MCP Market Russia

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

With no annotations provided, the description carries the full burden. It explains that the tool uses average price per m² by material and region from actual data, but does not disclose behavioral traits such as whether it is read-only (likely safe), if there are rate limits, or if it caches results. The description adds some context beyond the schema but lacks full transparency.

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 concise and well-structured with a clear introductory sentence followed by parameter explanations in a bullet-like format. Every sentence provides necessary information without 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?

Given the tool has an output schema (so return values are covered), the description is complete for a straightforward cost calculator. All four parameters are explained, and the overall purpose is clear. No additional behavioral context is needed beyond what is provided.

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 0%, yet the description adds rich meaning for each parameter: area is required with an example, material lists enum values, region specifies city vs nationwide, and floors indicates adjustment. This fully compensates for the schema's lack of description.

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 calculates estimated construction cost based on real market data. It uses a specific verb 'calculate' and defines the resource 'construction cost'. The description distinguishes it from siblings like price_comparison and project_estimator by mentioning real market data and per m² pricing.

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 context on how to use the tool (parameters) but does not explicitly state when to use it versus sibling tools. No guidance on exclusions or alternative tools is given, leaving the agent to infer from parameter descriptions.

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 carry the full burden. It lists the data included but does not disclose behavioral traits such as rate limits, authentication requirements, or whether the data is real-time. It is clear about the output scope but lacks depth.

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 of 18 words, which is concise and front-loaded with the main action. However, it could be structured (e.g., bullet points) for better readability. No wasted 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 the output schema exists, the description does not need to explain return values, but it lists many data types. It lacks explanation of prerequisites (e.g., valid slug format) and potential limitations. Adequate but incomplete without clarifying the parameter.

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 slug has no description in the schema (0% coverage). The tool description does not explain what slug represents (e.g., company identifier or URL slug). The description fails to add meaning beyond the schema, which is critical given the low 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 it returns a comprehensive company profile including contacts, projects, pricing, reviews, market position, and competitor comparison. This distinguishes it from siblings like get_company (basic info) and compare_companies (just comparison). Verb 'Get' + resource 'company profile' is specific.

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 it is for obtaining a full company profile, but does not explicitly state when to use over alternatives (e.g., use get_company for basic info, compare_companies for comparisons). No exclusions or when-not-to-use guidance.

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 provided, so description carries full burden. It states it returns a 'comprehensive dossier' but does not explicitly confirm read-only behavior, side effects, auth requirements, or rate limits. Minimal behavioral disclosure beyond data scope.

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 plus one-line parameter description. Front-loaded with key action and scope. No redundant or unnecessary text.

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 an output schema exists, the description appropriately summarizes high-level output (details, projects, prices, etc.) and use case. Missing explicit read-only declaration, but overall complete for selecting and invoking the 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?

Despite 0% schema coverage, the description adds meaning to 'company_slug' by stating it is 'from search results', guiding the agent to source it correctly. Could be more specific (e.g., exact format), but provides useful 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?

Clearly states the action 'Get FULL company portfolio' and specifies included data (details, projects, prices, reviews, contacts). Distinguishes from siblings like 'get_company' and 'company_deep_profile' by emphasizing comprehensive coverage for due diligence/hiring.

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?

Implies usage for due diligence or hiring decisions, but no explicit guidance on when to choose this over alternatives like 'get_company' or 'company_deep_profile'. No when-not-to-use or exclusion criteria 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 discloses that it compares companies on the listed attributes but does not detail output format, sorting, or any constraints (e.g., region or validity of IDs). The behavior is partially transparent but could be more explicit about what the result looks like.

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 extremely concise, with only two sentences plus an arg specification. Every word adds value; no repetition or fluff. Front-loaded with the core purpose.

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 tool with one parameter and an existing output schema, the description covers the essential aspects: what is compared, how many items, and parameter details. It could mention that IDs must be valid, but overall completeness is high.

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 company_ids is a string with 0% schema coverage. The description adds crucial semantics: comma-separated format, length constraint (2-3 IDs), and an example. This fully compensates for the lack of 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 states the action (compare) and resource (construction companies) and specifies the attributes compared (prices, ratings, number of projects, specialization). It distinguishes from siblings like price_comparison and region_comparison by its multi-attribute comparison scope.

