catalog

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

With no annotations, the description carries the burden of behavioral disclosure. It adds context about the data source (mfoxa.com.ua) and an attribution obligation (use canonical_url), and describes the response composition. However, it does not explicitly state read-only behavior or side effects, though the 'get' prefix implies a safe read.

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 a single, information-dense sentence that covers purpose, content, data source, and attribution. Every clause adds value, with no wasted words or repetition of the title.

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 compensates by listing key response elements (selection criterion, sorting, MFO offers table with conditions, canonical_url) and data provenance. The reference to the '.md version' may be obscure, but the description still gives a workable mental model. It lacks pagination or error details, which are not critical for this low-complexity 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?

The input schema already provides full descriptions for both parameters (lang and slug), including an enum for lang. The description does not add parameter-level detail, but with 100% schema coverage, the baseline of 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 clearly states the tool returns a category-based selection with sorting and a table of MFO offers including conditions and canonical_url. It distinguishes from sibling list_categories by focusing on a single category's compiled offers, but does not explicitly name alternatives or contrast with search_offers.

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 clear context about what the tool returns—selection criterion, sorting, and an MFO offers table—so an agent can infer when to use it. It does not explicitly exclude alternatives like list_categories or search_offers, but provides enough context to guide use.

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 burden. It discloses important behavioral facts: data source (mfoxa.com.ua), canonical language ('Legal facts are maintained in Ukrainian only'), that lang affects only the note field, the presence of a manual-check date, and a citation requirement (canonical_url). These go beyond the basic purpose, though the read-only nature is implied rather than explicit.

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 moderately concise, listing essential legal content followed by usage and source notes. Every sentence provides specific value, though the mix of Ukrainian and English and the dense legal citations make it slightly less approachable. It remains within an acceptable length.

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?

Even without an output schema, the description covers the main content areas, data provenance, language behavior, update date, and citation instruction. This is sufficient for an agent to decide and invoke the tool correctly. It does not enumerate all return fields beyond canonical_url and note, but the overview is reasonably complete for this simple resource.

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 fully documents the lang parameter with enum and default, so the baseline is 3. The description adds non-obvious semantics: 'lang affects only the note field' and clarifies that legal facts remain canonical in Ukrainian regardless of lang. This extra nuance justifies a higher score.

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 provides legal context of microlending in Ukraine, listing specific legal topics (rate limit, creditworthiness assessment, fines, collectors, military credit holidays). This distinguishes it from sibling tool categories like MFOs, reviews, and offers. However, it lacks an explicit verb like 'returns' or 'retrieves', relying on the tool name itself.

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 usage context is implied: an agent would use this tool when legal market rules are needed. However, there is no explicit guidance on when to prefer this tool over siblings, nor when not to use it. It does not name alternative tools or exclusion criteria.

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 must disclose all behavioral traits. It mentions the data source (mfoxa.com.ua) and an attribution requirement (cite canonical_url), which is useful. However, it does not disclose whether the operation is read-only, requires authentication, or has any side effects, nor does it describe error handling or staleness policies.

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 a single, well-organized sentence listing the key data points, followed by two short sentences about data source and attribution. It is front-loaded with the main content and avoids unnecessary words.

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 that there is no output schema and no annotations, the description provides a detailed inventory of the expected content, which is helpful for an agent. However, it does not specify the response format, error cases, or how this tool relates to the other catalog tools, leaving some gaps in an otherwise simple retrieval context.

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 provides full descriptions for both lang and slug, with 100% coverage. The description adds no additional parameter-level meaning, so the baseline score of 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 clearly identifies the tool as returning a 'Картка МФО' (MFO card) with a comprehensive list of fields including terms, rates, legal entity, license, and rating. This distinguishes it from siblings like list_mfo (listing) and search_offers (search). However, it lacks an explicit verb or direct statement that it retrieves a single MFO's data, relying on the tool name.

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 provides no guidance on when to use this tool instead of alternatives. It does not mention list_mfo for finding slugs, nor does it discuss scenarios such as comparing MFOs or looking up market rules. The only indirect hint is in the schema's slug description, not the tool description.

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. It adds useful context: data source (MFOHA catalog), privacy note (no personal data beyond public), and attribution requirement (refer to canonical_url). However, it does not disclose ordering, pagination behavior, or error handling, which are relevant for a data-fetching 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?

The description is three short sentences that front-load the core purpose, then add source and attribution notes. Every sentence carries practical information without redundancy or fluff.

