noisefloor
Server Quality Checklist
Latest release: v0.1.3
- Disambiguation5/5
Each tool targets a distinct statistical question: A/B testing, change detection, forecasting, denominator shift, forecast evaluation, and multiple testing correction. No overlap in purposes.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern in lowercase snake_case, e.g., did_it_change, forecast_next, which_metrics_matter. No deviations.
Tool Count5/56 tools is an ideal number for a focused statistics toolkit, covering essential operations without being overwhelming or sparse.
Completeness4/5The set covers key statistical tasks (A/B testing, change detection, forecasting, multiple testing), but lacks a sample size/power analysis tool, which is a minor gap.
Average 3.7/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states that the range adapts to recent volatility and stays valid with shifts, but does not disclose how many values are needed, what model is used, or what happens with insufficient data. No mention of errors 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, consisting of two sentences. It is front-loaded with the key question. However, the use of a question format might be less direct for an AI agent. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a simple input schema but no output schema. The description is incomplete because it does not specify the structure of the output (e.g., object with 'prediction' and 'range' fields). It also lacks guidance on edge cases like very short history or extreme volatility. For a forecasting tool, more detail is needed.
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 clear descriptions for both 'values' and 'nominal'. The tool description adds no additional meaning beyond the schema. Given high coverage, baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a question to state the tool's purpose: predicting next reading and range. It mentions adapting to volatility, which hints at a unique feature. However, it does not explicitly state 'forecast' or 'predict' as a verb, and could be more direct. It distinguishes well from sibling tools which have different focuses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. There is no mention of prerequisites, data requirements, or scenarios where other tools like 'score_forecasts' or 'did_it_change' would be better suited. The description only implies use for forecasting, but lacks comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It explains the statistical concept (FDR control) and provides an example, but does not describe output format, assumptions, or edge cases (e.g., required dependencies like did_it_change evidence values).
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?
Two concise sentences with an engaging tone. The first sentence is a question but quickly leads to explanation. No filler, every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and sibling tools, the description misses key context: the evidence parameter expects output from a sibling (did_it_change), and the output is not described. The tool's niche is clear but integration details are lacking.
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 coverage is 100%, so baseline is 3. The description adds no extra detail about parameters beyond what the schema provides (e.g., alpha default, evidence structure).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool controls false discovery rate across many metrics, contrasting with per-metric thresholds. It includes a concrete example illustrating the problem. However, it does not explicitly differentiate from sibling tools beyond the conceptual niche.
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?
The description implies use when multiple metrics are tested and false discovery control is needed, but lacks explicit guidance on when to use versus alternatives (e.g., when to use ab_test or score_forecasts).
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?
No annotations provided, so description carries full burden. It discloses that the tool uses only information known at the time (avoiding look-ahead bias) and reports calibration and worst misses. No destructive side effects mentioned, which is appropriate for a read-only evaluation tool.
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 sentence, front-loaded with purpose, no redundant information. Every word contributes meaning.
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 output schema, the description adequately hints at return values ('calibration plus worst misses'). Parameter descriptions in schema complement well. No significant gaps for a tool of this complexity.
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 coverage is 100% with parameter descriptions for 'values' and 'nominal'. The description adds no new meaning beyond what the schema already provides, so baseline score of 3 is appropriate.
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 clearly states the tool 'grades every prediction the tool would have made over the history' and 'reports calibration plus the worst misses', using specific verbs 'grade' and 'reports'. It distinguishes from siblings like 'forecast_next' by focusing on historical evaluation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'forecast_next' or 'ab_test'. The description implies usage for historical forecast evaluation but does not provide explicit context or exclusions.
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 full burden. It discloses key behavioral traits: detection of both rises and drops, use of historical data, a false-alarm rate, and no distribution assumption. This is fairly transparent, though it doesn't clarify whether the tool is read-only or how the false-alarm rate is set.
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 a single, well-structured sentence that front-loads the core question. It is concise and efficient, though could be slightly more scannable with a brief list. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, and the description fails to specify what the tool returns (e.g., boolean, p-value, signal strength). This is a significant gap, especially for a statistical tool where output interpretation is critical. The description also doesn't explain how the false-alarm rate is configured or communicated.
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 coverage is 100%, so baseline is 3. The description adds algorithm context (false-alarm rate, no distribution assumption) but does not elaborate on parameter syntax beyond what the schema provides. No parameter-specific details are added, so it meets but does not exceed the baseline.
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 clearly states the tool's purpose: determining whether a metric change is real or noise. It specifies 'detects both rises AND collapses' and includes unique algorithm properties (false-alarm rate, no distribution assumption), distinguishing it from siblings like ab_test (hypothesis test) or which_metrics_matter (metric selection).
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 implies the tool should be used when you need to know if a change is noise, providing context like 'against the metric's own history'. However, it does not explicitly state when not to use it or mention alternatives among siblings, slightly limiting 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?
With no annotations provided, the description fully carries the behavioral burden. It discloses the key behavior: peeking is safe and does not inflate false-positive rates. This is critical for correct usage. However, it does not mention other behaviors like mutability or side effects, which are not relevant here.
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 sentences, front-loaded with the core question, and immediately providing the essential statistical property. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema and the description does not explain what the tool returns (e.g., recommendation, p-value, winner). Given 7 parameters and no output schema, the description is incomplete for an AI agent to fully understand the tool's behavior.
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?
The input schema covers 71% of parameters with descriptions. The description adds no additional meaning to parameters beyond what the schema provides. For the 29% not covered (a_label, b_label), the description does not compensate. Baseline of 3 is appropriate.
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 clearly asks 'Can you call a winner on an A/B test yet?' which directly states the tool's purpose. It specifies the methodology (confidence sequences) and distinguishes from sibling tools like forecast_next or did_it_change.
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 explicitly states that the tool uses anytime-valid confidence sequences and that it is safe to run after every observation without inflating false-positive rates. It contrasts with t-test/z-test, providing clear usage guidance, though no direct comparison to siblings is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It describes the conceptual behavior (checking denominator shifts) but does not specify the output format, assumptions, or edge cases. The agent knows the goal but not the result type.
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 extremely concise, consisting of two short sentences. It front-loads the core question and immediately provides actionable context. Every word earns its place, and there is no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should ideally specify the return value. It adequately explains the use case and parameter relationships but leaves a notable gap regarding what the tool returns, which is essential for an agent to use it correctly.
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 basic descriptions for both parameters. The tool description adds meaningful context by linking parameters to real-world rate metrics and denominator shifts, enhancing understanding beyond the raw schema.
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 clearly states the tool's purpose: to determine if a change in a rate metric is due to actual movement or a shift in the denominator. It uses a specific verb 'run' and resource 'rate metric change'. The description effectively distinguishes from sibling tools like 'ab_test' and 'did_it_change' by focusing on the artifact of denominator shifts.
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 when to use the tool: 'Run this before reporting any RATE as a change', with examples like conversion rates and error rates. It implies the context for use but does not explicitly mention when not to use it or list alternatives; however, the context is clear enough.
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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