night-margin-mcp

Server Quality Checklist

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Latest release: v0.3.0
Disambiguation5/5
Every tool has a clearly distinct purpose: analysis, target setting, alerts, history review, strategy comparison, gap evaluation, proposal creation, application, reversion, and a copilot for recommendations. Despite some conceptual overlap (e.g., gap_night_check and propose_decisions both deal with gap nights), their scope and output are well-differentiated.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., analyze_portfolio, set_target, check_alerts). However, 'margin_copilot' deviates as a noun phrase, breaking the pattern slightly. The rest are uniformly snake_case with a clear verb.
Tool Count5/5
With 10 tools, the set is well-scoped for the domain of short-term rental pricing management. Each tool contributes a specific, necessary function without redundancy, making the API lean yet comprehensive.
Completeness4/5
The tools cover the full price decision workflow: analysis, proposal, application, reversion, and monitoring. Missing are operational tools like notification configuration or historical alert logs, but these are secondary. The core lifecycle is complete, with safety features (dry runs, snapshots) included.
Average 4.4/5 across 9 of 10 tools scored. Lowest: 3.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 51 commits in the last 12 weeks
- Last stable release on July 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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?
No annotations provided, so description carries burden. Mentions local persistence to NM_STATE_DIR and that it shows current booked gross when possible. Lacks details on overwrite behavior, error cases, or idempotency.
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?
Two concise sentences, front-loaded with primary action, no wasted words.
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?
Covers purpose and persistence sufficiently for a simple tool, but lacks mention of error conditions, validation, or return behavior. Could be more complete given no output schema.
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?
Schema coverage is 100% with descriptions in schema. The description adds context about local storage and integration with analyze_portfolio but does not elaborate on parameter meanings beyond 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?
Clearly states it sets a monthly gross revenue target for a property, with specific verb and resource. Distinguishes from sibling analyze_portfolio which reports progress.
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?
Implicitly guides usage by mentioning analyze_portfolio reports progress, implying this tool is used to set targets before analysis. Does not explicitly list when-not-to-use or alternatives, but context is clear.
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?
Since no annotations are provided, the description carries full burden. It discloses that the tool changes prices (only with confirm), deletes custom rates, and restores prior rates from snapshot. It also mentions the API key requirement. No contradictions.
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 long, with no wasted words. Each sentence serves a purpose: stating core action and highlighting the confirm parameter and authentication requirement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks description of the return value, which is important for understanding what the preview or result looks like. As a mutation tool with no output schema, the description should cover this but does not.
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?
Schema coverage is 100%, and both parameters are well-described in the schema. The description adds minimal value: it clarifies that confirm must be true to actually delete and gives an example decision_id. This meets the baseline for high schema coverage.
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 reverts an applied pricing decision, specifying the actions: deletes custom rates and restores prior rates. It distinguishes from sibling tools like 'apply_decision' by focusing on the reversal operation.
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 context: requires explicit confirm=true to execute, otherwise returns a preview. It also mentions the need for WHEELHOUSE_API_KEY. However, it does not specify when NOT to use the tool or list alternative tools.
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 explicitly states it is a read-only simulation that never changes prices, explains assumptions (e.g., Strategy A is an upper bound), and lists the metrics shown. Since no annotations exist, the description carries the burden well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently written and front-loaded with the purpose, though it could be slightly more concise; it is not overly verbose.
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?
Given no output schema and 5 optional parameters, the description covers strategies, defaults, assumptions, and metrics. It could mention edge cases or error conditions but is reasonably complete.
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 coverage is 100%, but the description adds context beyond the schema by explaining the strategies' logic and default behaviors, enriching the meaning of parameters like discount_pct and uplift_pct.
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 it simulates and compares two pricing strategies to baseline, specifying the actions of each strategy. It distinguishes itself from sibling tools like 'analyze_portfolio' or 'gap_night_check' by being a dedicated simulation/comparison tool.
