Live bitcoin bot logbook

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true and openWorldHint=false, so the description doesn't need to argue safety. What it adds beyond the annotations is substantive: the exact dataset (Bitvavo daily closes), the currency pair (BTC-EUR), the start date (2019), and the 'so far' as-of-now framing, which signals results shift with the current price. It does not discuss latency, caching, or failure modes, which keeps it off a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler, and the core computation leads ahead of the enumerated outputs. The clause stacking ('how often and how long ... so far, the deepest drop and the current drop') is dense on first read but every element maps to a returned figure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does so by enumerating the four reported measures plus the underlying dataset and window. Remaining ambiguity is minor: the unit for 'how long' (days vs. bars) and whether 'top' means the all-time high within the window are left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the sole parameter carries only the terse 'Default 40' in the schema. The description supplies the missing semantics — depth_pct is a threshold measured in percent below the running top, and the output conditions on closes at or beyond that depth. That is real meaning the schema does not convey, earning above the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The definition names a specific resource and computation: drawdown of BTC-EUR closes versus its top, reported as frequency, duration, deepest drop, and current drop. It also pins the data source and window (Bitvavo daily closes since 2019), so the agent knows exactly what it returns. It falls short of 5 only because it is framed as a question about the output rather than a verb-first statement of the operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the subject matter — reach for this when asked about Bitcoin drawdowns or peak-to-trough history. There is no explicit when-to-use framing, no stated prerequisites, and no mention of any alternative tool for related questions. Since none of the listed siblings overlap in scope, the absence of routing guidance is a smaller gap, but it is still absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile of this zero-argument call is fully covered. The description adds content scope (price and availability information are returned) but says nothing about response format, whether pricing is embedded text or a link, or how current the price is. 'Where to get it' implies a URL or pointer, which is modestly useful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no wasted words, and the payload contents (definition, price, source) are listed up front. The parenthetical explanation is dense but genuinely informative rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only information lookup with no output schema, the description covers what an agent needs before calling: what subject it answers and what facts come back. Minor gap in not stating the response shape, but the tool's simplicity keeps that low-stakes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes no parameters, so there is nothing for the description to disambiguate beyond the input schema. Baseline of 4 applies for a correctly parameterless tool; the description does not need to compensate for anything.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific resource (the build kit) and enumerates the content it returns: what it is, its price, and where to obtain it. The parenthetical clarifies what 'build kit' actually means, which an agent could not infer from the name alone. It does not explicitly contrast with siblings like get_summary or get_design_decisions, but the distinct content scope is clear enough to separate them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent should call this when a user asks about purchasing or understanding the build kit. There is no explicit when-to-use statement, no exclusions, and no mention of the sibling informational getters that might overlap with a general question about the bot.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered and the description does not need to repeat it. The description adds genuine context by stating the payload contains both dated rationale and measured leak locations, but says nothing about volume, ordering, or response format. With a zero-argument read tool and annotations carrying the operational semantics, 3 is fair.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler, and the 'plus' clause signals the second half of the payload up front. The parenthetical example list is slightly loose but earns its place by making the abstract phrase 'design decision' concrete.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining what comes back, and it does so at a useful level: decisions plus dates plus reasons, and measured leak sites. It does not describe the shape or size of the result, which is the remaining gap for a tool with no schema to fall back on.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes no parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The parenthetical examples describe returned content rather than inputs, which is appropriate here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description says exactly what is returned: every design decision of the bot, each with its date and rationale (naming examples like 'never sell at a loss' and 'depth budget'), plus the measured locations of profit leakage. That is a specific resource with concrete content, so it reads clearly against siblings like lookback_calculator or simulate_ladder. It stops short of explicitly contrasting itself with any sibling, which keeps it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use statement and no mention of alternatives; guidance is only implied by the content listing (consult this when you need the rationale behind the bot's rules or evidence of where profit was lost). An agent can infer the use case, but nothing tells it when this tool is the wrong choice versus, say, get_summary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds one genuinely useful behavioral fact beyond annotations — that the response also contains the bot's rules and the definitions of the figures — which matters for interpretation. It says nothing about size, freshness, or whether statistics are over all weeks or a filterable range.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single compact sentence fragment with no filler, but the lack of a leading verb or clause ('Returns a summary including...') makes it read as a list of nouns rather than a statement of what the tool does. Front-loading is acceptable given the length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-argument, read-only tool with no output schema, the description partially compensates by naming the metrics returned. However, it omits the scope of aggregation and the shape of the result, so an agent still cannot predict the output structure beyond topic keywords.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is no parameter semantics for the description to carry; baseline 4 applies. The enumerated metrics do at least preview the response fields even though no output schema exists.