Trandence Trading Journal

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

With no annotations, the description carries the full burden and does well by disclosing the row semantics and the key omission behavior ('Days with no trades are omitted'). It could additionally mention sort order or date inclusivity details, but these are minor for a simple read tool.

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 tight sentences lead with the output shape hands-on, then give usage context and an important caveat. No wasted words.

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 retrieval tool with no output schema, the description sufficiently defines the row shapehog and the no-trade-day edge case. It is not explicitly stated whether rows are sorted chronologically, but this is a minor omission given how specific the rest of the description is.

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 description coverage is 100%, and the schema already documents both parameters as inclusive YYYY-MM-DD dates. The description does not add parameter-level semantics, but it does not need to because the schema fully covers it.

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?

States a specific resource ('one row per trading day') and lists the concrete fields it returns: net P&L, trade count, wins, losses, and fees. The warning against inferring daily activity from monthly totals clearly differentiates this from monthly-level sibling tools.

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?

Explicitly says to use this for frequency, overtrading, streaks, and day-to-day shaping of a month, and gives the negative guidance 'do not infer daily activity by dividing a monthly total.' This makes the intended use case unmistakable.

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?

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context by stating the score range and that every score includes its sample size, but it does not explicitly state that the tool is read-only or describe any limitations, side effects, or data aggregation details. This is adequate but not comprehensive.

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?

The description is two sentences with no filler. It front-loads the core meaning (process consistency dimensions, scoring), then adds the use case and the sample-size guarantee. Every sentence earns its place.

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 simple read-only tool with two optional, well-documented date parameters and no output schema, the description covers the key semantics: what is measured, the score scale, the sample-size inclusion, and the intended question. It does not elaborate on how sample size is used or how scores may be interpreted, but the essentials are present.

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 parameters are fully documented in the schema. The description adds no additional parameter meaning, which is acceptable given the high schema coverage, but it earns the baseline 3 rather than more.

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 clearly defines the tool's output: discipline metrics covering risk control, patience, consistency, and mental stability, each scored 0-100. It also explicitly states the use case ('was I disciplined' questions), which distinguishes it from sibling analytics tools.

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 description gives an explicit when-to-use signal: this is the tool for 'was I disciplined' questions. It does not name alternatives or state exclusions, so it falls short of full guidance, but the intended context is clear.

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?

No annotations are provided, so the description carries the burden. It discloses that the tool reports days traded without entries, which is a behavioral trait beyond simple retrieval. It also implies the journal is the source for psychological context. It doesn't mention pagination or max 31-day enforcement beyond the description, but the date range limit is stated.

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?

Three sentences, each earning its place: what the tool returns, why to use it, and an additional behavioral detail. Front-loaded with the resource and content, then usage guidance.

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 read-only retrieval tool with two well-documented parameters, the description is nearly complete. It explains the content, the usage context, and a key behavioral nuance. It doesn't describe the return format, but no output schema exists and the description gives enough for an agent to call it correctly.

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 description coverage is 100%, so the schema already documents both parameters. The description adds the max 31-day constraint and the inclusive date range context, which is useful but not extensive. Baseline 3 is appropriate since the schema handles parameter documentation.

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 clearly states the tool retrieves the trader's written journal for a date range, listing specific content types (plans, notes, lessons, self-assessments). It distinguishes itself from sibling tools by emphasizing it is the written journal, not metrics or breakdowns.

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 description explicitly instructs to read this before commenting on psychology or intent, and notes that numbers alone don't carry that context. It also explains the tool reports days traded without journal entries, helping the agent distinguish a missing record from a quiet week. This provides clear when-to-use guidance.

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?

With no annotations, the description carries the disclosure burden. It makes clear this is a server-side aggregation rather than a raw fetch, implies read-only behavior through 'aggregate,' and clarifies the date-range default. It doesn't mention auth or rate implications, but for a simple summary tool the behavior is adequately transparent.

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 tight sentences lead with the core action and metric list, then give the essential date behavior and efficiency tip. There is no filler or repetition of schema details.

