ru_page
RU-страница URL -> LLM-ready Markdown. $0.01 USDC
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| args | Yes | ||
| extra | Yes |
RU-страница URL -> LLM-ready Markdown. $0.01 USDC
| Name | Required | Description | Default |
|---|---|---|---|
| args | Yes | ||
| extra | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There is no disclosure of side effects, permissions, error handling, or outcomes beyond the conversion. The cost is mentioned but no other behavioral aspects are addressed, and annotations are absent.
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 brief and to the point, which is concise. However, it is not well-structured; it uses an arrow notation and includes pricing but omits essential usage details, so it balances brevity with incompleteness.
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 tool lacks context about how it fits with sibling tools (inn_lookup, ru_search). No explanation of input requirements, output format, or differences from alternatives, leaving the tool's role ambiguous.
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 parameters 'args' and 'extra' are generic objects with no description of expected contents. The description does not explain what fields are needed (e.g., URL, options), making it impossible to invoke correctly 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 clearly indicates the tool converts a Russian page URL to LLM-ready Markdown, and notes the cost. The arrow mapping and cost info make the purpose reasonably clear, though it could be phrased more explicitly as a verb phrase.
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?
No guidance is provided on when to use this tool versus alternatives like inn_lookup or ru_search. The description lacks any context about typical use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The server splits cleanly into INN, Pochta, and RU research domains, and most tools have a distinct purpose. The main confusion risk is pochta_delivery_time vs pochta_tariff, since tariff also includes delivery time, and pochta_offices vs pochta_zip when both are used for index lookup.
Names are consistently lowercase snake_case with clear domain prefixes: inn_, pochta_, ru_. However, the naming is not perfectly uniform because some names are verbs like lookup/track/search, while others are nouns like offices/tariff/zip.
11 tools is a reasonable scope for three covered areas: Russian company lookup, postal services, and Cyrillic search/research. The count is not excessive, but there is enough similarity between a few tools that the set could be slightly trimmed without losing capability.
The Pochta domain is well covered: address normalization, tariffs, delivery times, offices, and tracking are all present. For a read-only RU data API, no critical dead ends are obvious, though the broad 'RU data' scope could plausibly include more data sources beyond INN, postal, and web search.