Quantified Self MCP Server
Server Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
Each tool clearly targets a separate domain — health data vs. finance data — with no overlap in scope or purpose. Even though both are read-only, the descriptions explicitly differentiate the types of data and their sources.
Naming Consistency5/5Both tool names follow the exact same pattern: read_<domain>_data. This is a highly consistent and predictable naming convention that makes it easy for an agent to infer function from name.
Tool Count3/5With only two tools, the server feels minimal, but given its explicitly read-only and privacy-focused design, a small surface is plausible. The count sits at the borderline of 'thin' but is not unreasonable for the narrow scope described.
Completeness4/5The server covers the main categories it promises (health and finance) with broad read access. There may be missing granularity (e.g., separate tools for steps vs. sleep), but the combined tools likely suffice for typical queries. No obvious dead ends exist for the stated read-only purpose.
Average 4.2/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
Tools from this server were used 2 times in the last 30 days.
This repository includes a glama.json configuration file.
This server has been verified by its author.
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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?
No annotations are present, so the description carries the full burden. It goes beyond the basic action by disclosing that the server is intentionally read-only by design, that data lives in a local health database, and that no write tool exists. This meaningfully informs the agent that calls are safe and non-mutating.
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?
The description is two concise sentences with no filler. The first sentence front-loads the action and the specific data types, and the second adds valuable read-only context. Every sentence earns its place.
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?
For a simple read-only tool with two optional, fully documented date parameters, an output schema, and explicit read-only context, the description is complete enough for an agent to invoke it correctly. The only minor gap is not naming the sibling explicitly, but the health-domain framing makes the intended use unambiguous.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both start_date and end_date fully documented in the input schema. The description adds no parameter-level detail, but the baseline of 3 applies because the schema already provides complete parameter semantics.
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 opens with a specific verb ('Read') and a concrete resource ('local health database'), then enumerates the exact metrics: daily steps, sleep hours, and resting heart rate. This clearly differentiates it from the sibling read_finance_data by data domain and content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the tool name and the described health metrics, but the description does not explicitly say when to choose this tool over read_finance_data or provide exclusion conditions. The read-only statement does address the absence of a write counterpart, but it does not fully position this tool against its sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does disclose a key behavioral trait: the server is intentionally read-only and there is no corresponding write tool. This tells the agent the call has no side effects, though it does not cover more detailed behavior like failure modes or permissions.
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?
Two tightly written sentences: the first states the action and resource, the second adds important read-only context. No filler or redundancy.
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 three optional parameters all documented in the schema and an output schema present, the description is complete: it states the domain, the safety model, and the absence of a write counterpart. Nothing necessary for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and each parameter already has detailed semantics (format, defaults, empty-list behavior). The tool description adds no parameter information, so the baseline 3 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?
The description names a specific verb and resource: 'Read categorized expenses from the local finance ledger database.' The finance-ledger scope clearly distinguishes it from the sibling read_health_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?
It gives clear context: this tool is for finance-ledger expense reads, so an agent can infer it is the right choice for finance data and not health data. It does not explicitly name read_health_data as the alternative, so it falls just short of full routing guidance.
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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- Evaluate tool definition quality.
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