A
licenseA
qualityB
maintenanceEvidence-based supplement recommendation MCP server covering 17 supplements and 40+ conditions with medication interaction checking and form quality classification.
5
77
MIT
Plant-Based Nutrition Protocols
Server Details
Evidence-based plant-based food-as-medicine protocols for 47 chronic conditions. ACLM-aligned.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- rabyavalla/bonsai-api
- GitHub Stars
- 0
Available Tools
2 toolsget_protocolBRead-onlyInspect
Generate an evidence-based whole-food plant-based protocol for one of 47 chronic conditions. Returns therapeutic foods, daily meal structure, foods to minimize, monitoring markers, and clinical citations.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| condition | Yes | ||
| user_context | No | ||
| duration_weeks | No |
TDQS
B3.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=false. The description adds meaningful behavioral context by enumerating the protocol's output components (therapeutic foods, meal structure, foods to minimize, monitoring markers, clinical citations), which helps the agent anticipate the result. It does not mention error handling but is still valuable beyond annotations.
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 the primary action and followed by a succinct list of return items. Every word earns its place with no redundancy or filler.
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?
The description adequately explains the output components in the absence of an output schema, but it leaves gaps in parameter semantics and usage guidance. The user_context object is not explained, and there is no comparison to query_nutrition_topic. For a moderately complex tool, this is functional but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description provides no elaboration on any of the three parameters. While the condition enum is self-explanatory, user_context is completely opaque, and duration_weeks is only defined by the schema's bounds. The description fails to compensate for the low schema coverage.
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 the tool generates an evidence-based whole-food plant-based protocol for 47 chronic conditions, with a specific verb and resource. It lists the return content, which differentiates it from a general nutrition query, though it does not explicitly reference the sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus query_nutrition_topic, nor does it mention any alternatives or exclusions. The intended use is implied by the description but not stated as a recommendation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_nutrition_topicBRead-onlyInspect
Ask a nutrition or food-as-medicine question. Returns evidence-based answer from the 200-chunk lifestyle medicine knowledge base.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| max_results | No |
TDQS
B3.3/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety is covered. The description adds that answers are evidence-based and drawn from a 200-chunk knowledge base, which provides useful context about the data source but not deep behavioral details like result formatting or limitations.
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 the actionable purpose, and contains no redundant information. Every word earns its place.
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?
For a simple read-only tool with two parameters and no output schema, the description covers the core purpose and data source. However, it omits parameter behavior (especially max_results) and doesn't hint at the answer format or any usage restrictions, making it adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention the 'max_results' parameter or provide parameter-level guidance. The 'query' parameter is inferable from the description, but 'max_results' (including its default of 3 and max of 10) receives no explanation, so the description fails to compensate for the lack of schema descriptions.
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 the tool answers nutrition/food-as-medicine questions and returns evidence-based answers from a specific knowledge base. The verb 'Ask' and resource are specific, but it doesn't explicitly differentiate from the sibling tool 'get_protocol', though the intent is clear.
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 asking nutrition questions, providing a general context. However, it doesn't explicitly state when to use this tool over 'get_protocol' or any exclusions. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
get_protocol - First observed
query_nutrition_topic
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TDQS
A3.6/5.0
Disambiguation5/5
get_protocol is specifically for generating structured protocols for chronic conditions, while query_nutrition_topic handles general nutrition questions. The purposes are clearly distinct with no overlap in expected inputs or outputs.
Naming Consistency5/5
Both tools follow the verb_noun pattern: get_protocol and query_nutrition_topic. The naming is consistent and predictable, making it easy for an agent to infer tool behavior.
Tool Count3/5
With only two tools, the server feels thin for a domain that could include listing conditions, retrieving specific foods, or managing protocols. However, the narrow focus on protocol generation and Q&A makes the count borderline acceptable.
Completeness4/5
The two tools cover the main use cases: generating protocols and answering nutrition questions. A notable gap is the lack of a tool to list the 47 supported conditions, which agents would need to discover without guessing. This is a minor workaround but not a critical failure.