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 there is no output schema and no annotations, the description covers the returned data fields, data source, and usage restriction. However, it omits details about the maximum limit, ordering, and behavior when the slug is invalid, leaving some gaps for an agent invoking the tool. It is adequate but not thorough.

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 describes 'slug' and 'lang', but 'limit' is undocumented. The description does not add meaning to any parameters, and does not compensate for the missing 'limit' semantics. It mentions output fields but not how they relate to parameters, so it provides minimal additional value 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 clearly states it returns 'latest reviews of borrowers about MFOs' and enumerates the data fields (rating, date, text). It distinguishes this from sibling tools by focusing specifically on reviews, with no ambiguity about the resource being accessed.

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 provides no explicit guidance on when to use this tool versus alternatives like get_mfo or search_offers. It mentions the data source and attribution requirement, but does not clarify context or exclusions, leaving the agent to infer usage from the tool name alone.

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 transparency burden. It discloses the data source (mfoxa.com.ua), the two groups (credit and loan), fields like llms_criterion and canonical_url, and a usage requirement to reference canonical_url. However, it lacks details about read-only nature, pagination, or other behavioral 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?

The description is short but poorly structured. The first sentence is grammatically incomplete and lacks a verb, making it unclear. The flow is awkward and not front-loaded with the action. It could be rewritten as a clear, direct statement.

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 has no output schema or annotations, so the description must provide context. It does mention the two groups, key fields, data source, and a usage requirement. However, it misses the exact response format, how groups are represented, and any limitations, leaving room for improvement.

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 covers 100% of parameters (the lang parameter with description). The description adds no extra parameter semantics, but since schema coverage is complete, the baseline of 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 mentions 'Категорії каталогу' and describes two groups, but it lacks an explicit action verb like 'returns' or 'lists'. It gives some specifics about the output but is vague about the actual operation. It does not differentiate from the sibling tool 'get_category' which is a singular lookup.

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 explicit guidance on when to use this tool versus alternatives. It doesn't mention that this is for listing all categories or when to prefer it over get_category. The description focuses on output content rather than usage context.

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 compensates by explaining that rating: null means no reviews yet (not a low score), cites the data source (MFOHA catalog), and instructs to refer to canonical_url. These are meaningful behavioral details beyond the basic purpose. It does not cover pagination or ordering, but for a simple list tool this is sufficient.

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 four short sentences, each serving a distinct purpose: purpose, null semantics, data source, and usage note. There is no redundancy or irrelevant content, making it concise and well-structured.

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 simple scope (list all MFOs) and one optional parameter, the description covers the essential aspects: purpose, null rating meaning, data provenance, and canonical_url instruction. It lacks explicit mention of the output being an array, but the word "list" implies that. Overall adequate for the tool's 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?

The only parameter (lang) is fully documented in the schema (100% coverage with enum and default). The description adds no extra semantic detail about the parameter, 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 clearly states the tool lists all MFOs in the catalog with borrower ratings and canonical_url fields. This distinguishes it from siblings like get_mfo (single MFO) and list_categories (categories), making the purpose 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?

It implies usage as a broad listing tool ("all MFOs of the catalog") but does not explicitly state when to use it over alternatives or any exclusions. For example, there is no mention of using get_mfo for a single institution. Context is present but guidance is not 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 burden and does well: it discloses the return fields, the null meaning of first_loan_zero_percent, the unfiltered full-catalog behavior, and the data source with attribution requirement. Missing details include error handling, sorting, and pagination beyond limit.

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 front-loaded: purpose, filters, output details, usage caveat, and attribution are packed into three sentences. Each sentence contributes value, though the semicolon-heavy middle sentence is slightly dense.

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 search tool with no annotations and no output schema, the description covers core behavior: filterable fields, return semantics, full-catalog behavior, and data provenance. It does not mention sorting or empty-result handling, but overall is sufficient for an agent to invoke 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 100%, so the baseline is 3. The description restates amount, term, and first_loan_zero in prose but does not add new semantics beyond the schema; lang and limit are covered only in 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 clearly identifies the tool as selecting credit offers by amount, term, and first-loan-zero flag, and states that it returns terms and canonical_url. This distinguishes it from sibling tools like list_mfo or get_reviews, which focus on different entities.

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 notes that calling without filters returns the full catalog and advises using limit or passing amount/term for targeted results. It stops short of naming alternative tools or when-not-to-use scenarios, so it lacks explicit exclusions.

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