The description provides minimal behavioral context beyond the basic operation type.

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 - a single sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded with the core functionality and wastes no space on redundant information.

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?

Given the tool has an output schema (which handles return values) and relatively simple functionality (a single query parameter), the description provides the minimum viable information about what the tool does. However, with no annotations and poor parameter documentation, there are significant gaps in understanding how to use this tool effectively in context with its many sibling tools.

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

Parameters2/5

Does 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 for undocumented parameters. The description mentions 'Queries the comments collection' but provides no information about the 'query' parameter's purpose, format, or MongoDB query syntax. This leaves the single parameter essentially undocumented despite the schema coverage gap.

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

Purpose4/5

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

The description clearly states the action ('Queries') and target resource ('comments' collection of 'recipies' MongoDB database), providing a specific verb+resource combination. However, it doesn't distinguish this tool from sibling query tools like query_users or query_ustensils, which follow similar patterns for different collections.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools that query different collections (query_users, query_ustensils) and other search tools (search_by_ingredient, get_recipe_by_index), but the description offers no context about when this specific comments query is appropriate versus those 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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