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?

The description lists the output metrics and explains date-range semantics, which is sufficient for a simple read-only aggregation with no output schema. The only minor gap is ambiguous behavior when exactly one date is omitted.

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 schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds the important rule that omitting both dates returns all-time performance, which is not derivable from the schema alone.

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 uses a specific verb ('aggregate') and resource ('performance over a date range'), and enumerates the exact metrics included. It clearly distinguishes itself from raw trade fetching, and the rollup framing differentiates it from breakdown-style siblings.

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?

It explicitly directs the agent to 'Use this instead of fetching trades and adding them up,' naming the alternative and the reason. It also explains the all-time behavior when both dates are omitted, though it doesn't specify when breakdown siblings should be preferred.

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?

No annotations are provided, so the description carries the full burden. It discloses the aggregation scope ('Covers every trade in the range') and defines the unusual `symbolDays` metric. As a read-only analytics tool, this is sufficient behavioral context, even though it doesn't discuss permissions or rate limits.

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?

Three dense, purposeful sentences: the first defines the output metrics, the second states the use case, and the third points to a relevant follow-up. Every clause earns its place with no 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?

Without an output schema, the description compensates by naming the return metrics and the data scope. It could more explicitly describe the output shape, but an agent can infer per-playbook and per-strategy rows from the first sentence, and the schema covers the date parameters.

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 description coverage is 100%, so the baseline is 3. The description only references 'the range' and adds no format or semantic detail beyond what the schema already provides for startDate and endDate.

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 opens with a specific statement of what the tool reports: how each playbook has actually performed, with the exact metrics (trades, win rate, net P&L, fees, symbolDays). This clearly distinguishes it from sibling performance tools by its per-playbook and per-strategy focus.

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?

It explicitly gives a when-to-use framing: 'This is the tool for "is this playbook working for me"' and for checking a plan against its record. It also suggests a follow-up tool (get_symbol_reviews filtered to the playbook), though it doesn't explicitly list when-not-to-use alternatives.

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?

No annotations are provided, so the description carries the burden. It discloses that the tool returns risk settings and self-reported state, and implies a read-only operation. However, it doesn't describe what happens if no data exists for the date, whether the risk matrix is structured or freeform, or any authentication/authorization nuances. It's adequate but not rich.

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 resource and components, followed by a clear use-case statement. Every sentence earns its place; no fluff or repetition of the schema.

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 single-parameter read tool with no output schema, the description is nearly complete. It explains what data is returned and why an agent would use it. The only missing piece is behavior for missing data or edge cases, but given the simplicity, this is a minor gap.

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 description coverage is 100%, so the schema already documents the single 'date' parameter with format and description. The description adds context about what the date represents (a trading day) but doesn't add new parameter-level semantics beyond the schema. 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?

The description clearly states the tool returns the trader's own risk settings for a given day, listing specific components (daily stop loss, risk matrix, self-reported state/readiness). It also explains the intended use case (judging whether a day was traded within stated limits), which distinguishes it from sibling tools like get_daily_breakdown or get_performance_summary.

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 description provides clear context for when to use the tool: when you need to assess whether a day was traded within the trader's stated risk limits. It doesn't explicitly name alternatives or exclusions, but the purpose is specific enough that an agent can infer when to choose it over siblings. A small gap is not mentioning when NOT to use it (e.g., for performance metrics).

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?

There are no annotations, so the description carries the burden. It usefully discloses the output composition (grouped results plus risk matrix), but it doesn't explicitly state that this is a read-only operation, describe date-range behavior, or mention any edge cases in grading/grouping.

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 focused sentences with no filler. The first sentence explains what the tool returns, and the second states the intended analytical purpose. Every sentence earns its place.

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 simple two-parameter report tool, the description covers the purpose, output composition, grade range, and use case. Since there is no output schema, a bit more detail about exact return fields would help, but nothing essential is missing for correct invocation.

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?

