runmeter

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct operation: recording, retrieving a single run, listing with filters, aggregating summaries, exporting, and deleting. There is no overlap in purpose, making tool selection unambiguous.
Naming Consistency5/5
All tool names follow the pattern 'runmeter_' + verb (get, summary, record, list, export, delete). The consistent prefix and verb-based naming make the toolset predictable and easy to navigate.
Tool Count5/5
Six tools is well within the ideal 3-15 range and perfectly scoped for a telemetry server. Each tool earns its place, covering core operations without unnecessary bloat.
Completeness5/5
The toolset covers the full lifecycle of LLM/agent run telemetry: record, retrieve, list, analyze, export, and delete. There are no obvious missing operations for the domain, and the single-run delete with confirmation prevents accidental data loss.
Average 4.3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 commits in the last 12 weeks
- No stable releases found
- 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds the 'full stored row' detail, which provides some value beyond the schema, but it does not mention behavior for nonexistent IDs or permission requirements. No contradiction with 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 a single, clear sentence with zero filler. It front-loads the action immediately and wastes no words.
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?
The tool is simple, and the presence of an output schema covers return structure. Annotations cover the read-only and idempotent nature. For a basic get-by-ID operation, the description is sufficiently complete, though it omits error-case behavior.
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?
With only one parameter (run_id) and 0% schema description coverage, the description is expected to compensate. It mentions 'run id', which aligns with the schema title, but adds no additional semantics beyond what the parameter name itself implies. Adequate but not enhancing.
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 uses a specific verb 'Return' and clearly identifies the resource ('full stored row') and scope ('single run id'). This distinguishes it from sibling tools like list (multiple runs) and summary (aggregate data).
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 when fetching a single run's full data, but it does not explicitly state when to use this tool over alternatives like summary or list. No exclusions or alternative tool names are given.
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?
The description adds substantial value beyond the basic annotations by detailing cost-handling logic: explicit cost_usd takes precedence, otherwise computed from a pricing table, and unpriced models result in null cost with a response note. This helps the agent predict behavior without contradicting the 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 short paragraphs, front-loaded with the core purpose, and every sentence provides meaningful detail (explicit vs computed cost, unpriced behavior). No wasted words.
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?
The description covers the primary behavior and a key edge case (unpriced model), while return format is covered by the output schema. It does not mention required fields or validation, but those are already in the schema, so the description is sufficiently complete for the tool's simplicity.
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 description enriches the meaning of cost-related parameters (e.g., what happens if the model is unpriced) that the schema does not fully cover. However, it does not explain the overall `run` object structure or other parameters, and the top-level schema coverage is 0%, so compensation is partial.
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 states a specific action ('Record a single LLM/agent run') and the resource targeted, plus the outcome ('return the stored row'). This clearly distinguishes it from sibling tools like runmeter_list or runmeter_get, which read or summarize data.
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 that this tool is for logging new runs, but it does not explicitly state when to use it versus alternatives. No mention of prerequisites or exclusions, so the usage context is implied rather than directly guided.
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?
Annotations already mark the tool as read-only and idempotent. The description adds valuable behavioral details: group_by allowed values, since accepting ISO or relative formats, sorting by total cost descending, and the exact metrics reported per group. No contradictions are present.
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 compact and front-loaded: purpose first, then parameter details, then output behavior. Every sentence adds relevant information without any fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present and strong annotations, the description nevertheless covers purpose, parameters, and output metrics in sufficient detail. Minor semantics like timezone are not specified but are not critical for this simple aggregation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and parameters are plain strings, but the description fully compensates by listing all valid group_by values and explaining the since format (ISO timestamp or '30d'). This adds meaning well beyond the schema, though defaults are already present in the schema.
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 opens with 'Aggregate telemetry into cost and reliability rollups,' which uses a specific verb and resource, and distinct output details. This clearly separates it from sibling tools like runmeter_get, runmeter_list, and runmeter_export, which are not aggregation tools.