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 provide guidance on when to use this tool versus the many siblings (e.g., price_comparison, company_deep_profile). It lacks when-to-use and when-not-to-use information, leaving the agent to infer 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?

No annotations are provided, so the description must carry the full burden. It mentions returning a ranked list with match scores but does not disclose whether the tool is read-only, potential side effects, or limitations. The behavioral disclosure is minimal.

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 extremely concise: two sentences that front-load the key purpose ('AI-powered contractor recommendation') and immediately follow with the core function and output. No 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 the tool has 7 optional parameters, an output schema, and many siblings, the description is somewhat incomplete. It explains basic inputs and output format but lacks details on ranking criteria, match score interpretation, and algorithm behavior. The output schema exists but the description could still provide more 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 parameter schema has 0% coverage, so the description must compensate. It mentions budget, region, and quality requirements, which map to some parameters (budget_max, budget_min, region, min_rating), but ignores others like category, need_contacts, and need_portfolio. It adds high-level meaning but not full detail.

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 finds the best matching companies based on budget, region, and quality requirements, and returns a ranked list with match scores. However, it does not explicitly differentiate from similar siblings like smart_match or compare_companies, though the mention of 'AI-powered' and 'ranked list' provides some distinction.

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 offers no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. Without any usage context, the agent must infer suitability from the purpose 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?

No annotations are provided, so the description carries full burden. It discloses UTF-8 BOM, Excel-friendly, limit range 1..2000, and return format (CSV text). It doesn't mention authentication needs or side effects, but since export is read-only, this is acceptable.

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 concise: one-line purpose, then bullet points for parameters. Every sentence adds value—no filler. Front-loaded with the main action.

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 6 parameters (all explained), no required params, and an output schema (though not shown, description mentions CSV text return), the description is complete for the tool's complexity. Sibling tools suggest this is the only CSV export option, so no missing 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?

Schema coverage is 0%, so description must compensate fully. It does: each parameter is explained (entity: 'companies' or 'projects', query: free-text, category/region: filters, budget_max: cap for companies, limit: 1..2000 with default 500). This adds significant meaning beyond the schema's type/default information.

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 starts with a specific verb 'Export' and resource 'search results as CSV text'. It clearly distinguishes from sibling search tools (search_companies, search_projects) by focusing on CSV output. No ambiguity.

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 parameter explanations (entity, query, category, region, budget_max, limit) and the overall purpose (export CSV). It implies when to use (need CSV export) but doesn't explicitly exclude scenarios (e.g., 'if you need filtered browsing, use search_companies instead'). Still, guidance is clear enough.

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 exist, so the description must disclose behavior. It explains filtering and sorting but does not mention side effects, data freshness, pagination behavior (limit up to 50), return format, or error cases. The output schema exists but is not referenced. Transparency is moderate.

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 well-structured with an initial summary and a labeled 'Args' section. The summary sentence is somewhat marketing-ish ('Smart lead generation') but not overly verbose. The parameter list is clear and consistent. A bit of fluff ('Perfect for finding contractors') could be trimmed, but overall it's efficient.

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 9 parameters and no required ones, the description covers input semantics thoroughly. However, it does not explain output structure, pagination limits beyond the 'limit' parameter, or edge cases (empty results). Since an output schema exists, the description could be more complete by referencing output fields or linking to the schema.

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 has no descriptions for any of the 9 parameters, so the description carries the full burden. It explains each parameter clearly (e.g., 'region: Filter by region name... Empty = all', 'min_rating: Minimum rating (0-5). 0 = no filter') with examples and default behaviors, adding significant 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 the tool finds 'the best construction companies' with specific criteria, distinguishing it from sibling 'search_companies' which likely provides a more basic search. The verb 'find' and resource 'best construction companies' are specific. The use cases (lead generation, contractors, market research) reinforce purpose.

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 when to use (lead generation, finding contractors) but does not explicitly state when not to use it or how it differs from alternatives like 'search_companies' or 'smart_match'. No exclusions or alternative suggestions are provided. It's adequate but lacks explicit guidance.

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 full burden. It correctly implies a read-only operation (fetching data) with no side effects. It does not disclose minor details like ordering or pagination, but for a zero-parameter listing tool, this is adequate.