Input schema coverage is 100% with both startDate and endDate documented, so the baseline is 3. The description adds no extra parameter semantics, but the schema already fully explains the two parameters.

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 clearly states the output: performance results grouped by setup grade, including the grade range (A+ to C) and the associated risk matrix. It does not explicitly name sibling alternatives, but the unique grouping by grade and inclusion of the risk matrix set it apart from tools like get_playbook_performance or get_symbol_breakdown.

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?

It provides an explicit use case: checking whether the sizing rules match reality by seeing whether the grades the trader sizes up on actually pay. It doesn't state when NOT to use it or name alternative tools, so it stops short of full routing guidance.

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

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

With no annotations, the description carries the behavioral disclosure burden. It explains that the tool covers every trade in the range, aggregates options under their underlying (NVDA shares and calls merge into one row), and returns specific metrics. This goes well beyond a generic 'returns breakdown' statement, though it does not discuss auth, rate limits, or ordering behavior.

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?

Three sentences deliver the result, the use case, the sibling distinction, and the key aggregation rule. Every sentence earns its place, and the most important scoping details are front-loaded.

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 an aggregation/read tool with no annotations or output schema, the description is quite complete: it names the output metrics, date range, inclusion scope, and the unusual option-underlying behavior. Minor gaps like default date handling and exact ordering are not critical for correct invocation.

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 schema already documents limit, startDate, and endDate. The description adds only general context like 'over a period' and 'most-traded', which is useful but not a substantial supplement to the parameter documentation.

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 operation: trades grouped by ticker with concrete metrics (count, net P&L, fees, win rate). It names the resource (ticker breakdown over a period) and explicitly distinguishes itself from get_trades, so an agent can select it without opening the schema.

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?

It gives the exact question this tool answers ('which symbols do I trade most / lose most on') and explains when not to use it by contrasting with get_trades, which only returns the newest rows. This is explicit when/when-not guidance.

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?

With no annotations provided, the description carries the behavioral burden and does a strong job: it explains aggregation granularity, grouping by playbook, planned vs assigned playbooks, the 92-day range limit, and the non-obvious rule that options belong to the symbol-day of their underlying. It does not cover response ordering or pagination behavior, but the key behavioral quirks are disclosed.

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?

Three dense sentences with no repetition or filler; each sentence adds distinct value: resource definition, content, and usage constraints. The structure is logical and front-loads the core unit before moving to filters and edge-case semantics.

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 tool with no output schema and no annotations, the description covers the important selection and invocation context: what each review entry contains, the review scores, mistakes, reflection, date range, and option handling. It is slightly light on default sorting/return shape, but limit and date semantics are already in the schema.

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 description coverage is 100%, so the baseline is 3; the schema already documents startDate, endDate, limit, symbol, and playbookId. The description adds useful context around playbookId and grouping, but does not materially extend what the parameter descriptions already state.

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 a specific resource and what it returns: 'One entry per symbol the trader traded on each day,' followed by the entry's contents. It also distinguishes the tool from trade-level views with 'setup by setup rather than trade by trade,' making it clearly different from siblings like get_trades.

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?

It explicitly states when to use the tool: 'Use it to judge a playbook, or a session, setup by setup rather than trade by trade.' It also gives filtering guidance via symbol or playbookId, though it does not explicitly list exclusions or name alternative tools for cases where this tool would not apply.

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?

With no annotations, the description carries the behavioral burden. It clearly states the returned content categories and implies read-only via 'get', but it does not mention error handling, missing-trade behavior, or return envelope. This is moderate transparency, not a full disclosure.

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 with no filler; the data scope is front-loaded and the usage hint follows. Every sentence adds meaningful information.

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 single-parameter read tool with no output schema, the description names the trade scope, the data categories included, and the exact entry point after get_trades. Nothing needed for a correct first call is missing, though it is light on error semantics.

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 only parameter, tradeId, is fully described in the schema as 'Trade id from get_trades', and the description reinforces this with 'Use after get_trades'. The description adds no extra syntax or format details, so the baseline 3 applies when schema coverage is complete.

