AlexCredit - Kazakhstan Microfinance Catalog

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

There are no annotations, so the description carries full responsibility. It discloses the not-found behavior (isError with «Не найдено»), explains the semantics of the top field, and explicitly instructs agents to use the precomputed tops instead of performing their own sorting. This is substantial behavioral context beyond the schema.

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 front-loaded with the core purpose and then gives a dense but purposeful breakdown of return fields and usage routing. It is long, but every clause contributes meaning, and the length is justified because there is no output schema to carry return-structure documentation.

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?

With one required parameter, no output schema, and no annotations, the description covers the response fields, error handling, sorting semantics, and sibling-tool routing. Nothing an agent needs in order to select and invoke this tool correctly is missing.

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 single slug parameter has 100% schema description coverage, including format, an example URL, and the instruction to take the slug from list_categories. The tool description itself adds no additional parameter detail, so the baseline of 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 opens with a precise verb+resource statement: «Одна тематическая подборка каталога по slug», then lists the returned fields. It clearly distinguishes this tool from siblings by naming the alternatives for other use cases, e.g., search_offers for filtering by amount/term and get_mfo for full MFO cards.

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 states when to use the tool: «Используйте, когда вопрос совпадает с готовой подборкой», gives a concrete example («микрокредиты пенсионерам»), and routes to specific sibling tools for adjacent cases. This leaves no ambiguity about selection.

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 present, so the description carries the full burden. It discloses that the legal context is 'as of the date of the last editorial review' and explicitly instructs the agent to verify reviewed_at for currency. It also states there are no parameters and enumerates the returned fields. It does not explicitly label the tool as read-only, but the informational nature is strongly implied by the content and return values.

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 front-loaded with the primary domain, then states parameter absence, describes return fields, and concludes with usage guidance and the sibling pointer. It is dense but information-rich; the long list of specific legal facts (46%, 0.3%, 179%, 45 МРП, 45 days) helps the agent judge scope, though the single-block format could be more scannable.

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 parameterless tool with no output schema, the description is complete: it lists every returned field (country, reviewed_at, summary, details_markdown, source), explains the date-relevance caveat, and specifies the intended use cases. The explicit pointer to get_mfo rounds out the contextual picture, so nothing critical is missing.

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 description explicitly says 'Параметров нет' (no parameters). Since the schema coverage for the empty parameter set is trivially 100%, the baseline for 0 parameters is 4, and no further parameter documentation is needed.

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 precisely that the tool provides the legal context of microcredits in Kazakhstan and enumerates the concrete topics it covers: regulator ARRFR, licensing, maximum GESV of 46%, the special regime for short microcredits, MPP value, voluntary refusal via eGov, and signs of illegal lenders. It also explicitly contrasts itself with get_mfo, which provides data on a specific MFO, so an agent can distinguish it from siblings immediately.

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 direct usage instructions: 'Use for questions about the legality of rates, limits, and borrower protection; data on a specific MFO — get_mfo.' This names the exact use cases and the sibling alternative, leaving no ambiguity about when to choose this tool vs. 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?

With no annotations, the description carries the full disclosure burden. It explains error behavior for a non-existent slug, clarifies the null meaning of first_loan_zero_percent, highlights the regulator registry link as a primary source, and lists the returned data areas. It does not mention permissions or side effects, but it is a read-style endpoint and no mutation is suggested.

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 long but information-dense, and every listed item contributes because there is no output schema to document return values. It is front-loaded with the core purpose, then expands into well-grouped return categories. Slightly run-on, but justified by the complexity.

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?

Given the absence of an output schema, the description does substantial work by enumerating the main result categories, error behavior, null semantics, and source-attribution requirement. It could be more complete with exact field types or value formats, but it is sufficient for an agent to select and 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 already documents the sole parameter slug at 100% coverage, including its format, example, and instruction to derive it from list_mfo, search_offers, or get_category. The description only restates 'по slug' and adds no additional meaning beyond the schema, so the 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 identifies a precise resource and scope: 'Полная карточка одной МФО по slug'. It clearly differentiates this from list/search endpoints and explicitly points to get_reviews for reviews, so the agent knows exactly what this tool does and what it is not.

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 states that reviews should be fetched through get_reviews, which is an explicit alternative. It also implies use when a complete single-MFO card is needed, though it does not explicitly contrast with list_mfo, get_category, or search_offers in the description itself.

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 so well: it discloses that only public site-published reviews are returned, the new-to-old sort order, the empty-reviews/total=0 case, and the isError response for a nonexistent slug. These are exactly the behavioral quirks an agent needs to interpret results correctly.