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 aggregate summary queries but never explicitly contrasts it with alternatives or states when not to use it. There is no mention of sibling tools or exclusions, so 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite annotations already declaring destructiveHint=true, the description adds crucial behavioral context: the requirement for confirm=true, scoping to a single ID, and the safety guarantee that it cannot wipe the store accidentally. This goes beyond the structured annotations and is highly valuable for an 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 the primary action, and every word adds value. It is concise without omitting essential safety details.
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?
The description covers the tool's purpose, safety guardrail, and uniqueness among siblings. With an output schema present, it doesn't need to explain return values. It lacks only minor details like what happens if the run_id doesn't exist, but this is not critical given the overall completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions both parameters indirectly: 'by id' refers to run_id, and 'requires confirm=true' explains the confirm parameter's significance. With 0% schema coverage, this textual explanation compensates well, though it doesn't spell out the boolean type or default value which are in the schema.
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 opens with 'Delete a single run by id,' which is a specific verb+resource+scope. It clearly distinguishes from sibling tools by noting 'This is the only destructive tool,' making its purpose 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 states that this is the only destructive tool and requires explicit confirm=true, implying it should be used for one-off deletions with caution. It doesn't explicitly name alternative tools for retrieval or other operations, but the context is clear enough to guide an agent on when to invoke it.
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?
Beyond the readOnly/idempotent annotations, the description adds important behavioral details: output is JSON or CSV, ordered newest-first, capped at limit rows, with a configurable since window and max limit of 10000. These details help the agent predict exactly what happens without contradicting the 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-loads the main purpose, and every sentence adds useful information. It avoids repetition of schema defaults and stays compact.
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 read-only export tool with three parameters and an output schema, the description is nearly complete. It covers purpose, output format, ordering, and limits. Minor omissions are the exact format of 'since' and lack of explicit guidance vs sibling tools, but these do not critically impair usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description carries the burden. It does explain fmt values, since as a time window, and limit as a row cap with a maximum. The main gap is that the exact format of 'since' (e.g., ISO date, timestamp) is not specified, leaving some ambiguity.
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 states a specific verb ('Export'), a clear resource ('raw runs'), and output formats ('JSON array or CSV string'), which clearly distinguishes this from siblings like runmeter_get or runmeter_list. It focuses on bulk export rather than retrieval, summary, or mutation.
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 phrase 'for external analysis' provides a clear context for when to use this tool, and the behavior is described concretely. However, it does not explicitly mention alternatives or when NOT to use it relative to sibling tools, so it falls short of perfect guidance.
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?
Beyond the annotations (readOnlyHint, idempotentHint), the description adds meaningful behavioral detail: newest-first ordering, filter semantics (including status allowed values 'ok'/'error' and tag match behavior), relative time windows for 'since', and the return of total count to signal pagination needs. This goes beyond basic safety hints and enriches the agent's understanding of what to expect.
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 compact and well-structured: a one-line summary sentence, a concise filter list with explanations, and a final sentence on pagination and return count. Every sentence earns its place with no redundant or vague wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a list tool with 7 optional parameters, an output schema, and strong annotations, this description is complete. It covers purpose, ordering, all filter options with semantics, pagination behavior, and the return shape (limited rows plus total count). No critical gaps are evident.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explicitly listing all seven parameters (tag, agent, limit, model, since, offset, status) and explaining their semantics. It clarifies status allowed values, since format (ISO or relative like '24h'/'7d'), tag matching, and pagination use of limit/offset. This adds substantial meaning beyond the bare schema field names.
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: 'List runs newest-first with optional filters.' This uses a specific verb ('list') plus resource ('runs') and adds ordering and filtering scope. It distinguishes from sibling tools like runmeter_get (single run fetch), runmeter_summary (aggregates), runmeter_record (create), and runmeter_delete (delete) by emphasizing the paginated list behavior with filters.
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 clear usage context: it explains the available filters and how to use limit/offset for pagination, including how to know when to page further via the total count. However, it does not explicitly name sibling alternatives or state when not to use this tool (e.g., for a single run use runmeter_get). This matches 'clear context, no exclusions'.
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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