list_food_groupsUSDA food group reference (categories).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| note | Yes | Description of food groups source | |
| common_dataTypes | Yes | List of common FDC data types | |
| wweia_categories_url | Yes | URL to WWEIA category reference |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and behavior. The description adds no additional behavioral context, but does not contradict annotations.
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?
Extremely short, which is concise, but it is a noun phrase rather than a complete sentence. Could be structured better with a verb and clearer object.
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?
Given no parameters and existence of output schema, the description is minimally adequate. It does not elaborate on what the reference contains or how to use it, but annotations fill some gaps.
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?
No parameters exist, so schema coverage is 100%. Baseline score of 4 applies as description does not need to add parameter meaning.
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 name 'list_food_groups' clearly implies listing, and description adds 'USDA food group reference (categories)', which specifies the resource. It is clear but lacks an explicit verb in the description, and does not differentiate from siblings like list_foods.
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?
No guidance provided on when to use this tool versus alternatives such as list_foods or search_foods. The description gives no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set mixes USDA food data tools with a large number of unrelated tools (Polymarket betting, AI visibility, npm scanning, etc.), causing significant overlap in purpose. Many tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all perform research/lookups with similar scopes, making it difficult for an agent to select the appropriate tool.
Tool names generally follow a descriptive verb_noun pattern (e.g., list_foods, search_foods), but there is inconsistency in prefixes (ask_pipeworx vs. pipeworx_feedback vs. polymarket_arbitrage) and some names are long and varied. The naming is readable but not highly predictable.
35 tools is excessive for a server ostensibly focused on USDA Food Data Central. Many tools are unrelated to food (e.g., Polymarket, Kalshi, npm scanning, subscription management), making the server feel bloated and unfocused. A typical food data server would have 5-10 tools.
For the USDA FDC domain, the tool surface is complete: it includes list, search, get, and nutrient retrieval. However, the presence of many unrelated tools dilutes the server's focus. The food-specific operations are well-covered, but the overall server lacks coherence.