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 the tool is for comparing pricing strategies but does not explicitly state when to use it over alternatives, nor does it provide exclusions or when-not-to-use 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 carries the full burden. It explicitly states 'Read-only — never changes any prices', which is a critical behavioral trait. It also explains parameter defaults. More details on authentication or rate limits are not necessary for this simple analytical tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, consisting of three to four sentences. It front-loads the key identity ('The margin guard for Wheelhouse pricing') and immediately explains what it computes. Every sentence adds information, though the first sentence could be more explicit.
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?
Given no output schema, the description lists the specific outputs (net per available night, leak, best/worst properties), setting expectations. For a tool with three optional parameters and a well-defined purpose, the description is fairly complete and covers the essential aspects.
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 coverage is 100% with each parameter having a description. The description adds value by clarifying the default window behavior for from/to and explaining avg_turnover_cost as an override in € per turnover, providing context beyond 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 clearly states the verb 'computes' and the resource 'short-term rental portfolio's net after turnover costs', listing specific outputs (net per available night, leak, best/worst properties). It distinguishes itself from siblings like set_target and apply_decision by emphasizing its read-only analytical nature.
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 context: it is read-only and never changes prices, with optional from/to parameters defaulting to last 30 + next 90 days. It does not explicitly list when not to use it or alternatives, but the context of sibling tools and the described function imply appropriate usage.
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 exist, so the description carries full burden. It discloses read-only behavior, no price writes, and detailed notification logic (channel config, default send). Slightly vague on what 'nothing is sent' entails if no channel, but adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, uses numbered points for clarity, and includes all necessary details. It is slightly verbose by including the cron alternative inline but remains well-structured.
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?
Given no output schema, the description explains the return (text report, optional notification) but does not detail the report contents or format. For a two-alert tool, the description covers key behavioral aspects, but a sample or structure would improve 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 single parameter 'send' is described in the schema (100% coverage). The description adds context about default behavior and conditional sending, which goes beyond the schema's description. This adds meaningful value for an agent.
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 specifies two distinct alert checks (gap-floor and new-booking) and clearly distinguishes from propose_decisions by stating it never saves proposals. The verb 'check' and resource 'alerts' are specific and 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 explains the tool is for proactive checks and contrasts with propose_decisions. It also mentions the cron alternative. However, it does not explicitly list when to avoid this tool versus siblings like gap_night_check or review_history, leaving some ambiguity.
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 carries the full burden of behavioral disclosure. It explains that proposals are saved to a local decision log, never change prices, and that the risk preset affects the minimum margin. It does not detail authentication or error handling, but covers the core behavior well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of around 100 words, front-loaded with the main purpose. It is concise, but could be slightly more structured with bullet points or separate sentences for each detail. Still, it is well within acceptable length.
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?
Given 3 optional parameters, no output schema, and no annotations, the description is complete. It covers the tool's operation (finds gap nights, proposes fixes), defaults (window, risk), and side effects (saves to log, no price changes). No important aspects are missing.
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 coverage is 100%, so baseline is 3. The description adds value by explaining default values for 'from' and 'to' (today and 30 days) and the meaning of the 'risk' enum (conservative doubles MIN_MARGIN, recommended keeps it, aggressive uses 40%). This goes beyond the schema's brief descriptions.
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 purpose: proposing pricing decisions for gap nights by identifying those below cost floor and suggesting fixes. It uses specific verb+resource ('propose decisions') and distinguishes itself from sibling tools like apply_decision by clarifying that proposals do not change prices.
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 explains when to use the tool (for gap nights below cost floor) and contrasts it with apply_decision, which actually applies changes. It mentions defaults (30-day window) and adjustable risk presets but does not explicitly list all alternative tools or specify when not to use 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?
With no annotations, the description fully explains the algorithm: floor price computation from median cost rows, adjustable margin via risk preset, comparison with candidate price, and the verdict. It also mentions behavior for already-booked dates and confirms no state changes. No contradictions.
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 coherent paragraph, front-loaded with purpose, then explaining the algorithm and verdict. Every sentence adds necessary information, with no redundancy or fluff.
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?
Given no output schema, the description adequately explains return values: floor clearance, net after turnover costs, and booking info if applicable. All four parameters are covered, and the tool's behavior is fully described for its complexity.
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?
