A
licenseA
qualityA
maintenanceMCP server for Japanese food nutrition data, enabling bilingual JP/EN lookups across konbini, restaurant chains, and grocery brands.
6
93
1
MIT
Japan Food DB
Server Details
Resolve Japanese food names to nutrition facts. All 2,538 foods from Japan's official tables.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- yuki00yossi/toriigate-platform
- GitHub Stars
- 0
- Server Listing
- Japan Food DB
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A3.8/5.0
Tool DescriptionsA
Average 3.8/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: one for searching foods by name, the other for retrieving detailed nutrition facts for a specific food ID. No overlap or ambiguity.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern: search_food and get_nutrition. The naming is predictable and clear.
Tool Count3/5
With only two tools, the server feels minimal for a food database. While it may serve a basic query need, the thin toolset borders on inadequate for broader use.
Completeness3/5
The domain is querying Japanese food nutrition, but search_food already returns nutrition per 100g, making get_nutrition potentially redundant. Missing features like category filtering or recipe lookup are notable gaps.
Available Tools
2 toolsget_nutritionGet nutrition facts by food IDAInspect
Get nutrition facts per 100g for a food ID returned by search_food.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Food ID from search_food, e.g. "chicken-breast" |
Tool Definition Quality
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It states the tool gets nutrition facts per 100g, which is adequate for a simple retrieval, but does not mention potential errors, authentication, or rate limits.
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?
The description is a single, front-loaded sentence with no wasted words, achieving high efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers the unit (per 100g) and source of ID, but omits details on the structure of returned data.
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?
The sole parameter 'id' is described in the schema with an example. Schema coverage is 100%, so the description adds minimal value beyond what the schema already provides.
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 clearly states the verb (get), the resource (nutrition facts per 100g), and the input requirement (food ID from search_food), distinguishing it from the sibling search_food.
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?
The description implies usage after search_food and with a specific food ID. It does not explicitly exclude alternatives, but the context is clear given only one sibling exists.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_foodSearch Japanese foodsAInspect
Search Japanese foods by name (Japanese or romaji-free text). Returns candidates with nutrition per 100g, source, and disclaimer.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 5) | |
| query | Yes | Food name in Japanese, e.g. "鶏むね肉" or "ごはん" |
Tool Definition Quality
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that results include 'nutrition per 100g, source, and disclaimer', which is helpful. However, it does not describe response structure, pagination, or any other behavioral traits, leaving gaps for the agent.
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?
The description is two sentences, front-loaded with purpose, followed by output details. Every sentence is informative and there is no unnecessary text.
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?
Given the tool's simplicity, the description covers the main purpose and output highlights. However, without an output schema, more detail on the return format would be beneficial. The sibling relationship is not addressed, slightly reducing completeness.
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% with descriptions for both parameters. The tool description adds minimal value beyond confirming the 'query' parameter accepts Japanese or romaji text, which is already implied by the schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool searches Japanese foods by name, specifying the verb 'search' and resource 'Japanese foods'. However, it does not differentiate from the sibling 'get_nutrition', missing the chance to clarify when to use this tool vs the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching foods by name but provides no explicit guidance on when to use this tool vs alternatives, nor any exclusions or prerequisites. The sibling 'get_nutrition' is present but not mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- AlicenseAqualityDmaintenanceProvides SQL access to the ANSES Ciqual French food composition database, enabling query of nutritional data for over 3,000 foods with full-text search support.1MIT
- AlicenseAqualityDmaintenanceEnables natural language access to USDA's FoodData Central database with 1M+ foods, supporting search, nutrition facts, food comparison, and daily value calculations.8MIT
- AlicenseBqualityDmaintenanceProvides tools to search Korean food nutrition data from the Ministry of Food and Drug Safety (MFDS) database, enabling natural language queries for food composition details.2125ISC
Try in Browser
Your Connectors
Sign in to create a connector for this server.