A
license-
qualityB
maintenanceEnables querying a local health knowledge base with hybrid RAG (vector + FTS5) and exploring backlinks between notes.
MIT
Lifestyle Medicine Knowledge
Server Details
Semantic search over a 200-chunk ACLM lifestyle medicine knowledge base.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- rabyavalla/bonsai-api
- GitHub Stars
- 0
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Tool Definition Quality
A3.9/5.0
Tool DescriptionsA
Average 3.8/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlapping purposes. The tool's purpose is clearly and distinctly stated.
Naming Consistency5/5
The single tool name follows a clear and readable pattern (noun_verb-like). With only one tool, consistency is inherently maintained.
Tool Count2/5
The server claims to cover a vast knowledge base spanning multiple lifestyle medicine pillars, yet it provides only one tool. This is too few for the apparent scope, making the tool count inappropriate.
Completeness5/5
The single tool appears to fully cover the server's stated purpose of answering lifestyle medicine questions with citations. There are no obvious gaps in the tool surface for a query-only domain.
Available Tools
1 toollifestyle_queryARead-onlyInspect
Ask any lifestyle medicine question. Returns evidence-based answer with citations from a 200-chunk knowledge base spanning ACLM 6-pillars, B.O.N.S.A.I. nutrition, drug-food interactions, lab interpretation, GLP guidance, wearables, and CGM signal interpretation.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| pillar | No | any | |
| max_results | No |
Tool Definition Quality
A3.8/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds meaningful context beyond those hints by specifying the knowledge base size ('200-chunk'), the type of output ('evidence-based answer with citations'), and the broad content areas. This helps set expectations about the tool's bounded scope and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and every phrase adds value. It packs a lot of useful information (knowledge base size, content domains, output format) without 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?
Given moderate complexity (3 params, enums, no output schema), the description does mention the return format ('evidence-based answer with citations'), which is helpful. However, it omits guidance on optional parameters and does not fully compensate for the lack of schema descriptions or output schema. It is adequate but has clear gaps.
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%, so the description must compensate, but it only indirectly addresses the 'query' parameter. It does not explain the 'pillar' filter or 'max_results' parameter at all, even though the schema shows a rich enum and defaults. This is a significant gap, as the agent gets no guidance on how to use the optional parameters.
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 tool's function: 'Ask any lifestyle medicine question' and specifies the resource and output ('evidence-based answer with citations from a 200-chunk knowledge base'). The scope is well-defined, and although there are no siblings to distinguish from, the purpose is unambiguous.
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 provides a clear context for use: any lifestyle medicine question, and delimits the domain (ACLM pillars, nutrition, labs, etc.). Since there are no sibling tools, it does not explicitly state alternatives or exclusions, but it effectively communicates when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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