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, front-loaded sentence with no extraneous information. Every word contributes to understanding the tool's purpose.

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's simplicity (no parameters, existence of an output schema), the description is fully adequate. It succinctly conveys the essential behavior and result, and does not need to elaborate on return values since an output schema is present.

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?

There are zero parameters, and schema description coverage is trivially 100%. The description adds value by explaining what the output includes (categories and counts), which compensates for the lack of parameter info. Baseline for zero parameters is 4.

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 retrieves all company categories along with the count of companies per category, using a specific verb ('Get') and resource ('company categories'). It distinguishes itself from sibling tools like 'get_company' and 'get_regions' by focusing on a different entity.

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 clear context for when to use the tool (to get categories with company counts) but does not explicitly exclude scenarios or mention alternatives. Given its simplicity, the implicit usage is sufficient.

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 fully disclose behavior. It declares a 'Get' operation (likely read-only) but does not state whether it is safe, idempotent, or if authentication is required. No side effects or constraints are mentioned.

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 plus an Args section, with no redundant information. It efficiently states the tool's return value and parameter source without 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 presence of an output schema, the description does not need to detail return values. It covers the essential: what is returned and how to obtain the required parameter. However, it could mention if the company must exist or error handling.

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 has 0% description coverage and only one parameter (company_id). The description adds meaning by specifying it is a 'Company UUID from search_companies results', which provides type and source context 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 uses a specific verb 'Get' and resource 'company profile', listing key data (contacts, prices, rating, reviews, projects). It clearly distinguishes from sibling tools like search_companies (which returns list) and compare_companies (which compares).

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 company_id comes from search_companies results, implying a prerequisite. However, no explicit guidance is provided on when to use this tool versus alternatives like company_deep_profile, and there is no mention of conditions 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?

No annotations are provided, so the description must disclose behavior. It correctly indicates a read operation and lists return fields. However, it does not mention error handling, non-mutating nature, or any side effects. The absence of explicit read-confirmation is a gap, but the description is adequate.

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, focused paragraph with no superfluous words. It front-loads the purpose and provides parameter explanations and return structure in two concise sentences.

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 presence of an output schema, the description covers the key aspects: purpose, parameters, and return fields. It lacks error handling details (e.g., what happens if lead_id is invalid), but for a simple read tool it is mostly complete. The sibling list provides context that this is for post-creation status checks.

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 0%, but the description explicitly explains both parameters: lead_id is 'UUID returned by request_quote' and api_key is 'the api_key used when the lead was created'. This adds significant meaning beyond the schema's bare type and requirement.

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 'Check the status of a lead created via request_quote', specifying the action (check), resource (lead status), and the dependency on request_quote. This distinguishes it from siblings like request_quote (creation) and other query 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?

The description implies the tool should be used after request_quote by stating 'created via request_quote'. It provides clear context but does not explicitly state when not to use it or list alternatives. There is no mention of prerequisites beyond the parameters.

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 full burden. It implies a read-only operation via 'Get' but does not explicitly confirm non-destructiveness, auth requirements, or rate limits. The verb suggests safety, but more explicit disclosure would be better.

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 extremely concise: one sentence for purpose and one line for the parameter. It is front-loaded and contains no 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 an output schema exists (as per context signals), the description appropriately focuses on input and general content. It lists what is included (specs, price, features, contacts) but could mention that it returns a single project object or handle error cases.

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 has 0% coverage, and the description adds meaning by specifying that project_id is a UUID from search_projects results. This compensates for the schema's lack of description, though it could elaborate on format or constraints.

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 verb 'Get' and the resource 'detailed house project information', and lists specific contents (specifications, price, features, company contacts). It distinguishes itself from siblings like search_projects (which searches/finds projects) and get_company (company info).

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 that project_id should come from search_projects results, providing clear context for its use. However, it does not mention when not to use this tool or explicitly compare with 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?

No annotations are provided, so the description must carry behavioral transparency. It states the output (regions with company counts) but does not disclose whether it is read-only, performance aspects, or any 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?

One sentence of 13 words, front-loaded with the action and result. No extraneous information.

