prepare_log_recognized_foodStage logging an already recognized food name, confidence, and optional serving mass with USDA nutrition data; no image analysis.
Stage logging an already recognized food name, confidence, and serving mass with grounded USDA nutrition. This does not analyze an image.
| Name | Required | Description | Default |
|---|---|---|---|
| scan_id | Yes | ||
| food_name | Yes | ||
| serving_grams | No | ||
| recognition_confidence | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The word 'Stage' adds useful context beyond the annotations by implying this is a preparatory action rather than the final log, and 'grounded USDA nutrition' signals the nutrition source. It does not explain what staging means operationally, whether confirmation is required, or what side effects occur, so with annotations already covering the mutation/safety profile this is only moderate added value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight sentences with the core action front-loaded and no wasted language. The negative scope sentence earns its place by preventing the wrong image-analysis use case.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a non-readonly staging tool with four parameters, no output schema, and 0% schema description coverage, the description is too thin. It omits the required scan_id semantics and does not explain the next step after staging, leaving the agent to infer important lifecycle and invocation details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must carry parameter meaning. It identifies food name, confidence, and serving mass, but leaves the required scan_id unexplained and does not mention constraints such as confidence minimum 0.35 or serving maximum 2000.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb ('Stage logging') and resource ('already recognized food name, confidence, and serving mass'), and explicitly excludes image analysis. It is clear enough to distinguish this from image-based recognition, though it does not name the sibling alternatives it differs from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly frames the tool as intended for already-recognized food data and explicitly says it does not analyze an image, which provides a useful when-not boundary. However, it does not name the alternative analysis tool or explain the confirm/cancel lifecycle after staging.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.