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firaskudsy

cronometer-api-mcp

by firaskudsy

add_food_entry

Log a food item to your Cronometer diary by specifying food ID, measure ID, and gram weight. Automatically assigns to the correct meal based on your account settings.

Instructions

Add a food entry to the Cronometer diary.

Use search_foods to find food_id and measure_id, then get_food_details to confirm serving sizes and gram weights.

Args: food_id: Numeric food ID from search_foods results. measure_id: Measure/unit ID from get_food_details. grams: Weight of the serving in grams. date: Date to log as YYYY-MM-DD (defaults to today). translation_id: Translation ID from search results (usually 0). diary_group: LEAVE THIS AS "auto" unless the user explicitly named a meal. Do NOT infer it yourself from the time -- you do not know the user's timezone or which meals their account has, and guessing "snacks" at 11pm has repeatedly filed food under Morning Snacks. On "auto" the server reads the user's local clock and their own configured meals and picks correctly.

             Only pass a value when the user said one, e.g. "add it to
             lunch". Matching is case-insensitive on a substring of
             the account's real meal names; the error lists them.

The response includes a logged_to block naming the meal it chose, the local time, and the timezone. Tell the user which meal it went to -- that is how a misfiled entry gets caught immediately rather than days later.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
gramsYes
food_idYes
measure_idYes
diary_groupNoauto
translation_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations indicate a write operation (readOnlyHint false). Description adds context: response includes logged_to block, warns about misfiling, and explains diary_group behavior 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?

Description is well-structured and front-loaded, but somewhat lengthy due to detailed parameter docs. Every sentence adds value; conciseness is slightly sacrificed for completeness.

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?

Covers all parameters, response handling, prerequisites, and edge cases (e.g., meal guessing). With output schema present and good annotations, description is fully sufficient for correct agent usage.

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?

With 0% schema coverage, description thoroughly explains all 6 parameters, including defaults, usage, and implications (e.g., diary_group auto behavior, translation_id, date format).

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 'Add a food entry to the Cronometer diary' with a specific verb and resource. Distinguishes from sibling tools like search_foods and remove_food_entry.

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?

Explicitly instructs to use search_foods and get_food_details first. Provides detailed guidance on diary_group, warning against inferring meals and explaining when to override default.

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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