hobo.by Curds Laboratory

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

No annotations are supplied, so the description carries the full burden. It does disclose the shape of the result (safety/harm verdict plus a list of curd products containing the substance) and implies a read-only reference lookup, but it says nothing about error behavior for unrecognized codes, data freshness, or the language/format of the response.

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 that states the resource, the payload, and the deliverable with no filler. The parenthetical example list is slightly redundant with the schema examples but still earns its place by showing both code and ingredient-name forms.

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 lookup tool with no output schema and no annotations, the description covers what is needed to call it: what to pass (a code or ingredient name) and what comes back (a safety assessment and a list of curd snacks). Only edge-case behavior and output format details are missing, which is acceptable at this complexity.

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 single 'query' parameter is already documented with its own examples. The description reinforces that both E-codes and plain names are accepted and adds 'palm kernel oil' as an example beyond the schema's additive examples, marginally widening the understood input space but adding little new semantics.

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 (a food additive/ingredient identified by E-code or name) and a specific output (harm/safety assessment plus the list of curd snacks containing it), so the agent knows exactly what the tool produces. It does not explicitly contrast itself with the curd-centric siblings (search_curds, get_curd_dossier), though the additive-centric framing makes the distinction reasonably inferable.

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 examples (E476, E322, E202, palm kernel oil) suggest the tool is for looking up a single additive or ingredient. There is no statement of when to prefer it over siblings, no prerequisites, and no guidance on what happens with an unknown or ambiguous name.

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 disclose the computed output surface: price per kg delta, protein per ruble, KBJU per portion and per 100g, glaze type, sugar presence, lab rating. 'На лету' implies a non-mutating on-the-fly computation, but error behavior for unknown or ambiguous curd names is not covered.

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 front-loaded sentence with the verb and scope first, then the metric list. The enumeration is long but each item names a distinct computed comparison, so it 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?

No output schema exists, so the description must sketch return content, and it does so thoroughly across price, protein efficiency, nutrition, glaze authenticity, sugar and lab rating. Only edge-case handling (item not found, duplicate inputs) is left unspecified.

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 single parameter is fully documented, including case-insensitive matching and an example. The description's '2 to 5' count adds a mild constraint already implied but not stated in the schema, so 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?

Opens with a specific verb and resource ('Сравнить от 2 до 5 глазированных сырков на лету') and then enumerates exactly what the comparison computes. It is clearly distinguishable from the siblings, which search, rank, profile single items, or analyze E-codes rather than comparing a set.

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 '2 to 5 items' range is an implicit usage constraint, but nothing states when to prefer this over get_curd_dossier or get_top_curds. Usage must be inferred from the list of computed metrics rather than from explicit 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?

No annotations are supplied, so the description carries the full burden. The verb 'Получить' implies a read-only lookup, and the enumerated fields effectively describe the return payload in the absence of an output schema. However, it says nothing about not-found behavior, identifier matching rules, or any auth/rate constraints.

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 the action first and the payoff list after; the enumeration earns its place by substituting for a missing output schema. Slightly dense but free of 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 one-parameter read lookup with no output schema and no annotations, the description is close to sufficient: it spells out the returned fields so an agent knows what to expect. The remaining gap is error/not-found behavior and how the identifier is resolved.

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% for the single 'identifier' parameter, including examples of both slug and human-readable forms, so the schema does the heavy lifting. The description only implies that the identifier selects one specific product and adds no syntax or matching detail beyond the 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 description pairs a specific verb ('Получить') with a specific resource ('полное экспертное досье на конкретный сырок') and then enumerates the contents of that dossier, so an agent knows exactly what comes back. It does not explicitly contrast itself with siblings like search_curds or analyze_e_code, so it stops short of a 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?

Usage is only implied: the phrase 'на конкретный сырок' signals that this is for a single, already-identified product, whereas the sibling search_curds would be for discovery. There is no explicit when-to-use or when-not-to-use rule, and no guidance on what to do when the identifier is unknown or ambiguous.

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 supplied, so the description carries the full behavioral burden, yet it discloses almost nothing beyond the verb: no sorting rule beyond 'топ', no data source or lab-rating context, no note on what happens when a category has fewer entries than the limit, and no return shape. Only the read-only nature is inferable from 'Получить'.

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 or redundancy. It is efficient, though its brevity borders on under-specification given the absent annotations.

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 read tool this covers the core intent, and the schema covers both parameters fully. With no output schema and no annotations, however, the description should have said something about what a 'top' result contains (ranking basis, fields returned), which it omits.

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 category enum is documented in detail in the schema itself, including each thematic category's meaning. The description's phrase 'тематических категориях' merely echoes the schema and adds no new meaning, so the 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 gives a specific verb and resource ('Получить топ лучших сырков') plus a scope ('в тематических категориях Беларуси'), so an agent knows this returns ranked curd lists per category. It does not, however, distinguish itself from siblings like search_curds or compare_curds, which could also surface curds.

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 use this tool versus search_curds, compare_curds, or get_curd_dossier, and no stated prerequisites or exclusions. The category-based framing only weakly implies that this is the tool for browsing ranked lists rather than free-text lookup or side-by-side comparison.

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 behavioral burden; it discloses the corpus size (119 positions) and that search is over a fixed database, which is genuinely useful. It does not say whether filters combine with AND semantics, what happens when no parameters are passed, or whether results are read-only, leaving notable gaps for a 9-parameter 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 with the capability stated first and the supported filter axes second; no filler, no repetition of the tool name, and it reads 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 9-parameter search tool with no annotations and no output schema, the description covers purpose and filter breadth but omits default-result behavior, filter combination logic, and error/empty-result handling. Adequate to call the tool, but not complete enough to predict its behavior precisely.

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 every parameter is already documented in the schema, including enum meanings and units (BYN, per 100 g). The description largely restates those filter categories ('настоящий шоколад', 'отсутствие сахара', 'калории', 'белок', 'цена') without adding format details such as how query and brand interact. Baseline 3 applies when the schema does the heavy lifting.

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 verb and resource ('поиск и фильтрация сырков') and scopes it to a 119-item database, so the agent immediately knows this is the retrieval tool. It does not explicitly distinguish itself from siblings like compare_curds or get_top_curds, so it stops short of a 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?

Usable filters are listed (chocolate glaze, no sugar, calories, protein, price), which implies the situations the tool serves, but there is no explicit statement of when to prefer it over compare_curds or get_top_curds, nor any when-not guidance. Usage is inferable rather than stated.

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