oura-ring-mcp-server
Server Quality Checklist
Latest release: v0.2.0
- Disambiguation5/5
The two tools have clearly distinct purposes: 'info' returns server metadata, while 'oura_data' fetches Oura Ring API data. There is no overlap or ambiguity.
Naming Consistency3/5With only two tools, a consistent naming pattern is hard to establish. 'info' is a noun, while 'oura_data' uses underscore separation. The names are not chaotic but lack a strong, predictable pattern.
Tool Count3/5Having only two tools for a server that covers a wide range of health data (18+ collections) feels thin. The generic 'oura_data' tool consolidates all data retrieval, which is efficient but potentially overloaded.
Completeness3/5The server covers read access to many Oura Ring data collections, but lacks any write operations (create, update, delete). For a comprehensive API surface, this is a notable gap.
Average 4.5/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 16 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
This server has been verified by its author.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. Description adds specific content details (name, version, etc.) but doesn't disclose additional behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single, front-loaded sentence with no extraneous words. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and annotations, the description adequately covers the tool's output by listing the categories. Missing output schema is a minor gap but acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters defined; schema coverage is 100%. The description doesn't need to add parameter info, so baseline score applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies a clear verb ('returns') and resource ('identity and build information'), listing exact fields. Distinguishes from sibling 'oura_data' which likely deals with data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use instructions, but the purpose is clear and different from the only sibling. Context signals imply no alternatives needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, which the description confirms by implying data retrieval. The description adds valuable behavioral context: daily collections have a 7-day default, time-series require ISO-8601 datetimes, list collections paginate via next_token, and document_id fetches a single record. No annotation contradiction exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly long but well-structured with a clear opening, bullet-point collection list, and separate parameter usage notes. While every sentence adds value, it could be slightly more concise by grouping common parameter patterns. Still, it remains readable and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, 19 collections, no output schema), the description covers all necessary usage contexts: parameter selection by collection type, default behaviors, pagination, and single-document retrieval. It is complete enough for an agent to use the tool correctly without needing additional external documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing a baseline of 3. The description adds meaning by explaining which parameters apply to which collection types, the default date range, and the semantics of latest and document_id. This goes beyond the schema's field descriptions, justifying a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Fetch data from the Oura Ring API v2' and enumerates all 19 collection types with brief summaries, clearly distinguishing the resource and scope. It separates daily, time-series, and singleton collections, making the tool's purpose unambiguous and differentiating it from a sibling like 'info'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on which date parameters to use for daily vs. time-series collections, explains the default behavior when omitted, and covers pagination via next_token. However, it does not explicitly state when to prefer this tool over the sibling 'info' tool, which serves a distinct purpose, so a full 5 is not warranted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/jordanburke/oura-ring-mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server