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

With no annotations, the description carries the full burden. It does disclose useful behavior: execution reads straight off memory-mapped columns, and the result is byte-equivalent to replay_orders. It says nothing about the fills_csv input, error behavior for a missing/mismatched dataset, or cost of a replay run.

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 compact sentences, front-loaded with the Pro marker and the core action, with no filler. The parenthetical column list is dense but earns its place by defining the CSV format.

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 three-parameter tool with no output schema, the description covers the essential call path end-to-end: where the dataset name comes from and what the output will look like (same as replay_orders). The main gap is the optional fills_csv parameter, whose role is never explained.

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 0%, so the description must compensate, and it partially does: orders_csv is defined by its columns (time, side, qty) and dataset by its provenance and naming convention. The third parameter, fills_csv, is never mentioned and its purpose (optional fill data?) is completely undocumented.

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 verb and resource: replay an orders CSV (with its time/side/qty shape) against a dataset produced by pro_sync, and notes the output is identical to replay_orders. That distinguishes it from the plain replay_orders sibling, though the distinguishing axis (Pro / memory-mapped performance) is only implied rather than spelled out.

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 supplies a real prerequisite: the dataset must be the name returned by pro_sync, with a concrete example ("BTCUSDT-aggTrades"). But it never says when to pick pro_replay over replay_orders despite the two producing the same output, so the choice between siblings is left to inference.

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 implies a read-only license/installation check and reveals a dependency relationship, which is useful behavioral context. However, it does not describe the return value (e.g., boolean vs. license details), whether errors are raised, or what a negative result means, leaving gaps for a tool with zero annotation coverage.

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, front-loaded with the core purpose and followed by the dependency fact. Every sentence 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?

For a zero-parameter status-check tool with no output schema, the description covers the essential purpose and dependency. The main remaining gap is that without an output schema, the description could state what the status result looks like. Otherwise it is complete enough to call 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 takes zero parameters, so there are no parameter semantics to document and the baseline is 4. The description correctly does not spend words on 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 states a specific resource and condition: whether Deptho Pro is 'installed and licensed here', which is far more precise than a tautology. It also links to siblings pro_sync and pro_replay, helping the agent place it in the toolset. It lacks an explicit verb like 'check' or 'query', but the intent is unambiguous.

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?

By stating 'pro_sync and pro_replay need it', the description gives clear context for when this tool matters: as a prerequisite check before those two tools. It does not explicitly say 'call this first' or define what to do if the status is negative, but the guidance is concrete.

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 does well: it discloses the data source (Binance public archive, so no credentials implied), the write target (one local memory-mapped dataset named "<SYMBOL>-aggTrades"), and idempotent behavior for already-present days. It omits operational traits such as rate limits, handling of future or invalid date ranges, and whether the call is long-running.

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, front-loaded with the action and range semantics, and the incremental-download and return-value facts each earn their place. Minor waste in the '(Deptho Pro)' branding prefix and a long first sentence that packs source, range, and target together.

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 three-required-parameter ingestion tool with no output schema, the description covers source, destination, idempotency, and the return value (dataset name and size). What remains missing is modest: symbol formatting and failure/latency behavior for large date ranges.

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 0%, so the description must compensate for all three parameters. It adds real meaning for start/end (YYYY-MM-DD format, inclusive range) and implies symbol feeds the dataset name, but never states the symbol convention (e.g. BTCUSDT vs BTC-USDT) or the units the archive uses for USD-M contracts.

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 verb and resource: download Binance USD-M daily trade files from the public archive and append them into a local memory-mapped dataset. The scope (one symbol, one dataset) is concrete enough that an agent can distinguish it from replay_orders, pro_replay, pro_status and tearsheet without inspecting schemas.

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: the incremental note ('Days already on disk aren't downloaded again') signals this is the data-acquisition step that precedes replay/tearsheet work. There is no explicit when-to-use statement, no exclusion, and no named alternative such as pro_status for checking what is already local.

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 mostly succeeds: it discloses the side effect (writes one row per filled order to fills_csv, defaulting next to the orders file), the return value (a summary), and the slippage convention including its sign. It omits overwrite behavior for fills_csv and any error/partial-fill semantics.

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?

Purpose is front-loaded, then each parameter is addressed on its own line, followed by output behavior. It is dense but every clause carries information; only the output-format sentence runs long and mixes several facts.

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 three-parameter file-processing tool with no output schema, the description covers inputs, output destination, row schema, and slippage sign convention. Remaining gaps are minor: what the returned summary contains and whether an existing fills_csv is overwritten.

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 0%, so the description must compensate, and it fully does: orders_csv's accepted time formats, side encodings, and qty units; trades_file's accepted providers and compression formats; and fills_csv's default destination. All three parameters gain meaning unavailable in the bare schema.

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 first sentence states a specific verb and resource: replay a backtest's market orders against subsequent trades. An agent knows exactly what the operation does. It does not, however, distinguish itself from siblings such as pro_replay, so the agent must infer the boundary.

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 data-shape requirements: the trades file must cover the orders' period, and orders must be market orders. There is no explicit when-to-use guidance and no statement of when NOT to use it, nor any pointer to a sibling alternative.

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 does substantial work: it discloses that the tool writes one self-contained HTML file, gives the default output location, and says it returns the path and headline numbers. It does not mention overwrite behavior if report_html already exists or other file-writing side effects.

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 main purpose is front-loaded and the parameter details are dense but relevant. The single paragraph is longer than ideal for scanning, but every sentence contributes useful semantics 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 six-parameter file-generating tool with no output schema, the description covers the critical data inputs and return artifact well. It is slightly incomplete on title/report_html parameter semantics and overwrite behavior, but overall it is sufficient to call 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?

Schema description coverage is 0%, so the description must carry parameter meaning. It richly defines bars_csv columns and timing, funding_csv sign convention, fee/slippage units, and the default report path. It leaves title and the explicit meaning of report_html mostly implicit.

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 and resource: writing an HTML tearsheet for a strategy and returning its path plus headline numbers. It is clearly distinct from the sibling replay/status tools, though it does not explicitly name those alternatives.

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 purpose, and there is one useful routing hint: measure slippage with replay_orders rather than guessing. However, it does not state when to use this tool versus pro_replay or other siblings, nor any exclusions.

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