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 opens with 'Everything recorded about one trade' and enumerates fills (execution log), playbook notes, and rule adherence, giving a specific verb and resource. It also distinguishes this from get_trades by positioning it as the per-trade drill-down, so an agent can tell it apart.

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 line 'Use after get_trades to dig into a specific trade' provides explicit sequencing and context for when this tool is appropriate. It does not explicitly explain exclusions or alternatives, but the guidance is clear enough for selection among sibling tools.

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?

With no annotations provided, the description carries the full burden and does so thoroughly: it discloses default page size (20), max limit (50), ordering, totals computed over all matching trades, omission of fields with no value, and that rows represent executions rather than setups. This is far beyond a minimal 'List trades' statement.

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?

The description is four dense sentences with zero filler. It front-loads the core operation and ordering, then efficiently covers pagination, totals, field omission, and row granularity without repeating schema content.

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 9-parameter list tool with no output schema and no annotations, this description is remarkably complete. It covers purpose, filter semantics, pagination, aggregate totals, missing-value behavior, row granularity, and names the related review tool. Row-level field names and empty-result behavior are secondary for tool selection and invocation.

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 input schema already documents most parameters with 89% coverage, so the baseline is 3. The description adds meaningful parameter-related context by stating the default `limit` of 20 and max of 50, and by framing the filters as matching the Trades page behavior.

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 opens with 'Individual trades, newest first', giving a specific verb, resource, and ordering. It clearly distinguishes this from get_symbol_reviews by explaining that rows are executions and that multiple trades in one symbol/day belong to a single setup reviewed elsewhere.

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?

It explicitly routes aggregate performance questions to the returned `totals` rather than summing rows, and points to get_symbol_reviews for setup-level review. It doesn't exhaustively compare against all sibling analytics tools, but it provides clear contextual guidance for the main alternatives.

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?

No annotations are provided, so the description carries the full burden for behavioral disclosure. It only gives conceptual context about playbook strategies and does not state that this is a read-only listing, whether any filtering applies, or what the response contains.

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?

The description is only two short sentences with no filler and the most important noun phrase is front-loaded. It could use an action verb, but conciseness is not the problem.

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 simple zero-parameter list tool, the description provides enough subject matter context for an agent to infer it returns playbook strategies. However, since there is no output schema and no annotations, the description should more explicitly state the return value and any list scope; it currently only describes the conceptual role of playbooks.

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 has zero parameters and the schema has full coverage with no arguments to document. The baseline for a parameterless tool is 4, and the description adds no conflicting or missing argument details.

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 identifies the resource ('playbook strategies') and connects it to list_setups, but it never states an explicit verb such as 'list' or 'retrieve'. The purpose is clear only by combining the title with the noun phrase, so it is not a tautology but is vaguer than a direct action statement.

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 guidance on when to choose this tool over siblings like get_playbook_performance or list_setups. The mention that it works 'together with list_setups' hints at a relationship but does not provide selection criteria or exclusions.

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?

With no annotations, the description bears the burden of behavioral disclosure. It usefully explains what the returned items are and their purpose, but does not state access scope, response shape, or filtering behavior. For a simple list operation this is minimally adequate but not thorough.

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 short sentences with no filler. The first sentence front-loads the resource, and the second adds actionable usage guidance. Every sentence earns its place.

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?

The definition is adequate for a parameterless list tool, but it does not distinguish setups from playbooks, and list_playbooks is a sibling. An agent choosing between these tools may need additional context to select correctly.

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 are zero parameters, so the baseline is 4. The description still adds semantic value by clarifying that the setups are the trader's own definitions, which helps an agent reason about what the tool represents.

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 resource: the trader's own setup definitions and their rule checklists. This is clear, though the verb 'list' is only supplied by the name/title, and it does not explicitly differentiate setups from the sibling list_playbooks.

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 description gives concrete usage context: grade trades against these setups rather than generic trading advice. It does not name alternative sibling tools or state when to avoid using it, so it stops short of a full when/when-not guide.

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?