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 tool with no parameters and an output schema, the description completely covers what the tool does and what it returns. Nothing 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?

There are zero parameters, so the baseline is 4. The description adds no parameter information, but none 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 uses specific verb 'Get' and resource 'regions' with the detail 'with the number of companies', clearly distinguishing it from siblings like get_categories or region_comparison.

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 on when to use this tool versus alternatives such as region_comparison or market_analytics. There is no mention of constraints or prerequisites.

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 provided, so description carries full burden. Only states what is returned, but omits behavioral traits such as data freshness (e.g., real-time vs cached), performance implications, or whether it requires authentication. As a read-only operation, no destructive hints needed, but could mention that it returns a snapshot.

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?

Single sentence efficiently conveying purpose and output. No redundant words. Front-loaded with key action 'Get catalog statistics'.

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 zero parameters (accepts no input) and an output schema presumably describes return structure, the description adequately explains what statistics are returned. However, could mention that this is a summary overview (e.g., 'aggregated counts across the catalog') to distinguish from detailed tools.

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?

Input schema has 0 parameters (100% coverage trivially). With no params, baseline is 4. Description adds no parameter information, but since there are none, it's acceptable.

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?

Clearly states the verb 'Get' and resource 'catalog statistics', listing specific items (total companies, projects, regions, categories, agent queries, leads). Distinguishes from siblings like get_categories and get_regions which return individual lists, not aggregated counts.

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?

Implicitly indicates this tool is for high-level aggregated statistics, but does not explicitly state when to use it versus alternatives like get_categories or market_analytics. No guidance on when not to use or prerequisites.

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, and the description does not disclose behavioral traits such as read-only nature, rate limits, authentication requirements, or data freshness. The verb 'Get' implies a non-destructive operation, but this is implicit.

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 front-loaded with the tool's purpose and return values. The parameter documentation is structured as a list, making it easy to parse. No unnecessary sentences.

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 output schema exists, the description does not need to detail return types. It covers the necessary context: what the tool returns and the two parameters. However, it omits potential limitations like pagination or data recency.

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 description explains both parameters (region and category) with examples and default behavior ('Empty = all regions'). This fully compensates for the 0% schema description coverage, adding significant value.

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 comprehensive market analytics with specific data points like average prices, top companies, etc. However, it does not differentiate from sibling tools such as market_report or price_comparison, which may have overlapping functionality.

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 mentions the tool is 'perfect for investors, analysts, and companies entering the market,' but does not specify when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is 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 must cover behavioral traits. It lists what the report includes but does not disclose side effects, permissions, data sources, or response format. The output schema exists but is not referenced.

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 concise with three sentences plus an Args line, all front-loaded with essential information. Every sentence adds value without 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?

Given the presence of an output schema, the description sufficiently covers the tool's purpose and parameter. However, it could be more complete by specifying region format requirements or potential async behavior.

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 description adds an 'Args' section with examples for the region parameter, significantly improving upon the schema which only defines it as a string. This provides clear guidance and usage examples.

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 generates a comprehensive market report for a specific region, listing included components (market size, price tiers, top players, etc.) and ideal use cases. It distinguishes from siblings by specifying its unique scope and content.

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 mentions it is perfect for investors, business development, and market entry analysis, providing clear usage context. However, it does not explicitly state when not to use it or compare it to similar siblings like 'market_analytics'.

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 discloses that the tool returns 'detailed price statistics, percentiles, and regional rankings', which adds behavioral context beyond the schema. However, without annotations, it lacks information on destructive potential, authentication needs, or error handling. The empty input behavior is clarified, but completeness is modest.

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 extremely concise with three clear sentences: purpose, return content, and parameter explanations. Front-loaded with the key action and resource, no superfluous 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?

For a tool with only 2 parameters and an output schema, the description covers inputs (including defaults) and outputs (statistics, percentiles, rankings). It is mostly complete, though it could specify acceptable category values or region format more precisely.

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 has 0% description coverage, so the description fully carries parameter meaning. It explains 'regions' as comma-separated and notes empty defaults mean all. This adds significant value beyond the schema's bare definitions.

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 compares construction prices across regions and categories, using specific verb 'Compare' and resource 'construction prices'. However, it does not differentiate from sibling tool 'region_comparison', which might have overlapping functionality.