All parameters are described in the schema (100% coverage). The description adds behavioral meaning: how candidate_price is optional and used, how risk preset adjusts margin, and the role of property_id and date in computing floor price. This adds value beyond schema definitions.
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 purpose: checking whether filling a single gap night is worth it, with a specific verb ('Checks') and resource ('gap night'). It distinguishes from sibling tools by focusing on a single night assessment rather than portfolio analysis or strategy proposals.
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 when to use (when deciding about a gap night) and notes it is read-only, but does not explicitly state when not to use or compare with sibling tools like propose_decisions or apply_decision. The usage context is implied rather than prescribed.
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?
With no annotations, the description fully covers behavioral traits: it confirms the tool mutates prices, requires explicit confirmation, snapshots prior rates for reversibility, and requires an API key. No contradictions.
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 well-structured sentences. The first sentence states the primary action and its mutability; the second provides critical usage details (dry-run, snapshot, API key). 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 explains the dry-run preview and the write behavior, and mentions the snapshotting for revert. It lacks explicit details on the output format (e.g., confirmation response), but given the tool's write nature, this is adequate.
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 coverage is 100%, but the description adds meaningful context beyond parameter descriptions by explaining the dry-run behavior and the conditional writing based on confirm. This helps the agent understand the interplay between 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?
Clearly states the tool applies a pricing decision to Wheelhouse, writing fixed custom rates for gap nights. Distinguishes from sibling tools like propose_decisions and revert_decision by specifying the action (apply vs propose/revert).
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?
Describes the dry-run default and the need for confirm=true to write, and mentions the snapshotting for revert. However, it does not explicitly state when to use this tool over siblings, though the context implies it follows propose_decisions.
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?
Since no annotations are provided, the description carries full burden. It declares read-only nature, no state changes, estimation methodology for turnover cost, that estimates are not measured per booking, and that window defaults to available history (no fabricated spans). Comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with purpose. Every sentence adds value, though there is slight redundancy (estimation mentioned twice). Efficient overall.
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?
Without output schema, the description explains return values (revenue, turnover cost, net, turnover share, occupancy, ADR). It also covers derivation limitations, defaults, and read-only behavior. Thorough for a 3-param 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 coverage is 100%, so baseline is 3. The description adds value by explaining default behavior (optional, defaults to all history) and the purpose of avg_turnover_cost override and its estimation formula. This extra context justifies a 4.
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 mirrors your own past monthly KPIs (revenue, estimated turnover cost, net, turnover share, occupancy, ADR). It distinguishes from siblings like analyze_portfolio or compare_strategies by focusing on personal history review.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use: to review past history. Also clearly states what it does NOT do: forecast, recommend, rank strategies, or suggest alternatives. Provides clear context on default window behavior.
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?
With no annotations provided, the description fully discloses behavior: it is read-only (never writes prices or state), does not invent figures like a '30-day net', and requires an API key. It also clarifies what the tool does not do (claim unsold night as gain). This level of detail exceeds requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, but somewhat verbose. Every sentence adds value, but the paragraph could be slightly more concise. Nonetheless, it is clear and organized.
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?
Given no output schema, the description sufficiently explains what the tool returns (ranked list, options with recommended one). It also covers the workflow context (staging moves leads to propose_decisions/apply_decision). The parameter count is low and all are optional, so completeness is high.
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?
The input schema has 100% coverage with descriptions for each parameter. The description adds additional context: defaults for from/to (next 30 days), and risk presets are same as propose_decisions. This adds value beyond 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 clearly states the tool reads figures, groups gap nights, ranks by below-floor exposure, and weighs options (Hold, Guard floor, raise minimum stay). It explicitly distinguishes itself from propose_decisions by noting that it reads the same figures but does not write, and that staging a move routes to propose_decisions. The verb 'reads' and resource 'margin copilot' are specific, and sibling differentiation is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: it is read-only and should be used to inspect margin opportunities. It also tells the agent when not to use it (for actual writes, use propose_decisions → apply_decision) and how to route actions. It mentions the requirement for WHEELHOUSE_API_KEY. This is complete usage 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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