With no annotations, the description carries the full burden. It discloses that each tag includes a count of trades and that ids are returned (implied by 'Pass their ids'). However, it does not mention the exact return format, ordering, or any limitations such as pagination. For a simple read-only list, this is adequate but not exhaustive.

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 packs the core purpose, the returned data (tags with counts), and the primary use case (filtering trades). Every word earns its place, and the most important information is front-loaded.

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 list tool with no output schema and no annotations, the description covers the essential aspects: what it returns, the counts, and how to consume the ids. It stops short of specifying the exact JSON structure or any edge cases, but for this simple tool it is sufficiently complete 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.

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

The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific semantics since none exist. The schema is empty, and the description doesn't need to compensate for any undocumented params.

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 clearly states the tool lists tags with counts, and explicitly ties them to trade filtering via get_trades. The verb 'list' plus the specific resource 'tags' with the additional detail of trade counts and how to use the ids makes it unambiguous and distinct from siblings.

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 description provides a concrete usage pattern: pass the returned ids to get_trades as tagIds to replicate the Trades page filter. This gives clear context on when to use the tool. It does not explicitly mention exclusions or alternatives, but the usage guidance is direct and actionable.

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?

With no annotations provided, the description carries the behavioral burden. It discloses the search scope, the kind of text matched, and the response shape and ordering ('dated snippets, newest first'). Minor details like case-sensitivity and empty-result behavior are unstated, but this is acceptable for a simple read-oriented search.

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?

Three sentences carry all essential information: purpose, scope, example use, sibling contrast, and return ordering. There is no filler or redundant restatement of the title, and the main action is front-loaded.

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 simple two-parameter search with no output schema, the description covers what is searched, why to use it, which sibling to avoid, and what the results look like. It omits only explicit limit/default behavior and edge-case handling, which are not essential to invoke the tool correctly.

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 schema documents 'query' with an example but leaves 'limit' without semantic description, so coverage is only partial. The description clarifies that the query targets written journal text, but it does not explain the 'limit' parameter's behavior or default. This is adequate but leaves one parameter underspecified.

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 opens with a specific verb ('Find') and resource ('journal days whose written text mentions something'), and clearly defines the cross-history scope. It also enumerates content categories (plans, reflections, focus goals, risk notes), making it readily distinguishable from sibling tools like get_journal.

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?

It explicitly states when to use this tool: for recurring-behaviour questions such as 'when do I mention overtrading?'. It also names the alternative to avoid ('instead of walking month by month with get_journal'), giving clear routing guidance.

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?

With no annotations, the description carries the full burden, and it goes well beyond 'writes a journal note': it mandates using the trader's own words, tells the agent to ask rather than invent content, states that no other trade data can be changed, and explains the refusal and overwrite contract. This is strong behavioral disclosure for safe invocation.

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?

Three focused sentences with the purpose front-loaded and every subsequent clause adding behavioral or decision-relevant information. The length is justified by the refusal/overwrite logic, and there is no 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 5-parameter mutating tool with no output schema and no annotations, the description is remarkably complete: it covers what to write, where, whose words, what to do on conflict, and how to overwrite. Any remaining gaps, such as confirmation response shape, are minor and inferable.

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 schema already documents all five parameters, so the baseline is 3; the description adds meaningful semantics around section (the three fields), content (own words, markdown), and overwrite (only after user approval, otherwise refused). Writing this in natural language genuinely helps an agent decide parameter values, though date/symbol details remain schema-only.

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?

Description opens with a concrete action on a clear resource — 'Record what the trader tells you into their journal' — and enumerates the three distinct sections it supports. It is easy to distinguish from the read/search/list sibling tools, and 'Nothing else about a trade can be changed from here' further scopes the write 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?

It clearly frames the tool's role as capturing the trader's words for one of three journal fields, and the refusal/overwrite flow tells the agent how to behave when content already exists. It does not explicitly say 'use get_journal/search_journal when the user only wants to read,' so read-vs-write routing guidance is implicit rather than explicit.

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