Calorie MCP

Search foods

search_foods

Read-only

Search a catalogue of over 4 million foods by name and return calories and macros per 100 g. Use this first whenever the user names a food. Returns a food_id for each result, which calculate_portion and get_food_nutrition accept.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_foods"
  }
}

Input Schema

Name	Required	Description
`limit`	No	How many results to return (1-25)
`query`	Yes	Food name, e.g. 'grilled chicken breast'
`verified_only`	No	Only return foods with curated, verified macros. Keep true unless the user asks for broader coverage.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 9, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuinely useful behavior beyond that: the catalogue scale, the per-100 g normalization of results, and the chaining contract ('returns a food_id ... which calculate_portion and get_food_nutrition accept'). It omits match-failure behavior and any ranking/fuzzy-match semantics.

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 sentences, zero filler, with the highest-value fact (what it returns) and the routing instruction front-loaded. Every clause carries information the agent needs.

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?

With an output schema present and annotations covering the safety profile, the description only needs to establish purpose, entry-point precedence, and the id handoff to downstream tools — all of which it does. Nothing required to invoke it correctly is missing.

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 description coverage is 100%, so the schema already documents query, limit, and verified_only including defaults and ranges. The description adds no parameter-level detail beyond confirming results are returned for a name query, so the baseline 3 applies.

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?

States a specific verb and resource ('Search a catalogue of over 4 million foods by name') and the normalized return shape ('calories and macros per 100 g'). It also distinguishes itself from siblings by positioning itself as the entry point and naming the downstream consumers of its food_id output.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use this first whenever the user names a food' gives explicit precedence over siblings like suggest_foods or get_food_nutrition, and names the two tools that accept its food_id. It stops short of stating when NOT to use it (e.g. barcode or photo inputs, which other siblings cover), so a small inference remains.

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

All Calorie MCP tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?