MFOXA - Ukraine Microfinance Catalog

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 the data source and the requirement to cite canonical_url, which is a behavioral constraint. However, it does not discuss read-only nature, error conditions, or any potential side effects.

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 sentences with essential information. The first sentence is somewhat dense but remains understandable, and the second sentence adds a necessary attribution requirement.

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 no output schema, the description covers key aspects: what the response contains (criterion, sorting, offers table, canonical_url), the data source, and usage requirement. It is sufficiently complete for a retrieval tool, though a bit more detail on the response shape would help.

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 baseline is 3. The description adds context about the output (selection criterion, sorting, offers table) but does not add meaning to the parameters themselves 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 the tool returns a category selection with selection criterion, sorting, a table of MFO offers, and canonical_url. It distinguishes from siblings like list_categories by emphasizing the offers table, but does not explicitly 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?

No explicit guidance on when to use this tool versus alternatives. The description implies usage by describing the output, but lacks clear context, prerequisites, or exclusions.

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. It discloses that legal facts are canonical in Ukrainian, that 'lang' affects only the note field, that a manual-check date is included, and that the data comes from mfoxa.com.ua with canonical_url attribution. This provides meaningful behavioral context beyond a simple read operation.

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 dense and front-loaded with the core legal topics. While somewhat long and mixing Ukrainian with English, each sentence provides distinct information: content, freshness, canonical language, data source, and attribution. It is appropriately sized for a tool delivering legal context.

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 one-parameter tool without an output schema, the description covers content, language behavior, freshness, data source, and attribution. It does not enumerate output fields explicitly, but the provided details give sufficient context for an agent to select and use 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 fully describes the 'lang' parameter with an enum. The description adds value by specifying that 'lang affects only the note field,' clarifying the parameter's actual impact on output. This goes beyond the schema's generic 'response language' wording.

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 communicates that the tool provides legal context for microlending in Ukraine, listing specific legal topics. It is distinct from sibling tools like get_mfo or get_reviews, though it lacks an explicit action verb like 'returns' or 'gets'.

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 implies that it should be used when legal/regulatory context about Ukrainian microloans is needed, but it does not explicitly state when to use this tool versus alternatives. No exclusion criteria or alternative tool references are provided.

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 burden of behavioral disclosure. It provides useful context about data source (MFOXHA catalog) and attribution requirements, but does not mention error handling, auth, or side effects. As a read-only 'get' operation, the lack of destructive behavior is not explicitly disclosed but is reasonably inferred.

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 paragraph that front-loads the resource name and then lists the key output fields in a compact, informative manner. It is longer than strictly necessary but each item adds value and there is no redundancy.

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 description thoroughly enumerates the output contents (conditions for new/repeat clients, real annual rate, legal entity, license, documents, rating, update date) and adds source/attribution context. For a simple 2-parameter fetch without an output schema, this is largely complete, though it omits error cases and language behavior (which the schema covers).

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 (slug and lang), with 100% coverage. The description does not add additional meaning beyond mentioning 'canonical_url' indirectly, so it does not elevate above the baseline of 3.

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 detailed 'MFO card' with specific content (conditions, rates, legal entity, license, rating, update date). It is distinct from siblings like list_mfo or search_offers by focusing on a single entity's comprehensive data, though it lacks an explicit verb like 'returns' or a direct comparison to alternatives.

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 does not explicitly state when to use this tool versus alternatives. It includes an attribution note ('use canonical_url') but no guidance on prerequisites (e.g., needing a slug from list_mfo) or exclusions. The context is only implied by the tool name and schema.

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 burden and does well: it discloses the data source (MFOHA catalog), the privacy limitation (no personal data beyond what is public), and the attribution requirement (canonical_url). It does not cover error behavior or response format, but the key behavioral context is present.

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 two sentences, immediately front-loaded with the core purpose. It conveys essential information (what, source, privacy, attribution) without unnecessary words or repetition.

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 parameter set and no output schema, the description is adequately complete: it specifies the return content, source, privacy caveat, and attribution. Minor gaps like pagination behavior are not addressed but the schema's limit constraint mitigates this.