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 usage for price comparison but provides no guidance on when to use this tool versus alternatives like 'calculate_cost' or 'market_analytics'. No when-not or explicit context about prerequisites or limitations.

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?

Without annotations, the description lacks disclosure of behavioral traits such as read-only nature, performance implications, or data source freshness. 'Uses real market data' hints at external dependency but does not confirm safety or 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?

Single sentence with no filler. Essential information is front-loaded: action, resource, key parameters, and data source. Every word contributes to understanding.

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 4 parameters (0% schema coverage), no annotations, and an output schema, the description could be more thorough. It omits valid values for region/category, data coverage scope, and return format hints (partially offset by output schema).

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 0%, so the description must compensate. It names four parameters (area, region, category, quality) and mentions quality options (economy/standard/premium), adding meaning beyond raw schema. However, it does not explain valid regions or categories, leaving partial ambiguity.

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 action (estimate), resource (construction project cost), and key inputs (area, region, category, quality level). It distinguishes from siblings by specifying 'real market data' and 'construction project' context, making purpose unmistakable.

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 on when to use this tool versus siblings like 'calculate_cost', 'price_comparison', or 'market_analytics'. The description does not mention exclusions or prerequisites, leaving the agent without decision-making 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 must cover behavioral traits. It discloses what output is shown (companies count, ratings, etc.), but does not mention any side effects, cost, rate limits, or return format details. Lacks explicit safety info but is adequate for a read-like query.

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, no unnecessary words. The first sentence states the core purpose, the second lists outputs. Efficient 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 tool has two parameters, an output schema, and several siblings, the description is incomplete: it misses the category parameter and lacks guidance on when to use. It covers the basics but leaves gaps.

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 0%, so description must compensate. It explains the 'regions' parameter as comma-separated names, but completely omits the 'category' parameter (default ''). One of two parameters is undocumented, leaving ambiguity.

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 compares construction markets across regions and lists the specific data shown (companies count, ratings, prices, contact availability). It effectively distinguishes from siblings like price_comparison or compare_companies.

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 instructs to provide comma-separated region names, but does not explain when to use this tool versus siblings such as get_regions, price_comparison, or compare_companies. No exclusions or alternatives are mentioned.

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 provided, so the description carries the burden. It discloses return values ('confirmation with lead ID and company contact details'), which adds behavioral context. However, it does not mention side effects, permissions, or error conditions.

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 (2 sentences) followed by a parameter list. It is efficiently structured but includes some redundant wording ('for the quote request', 'for callback'). Still, it earns its place with no wasted space.

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 presence of an output schema and 6 parameters, the description provides parameter semantics and basic behavioral context. However, it lacks usage guidelines and could mention prerequisites or typical use cases.

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 0%, but the description provides explicit parameter descriptions (e.g., 'company_id: Target company UUID (required)', 'project_id: Specific project UUID if the user is interested in a particular house project'). This adds meaning beyond the schema defaults.

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 'Send a quote request to a construction company on behalf of the user.' This provides a specific verb (send) and resource (quote request), and distinguishes it from sibling tools like get_lead_status or calculate_cost.

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 over alternatives or provide exclusions. The usage is implied but lacks guidance on scenarios or comparisons 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 provided, so description must convey behavior. It states the analysis outputs but does not disclose limitations like data freshness, rate limits, or handling of empty inputs. Adequate but not thorough.

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, no filler. First sentence describes purpose and outputs, second explains parameter usage. Efficient 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?

Output schema exists, and description lists key outputs. However, handling of all-optional parameters is ambiguous (e.g., if all are empty). Lacks edge case coverage.

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 0%, but description adds meaning: company_slug for specific company, region/category for market overview. Adds context beyond raw schema names, though doesn't specify allowed values for region/category.

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?

Description clearly states the tool analyzes company reviews with specific outputs: sentiment breakdown, common themes, strengths and weaknesses. It distinguishes from siblings like get_company or market_analytics by specifying the review analysis focus.

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?

Provides clear guidance: use company_slug for specific company, region/category for market overview. Implicitly tells when to use this tool versus siblings, but lacks explicit exclusions or 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 burden. It mentions returned fields and the limit parameter's max value, but does not disclose read-only nature, rate limits, or filtering behavior (e.g., whether multiple filters combine). Adequate but not thorough.

