nutrients_for_foodConvenience: nutrient values only for a food.
| Name | Required | Description | Default |
|---|---|---|---|
| fdc_id | Yes | ||
| nutrient_numbers | No | Comma-sep nutrient numbers (e.g. "208,205") |
| Name | Required | Description | Default |
|---|---|---|---|
| fdc_id | No | FDC food identifier | |
| nutrients | No | Array of nutrient values | |
| description | No | Food description |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description's claim of being convenience-oriented aligns. However, it does not add further behavioral details beyond what annotations provide, such as error handling or response format.
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 a single sentence, which is concise but too brief to cover necessary details. It is not verbose, but it sacrifices clarity and completeness for brevity.
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 the presence of an output schema the description could be minimal, but it fails to explain that fdc_id is a food code and that nutrient_numbers filters results. The description is incomplete for a tool with two parameters and a specific output.
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?
With 50% schema description coverage (only nutrient_numbers has a description), the tool description adds no parameter explanations. The fdc_id parameter lacks any meaning in both schema and description, which is insufficient for an agent to understand its role without prior knowledge.
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 'nutrient values only for a food' clearly states the tool retrieves nutrient data for a specific food. It distinguishes it from sibling tools like 'get_food' which likely returns full food details. However, it could be more explicit about the scope.
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?
The description provides no guidance on when to use this tool versus alternatives like 'get_food' or 'search_foods'. It does not specify prerequisites or when not to use it, leaving the agent to infer usage solely from the name.
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.