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parsee_list_groups

Группы пользователя в ПАРСИ: мониторинг цен, сравнение с конкурентами и СПП. Возвращает название, раздел, число товаров и время последней проверки. С этого стоит начинать: дальше по названию группы работают остальные инструменты.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the burden, and it does disclose that the tool returns a set of group fields, implying a read operation. However, it does not explicitly state that it is non-mutating, nor describe what happens with the 'section' filter or edge cases. The output schema covers return structure, so this partial transparency is 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.

Conciseness5/5

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

Three short sentences convey purpose, return fields, and workflow placement with no filler. The most important guidance (start here) is at the end but the content is compact and every clause earns its place.

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

Completeness4/5

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

For a simple list tool with one optional parameter and an output schema, the description covers the domain, return data, and usage context. It does not explicitly explain how the optional 'section' filter behaves, but the enum values plus domain hints make the definition workable; a slightly clearer parameter mapping would make it complete.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must add meaning to the 'section' parameter. It maps the enum values to domain categories ('мониторинг цен', 'сравнение с конкурентами', 'СПП'), which is useful, but it never explicitly ties this to the parameter or explains the default/null behavior. This is partial compensation for the missing schema descriptions.

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

Purpose4/5

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

The description identifies a specific resource—user groups in PARSI—and states what the tool returns: group name, section, product count, and last check time. It is distinguishable from the sibling group tools mainly through the phrase about starting here and other tools operating by group name, though it relies on the verb 'Возвращает' rather than an explicit verb like 'list'.

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

Usage Guidelines4/5

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

The sentence 'С этого стоит начинать' gives explicit guidance to use this tool first, and 'дальше по названию группы работают остальные инструменты' clarifies the workflow context. It doesn't list formal exclusions or alternatives, but for a read/list entry point that is sufficient.

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

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TDQS

A3.5/5.0
Disambiguation4/5

Most tools map cleanly to distinct resource/action pairs, and the descriptions explicitly point to complementary tools (e.g., browser_search → parse_urls → wait → get_results). The main ambiguity is the help/cloud_manual duplication and the similar price/result output of get_prices versus get_results.

Naming Consistency3/5

All tools share the parsee_ prefix and snake_case, and most follow a verb_noun pattern (create_group, get_results, set_region). However, several tools are noun-like (status, help, price_history, spp_changes, wb_cabinet) and browser_search reverses the verb_noun order, so the pattern is readable but not uniform.

Tool Count2/5

32 tools is a large surface for an MCP server, even for a broad parsing/monitoring domain. The count feels inflated because multiple cloud/help/documentation tools and several similar data-retrieval tools could be consolidated.

Completeness5/5

The tool set covers the full workflow: search/discovery, collection, task lifecycle, group management, analytics/history, scheduling, settings, export, and seller-cabinet integration. There are no obvious dead ends, and every operation has the supporting tool needed to act on its output.