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 67% (slug and lang are described, limit is not). The tool description does not add parameter-level meaning beyond what the schema provides; it only lists output fields, not input semantics. Since coverage is above 50%, a 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 states the tool returns the latest borrower reviews of MFIs, specifies the content (rating, date, text), and differentiates from sibling tools that handle categories, MFO details, or search. The verb 'get' and resource 'reviews' are explicit.

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 implies the tool is for retrieving reviews but does not explicitly state when to use it over alternatives, nor does it mention any exclusions. It provides context about the data source and attribution, but no direct comparison with sibling tools.

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 useful behavioral context: categories appear in two groups, each with llms_criterion and canonical_url, data source (MFOXA), and a citation requirement. However, it does not mention return format, pagination, or whether the operation is read-only, which is a gap for a list 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 a single sentence packed with relevant details: grouping, attributes, data source, and citation instruction. It is front-loaded and avoids redundancy, though the phrasing is slightly awkward and could benefit from clearer segmentation.

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 low-complexity tool with no output schema, the description covers the key aspects: category groups, selection criterion, canonical_url, data origin, and usage note. It falls short of fully specifying the output structure (e.g., array shape) and any potential limits, but is reasonably complete.

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 fully documents the only parameter (lang) with enum and default, so schema coverage is 100%. The description adds no additional parameter details, matching the baseline of 3 for high schema 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 catalog categories in two groups (credit at root, loan at /loan/), with each having specific attributes. The verb 'list' is implied by the name and description, and it distinguishes from siblings like get_category (singular) and list_mfo (lists MFOs).

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 explicit guidance on when to use this tool versus alternatives. It mentions data structure and a usage instruction (refer to canonical_url) but does not contrast with get_category, list_mfo, or search_offers. Usage context is only implied by the description's content.

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?

The description provides useful behavioral context beyond the schema: the meaning of rating null ('no reviews yet') and the data source (mfoxa.com.ua). However, with no annotations and no output schema, it does not disclose pagination behavior, response format, or other potential side-effects/constraints, leaving some burden unmet.

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, each sentence adds value: core purpose, null semantics clarification, and source/attribution note. No filler or redundancy.

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 list tool with one optional parameter, the description covers purpose, key returned fields, null handling, and attribution. It lacks explicit output schema details, but the absence of an output schema is partially compensated by mentioning rating and canonical_url. Overall, complete for typical use.

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 with an enum and default. The description adds no extra semantic detail for parameters, but schema coverage is 100%, so 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 uses a specific verb-resource combination: 'Список усіх МФО каталогу' (List of all catalog MFIs), and specifies key fields (rating, canonical_url). It clearly distinguishes from siblings like get_mfo, which implies a single item, and get_reviews, which focuses on reviews.

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 phrase 'усіх МФО каталогу' establishes when to use this tool: when the complete list of MFIs is needed. It also gives an instructional hint to reference canonical_url when using the data. However, it does not explicitly mention alternatives or when not to use it, though sibling differentiation is implied.

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 transparency burden. It discloses return fields (conditions, canonical_url), the interpretation of first_loan_zero_percent (true/false/null), and behavior without filters (full catalog). It also notes the data source and the requirement to reference canonical_url, adding meaningful 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 three sentences, front-loaded with a clear purpose, followed by output/behavior details and usage guidance. There is no redundancy or filler, making it highly efficient for an agent to parse.

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 tool has no output schema, the description adequately explains what is returned (conditions, canonical_url) and the filter semantics. It covers the main use cases and special values, though it could mention pagination or total count details. Overall, it is complete enough for an agent to select and 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?

Although the input schema already provides 100% parameter coverage with descriptions, the description adds semantic value by grouping filters, explaining the 'promotional first loan' concept (grace period for new clients), and clarifying the null meaning for first_loan_zero_percent. It also advises using limit when no filters are passed, enriching the parameter usage context.

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's function: selecting credit offers by amount, term, and promotional first loan status. It specifies the resource (credit offers), criteria (amount, term, first_loan_zero), and output (conditions and canonical_url), distinguishing it from sibling tools like get_mfo or list_mfo which operate on individual entities or categories.

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 concrete usage guidance: 'Call without filters returns the full catalog (use limit); prefer passing amount/term for targeted results.' It explains when to use filters and the default behavior without them, though it does not explicitly name alternative tools or exclude specific cases.

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