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description enumerates the content of the response (median/average realized week, goal weeks, best/worst week, compounded profit, bot rules) which is more specific than the title 'Logbook summary', but it is a bare noun phrase with no verb and never states the scope of aggregation. An agent cannot tell from the text alone whether this summarizes all weeks or a subset, nor how it differs from siblings like get_weekly_results or lookback_calculator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no mention of any alternative tool. With five other read-oriented siblings in the namespace, the agent is left to infer that this is the aggregate/overview entry point rather than a per-week detail call.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real value by disclosing the shape of the returned record (rank, bitcoin move, bot changes, page URL), which is important since there is no output schema. It omits error behavior for unknown weeks, but for a read-only lookup this is solid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One compact sentence that front-loads the resource and then lists the payload fields. Every clause conveys information, though the fragmentary phrasing (no verb) reads slightly like a data label rather than a description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read-only lookup, the definition covers what is returned and the schema covers the input fully. The gaps are the lack of differentiation from get_weekly_results and no note on behavior for weeks that don't exist, both minor for a tool this simple.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single parameter with 100% schema coverage, including a regex pattern, maxLength, and an example ('2026-W43'). The description adds nothing about the week parameter, so the schema does all the work; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource precisely ('one logbook week') and enumerates what it contains: rank, bitcoin's move, bot changes, page URL. That is far more specific than the bare name 'get_week'. It doesn't explicitly contrast with sibling tools like get_weekly_results or get_summary, so an agent can't fully rule out overlap.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: fetching a single, specific week's logbook entry. There is no statement of when to prefer this over get_weekly_results or get_summary, and no guidance on what to do for an out-of-range or future week. The required ISO-week parameter hints at the single-week scope, but the agent must infer it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description contributes the useful behavioral detail that figures are net of fees and include bitcoin's own weekly move as a benchmark. It says nothing about ordering, empty-week handling, or what the default window is despite zero required parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence, front-loaded with the primary metric and tight with no filler. It is telegraphic to the point of reading as fragments ('goal reached, compounded total'), but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description must explain the return values, and it does list the fields, which is the right call. However, for a tool with zero required parameters, it never states what the default range is or how weekly rows are bounded, leaving a real gap an agent would hit on first call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both ISO-week parameters are already documented with pattern and example format. The phrase 'Optional range' confirms the optionality the schema already expresses but adds no semantics such as inclusive bounds or default behavior. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource (weekly results) and enumerates the metrics returned: realized profit after fees, percent of capital in play, goal reached, compounded total, and bitcoin's move for the week. That is concrete enough to separate it from get_week (single week) and get_summary, though the description never states the separation explicitly and relies on the tool name for the verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The only usage signal is 'Optional range,' which describes a parameter rather than when to call this tool. Nothing says when to prefer this over get_week, get_summary, or lookback_calculator, and there is no mention of what happens when the range is omitted.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavior context: results are based on realized profit only and are historical rather than projected, which sets expectations about output validity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core computation and closed with the key caveat. No filler or redundant restatement of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read-only tool with fully documented schema and annotations, the description covers purpose and the historical/realized framing. It does not say what the return looks like (per-week series vs. final balance), but with no output schema that is a minor omission.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, including the ISO-week pattern and amount bounds, so the schema does the heavy lifting. The description only reinforces that the amount is compounded week by week rather than held flat, which is marginal added meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific computation (compounding the logbook's realized weekly results from a start week with a given amount) rather than restating the name. Its 'history, not a forecast' clause implicitly separates it from simulate_ladder, but no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: 'Realized profit only; history, not a forecast' signals this is a backward-looking calculation, suggesting simulate_ladder is the forward-looking alternative, but the description never states when to choose one over the other.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description goes further by disclosing the simulation rules ('buy a slice on every step_pct drop below the lowest open buy, sell each slice at +target_pct, never at a loss') and the shape of the result, which is real behavioral context beyond 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense sentences with the core mechanics front-loaded and no filler. The only blemish is 'since 2019', which conflicts slightly with the schema default of one year before the last candle, and the trailing list of outputs is slightly compressed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly compensates by naming the returned metrics (realized %, trades, open positions, buy-and-hold). Combined with 100% schema coverage, an agent has enough to call it correctly, though the effective date window remains ambiguous given the 'since 2019' phrasing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine meaning by explaining the mechanics of step_pct (drop threshold below the lowest open buy) and target_pct (sell threshold). slices, fee_pct and the date bounds are left to the schema, which is acceptable given full coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: simulate a buy ladder on Bitvavo BTC-EUR daily candles. It names the exact strategy mechanics (slice on step_pct drop, sell at +target_pct) and is plainly distinct from siblings like lookback_calculator, bitcoin_drawdown and get_weekly_results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the description of what the simulation does, but there is no explicit 'use this when' or 'use X instead' statement, nor any stated prerequisites. An agent can infer the purpose but gets no routing guidance against the other analytical tools in the sibling set.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.