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 purpose is front-loaded and every sentence carries information: scope, return shape, edge cases, and routing. It is somewhat dense, but not padded; the small structural loss is from packing many facts into one paragraph.

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?

There is no output schema, so the description must document the return shape and result semantics. It lists all returned fields, explains total/reviews behavior, defines rating scale, covers the not-found error, and gives a selection rule—complete for a read-only lookup tool.

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 baseline is 3. The description mentions slug and review count only in passing; the schema already defines slug provenance and the limit range/default, so no compensation is required.

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: returns the latest public borrower reviews for one MFO by slug. It also names the distinguishing output fields and points to get_mfo for summary ratings, so an agent can tell this tool apart from the sibling suite.

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 it for reputation and customer-experience questions and routes summary rating/conditions to get_mfo. It does not enumerate exclusions against every sibling, but the main alternative is named.

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 of behavioral disclosure. It states that the tool takes no parameters, returns a sections[] structure, and clarifies that the 'criterion' field is the actual MFO selection sign without promising approval. It does not mention ordering or pagination, but for a zero-parameter listing the key behavioral details are 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?

The description is front-loaded with purpose, then provides the return structure and usage guidance, all without redundant filler. Every clause earns its place, and the text remains compact despite covering grouping, output fields, and sibling routing.

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 tool with no output schema, the description is complete: it specifies the return structure (sections[] with categories containing title, slug, criterion, canonical_url), defines the criterion field, and gives concrete usage scenarios with sibling routing. Nothing an agent needs to call it correctly is missing.

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 description explicitly states 'Параметров нет' (no parameters), eliminating any risk of an agent inventing arguments. The empty input schema is consistent with this, and no further parameter semantics are needed.

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 clear statement of what the tool returns: all thematic catalog collections grouped by sections, enumerating the grouping dimensions (popular, by method, by amount/term, by borrower category, etc.). It uses a specific verb ('list') and resource ('categories'), and distinguishes itself from get_category by framing itself as the way to obtain collection slugs.

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 gives use cases: to find a slug for get_category or to suggest a page matching a user's situation, with a concrete example ('microcredit with bad credit history'). It also names the alternative search_offers when the need is filtering by specific amount and term, making the routing explicit.

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 and handles it well: it discloses the response shape (total and mfos array with fields), the sorting rule, and the important nuance that rating_average is absent when there are no reviews and this is not a low rating. The canonical_url attribution note adds further behavioral context.

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 compact and information-dense: purpose and return structure come first, followed by usage routing and attribution. Every sentence adds value; there is no repetition or 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?

Given the absence of an output schema and annotations, the description compensates by specifying the full response fields, sort order, missing-field semantics, and sibling routing. Combined with the fully described limit parameter, an agent has everything needed to call and interpret 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 only parameter, limit, is fully described inside the input schema, including range, default, and the fact that total always contains the full count. The description itself adds no extra parameter-level detail, so the baseline 3 applies due to 100% schema description coverage.

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 all MFIs in the AlexCredit Kazakhstan catalog with borrower ratings, and enumerates the exact returned fields and sort order. It also names get_mfo and search_offers as alternatives, so the agent can distinguish it from 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?

It explicitly says to use this tool for market overview and obtaining slugs, and directs to get_mfo for specific MFI terms and search_offers for amount/term matching. This is a crisp when-to-use vs. alternatives statement.

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 of behavioral disclosure. It explains filtering behavior, ordering by rating, the meaning of total and truncated, the shape and semantics of offers, and the null interpretation for first_loan_zero_percent. This is detailed and 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?

The description is information-dense without being bloated. It opens with the core purpose, then methodically covers filtering logic, ordering, output fields, and edge-case 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?

Despite having no output schema and no annotations, the description fully compensates by listing the exact response fields, explaining truncated behavior, and clarifying the empty-offers case. An agent has enough information to call the tool correctly and interpret its results.

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 covers all parameters, so the baseline is 3. The description adds value by explaining how parameters affect results: MFOs are returned only if the requested amount and term fall within their ranges, and first_loan_zero filters only confirmed zero-rate offers. This goes beyond the basic schema descriptions.

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 identifies the tool's function: selecting microfinance organizations (МФО) based on loan amount, term, and first-loan zero-rate flag. It is specific and understandable, but it does not explicitly differentiate this tool from siblings like list_mfo or get_mfo.

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 explicit use context: "Используйте для вопросов «где взять N тенге на M дней»." It also provides practical advice for edge cases, such as increasing limit when truncated is true and relaxing filters when no offers match. It does not, however, state when to prefer a sibling tool.

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