Skip to main content
Glama
cabird
by cabird

log_food

Log a food to a meal in your diary. Specify portion via serving amount and unit or servings multiplier. Use dry run to preview.

Instructions

Log a food to a meal in the diary. Specify the portion EITHER as serving_amount + serving_unit (e.g. 120 and 'g') OR as servings (a multiplier of the food's default serving). Set dry_run to preview.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDay to log to: 'YYYY-MM-DD', 'today', or 'yesterday'.
mealNoOne of: breakfast, lunch, dinner, snacks.snacks
dry_runNoPreview the result without writing to the diary.
food_idYes32-character hex food ID from search_food.
servingsNoMultiplier of the food's default serving.
serving_unitNoUnit for `serving_amount`, e.g. 'g', 'mL', 'cup', 'oz'.
serving_amountNoQuantity in `serving_unit`, e.g. 120.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

Without annotations, the description carries full burden but only mentions dry_run for preview. It omits behavioral traits like whether logging overwrites existing entries, idempotency, or required permissions for a mutation 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?

Two short sentences, front-loaded with the main action, efficient and focused.

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?

With an output schema present, return values are covered. However, the description lacks context on required parameters, error handling, or state changes, making it moderately complete for a logging 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%, so baseline is 3. The description adds value by clarifying the two portion methods (mutually exclusive) and dry_run usage, but does not provide significant extra detail beyond schema descriptions.

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 states 'Log a food to a meal in the diary' with a clear verb, resource, and destination. It distinguishes from siblings like search_food and describe_food.

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?

No explicit guidance on when to use this tool versus alternatives like log_custom_food. The description only gives port specification options but lacks context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cabird/loseit-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server