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 concise, front-loaded with the core purpose, and uses a clear Args block. Every sentence adds value, and there is no 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?

The description covers the tool's purpose, all parameters with usage details, and mentions return fields. Since an output schema exists, return values are partly covered. It slightly lacks behavioral context like pagination or error handling, but is largely complete for a search 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 description coverage is 0%, so the description fully compensates. Each parameter (query, category, region, budget_max, limit) is explained with examples, default behavior, and constraints (e.g., 'Set to 0 for no limit'). This adds significant meaning 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 'Search Russian construction companies by category, region, and budget' and lists returned fields. This distinguishes it from siblings like search_projects or company_deep_profile by specifying the resource (companies) and filtering dimensions.

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 parameter examples and constraints (e.g., budget_max: 0 for no limit), implying usage for broad searches. However, it does not explicitly guide when to use this tool over alternatives like find_best_companies or contractor_recommendation.

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 full burden. It does not disclose behavior beyond parameter details, such as rate limits, pagination, error handling, or read-only nature. For a search tool this is acceptable but incomplete.

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 concise and well-structured: a clear purpose sentence, return types, then parameter explanations. Every sentence adds value with 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?

Given output schema existence, the description covers return fields sufficiently. It mentions limit maximum (20) but lacks pagination details. For typical search tool usage, this is adequate, though a note on result ordering or empty results would improve completeness.

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 0%, but the description fully compensates by explaining each parameter's meaning, defaults, and value constraints (e.g., 'Set to 0 for no limit', material options). This adds crucial meaning 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 searches house building projects with specific filters (area, floors, material, price) and lists return fields (specs, price, link, contacts). This distinguishes it from siblings like search_companies or find_best_companies.

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 when to use (searching projects by specific criteria) but provides no explicit guidance on when to use alternatives or when not to use this tool. With many sibling tools, this is a gap.

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 full burden of behavioral disclosure. It discloses the input (brief, top_n) and output (parsed filters, matches, explanation). It does not mention side effects, rate limits, or authentication requirements, but given that the tool likely performs a read-only search, the description is adequate.

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 concise: two sentences with no superfluous words. It front-loads the purpose, includes an example, and specifies output format. Every part is necessary and informative.

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's complexity (NLP search) and the presence of an output schema, the description provides sufficient context. It explains the input, output structure, and example. The output schema exists to document return values, so the description does not need to elaborate on them.

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 0%, so the description must compensate. The description explains the 'brief' parameter with a detailed example and format (free-form Russian). It implicitly defines the 'top_n' parameter by mentioning 'top-N matching', but it does not specify the default value or expected range. This is a minor gap.

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 purpose: natural-language contractor search. It provides a concrete example of a Russian brief and specifies the output (parsed filters, matches, explanation). This distinguishes it from sibling tools like search_companies (structured search) and contractor_recommendation (likely recommendation-based).

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 instructs the user to pass a free-form Russian brief. It does not provide explicit exclusions or alternatives, but the context of siblings implies when this tool is appropriate (natural language queries) versus structured searches. A clear 'when not to use' statement would improve this dimension.

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 full burden. It does not disclose behavioral traits such as side effects, permissions, rate limits, or constraints. It only describes output behavior ('shows how the market is developing') without input-side transparency.

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 concise with two sentences. It front-loads the core purpose ('Analyze market trends') and provides specific aspects. No fluff, but could be more 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 3 optional parameters, no schema descriptions, and no annotations, the description provides a high-level overview but lacks specifics on parameter values and output format. Output schema exists, so return values are not required, but input guidance is incomplete.

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?

With 0% schema description coverage, the description should compensate. It adds meaning by mentioning region and category filtering, and types of analysis (growth, prices, ratings). However, it does not explain the period parameter or acceptable values, leaving ambiguity.

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 the tool analyzes market trends including company growth, price dynamics, and rating changes by region and category. It also mentions showing how the construction market develops over time, giving a clear purpose. However, it does not clearly distinguish from siblings like market_analytics or market_report.

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 implies analyzing trends over time but lacks when-to-use or when-not-to-use instructions.

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