mcp-server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output format (aggregated prices with avg/median/min/max, listing counts, top districts) and the comparison scope (2-4 cities), which is helpful. However, it doesn't mention potential limitations like data freshness, source reliability, or error handling, leaving some behavioral aspects unclear for a tool with no annotation coverage.

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 perfectly concise and front-loaded: the first sentence states the core functionality, and the second sentence adds the use case. Every word earns its place with no redundancy or fluff, making it easy for an agent to parse quickly.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the comparison output well but doesn't cover parameter details or potential behavioral constraints. For a tool with no annotations and 0% schema coverage, it should ideally provide more guidance on inputs and limitations to be fully 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 description doesn't explicitly mention parameters, but it implies the 'cities' parameter by stating 'Compare 2-4 Northern Cyprus cities' and hints at price types by mentioning 'aggregated prices'. With 0% schema description coverage, this adds meaningful context beyond the bare schema. However, it doesn't detail the 'type' parameter (sale/rent) or city options, so it doesn't fully compensate for the coverage 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 with specific verbs ('compare side-by-side') and resources ('Northern Cyprus cities'), listing exactly what gets compared (prices, listing counts, top districts) and the use case ('for deciding between investment locations'). It distinguishes itself from siblings like get_market_overview or search_listings by focusing on multi-city comparison rather than general data retrieval.

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 this tool ('useful for deciding between investment locations'), which implicitly suggests alternatives like get_market_overview for broader market data or search_listings for specific property searches. However, it doesn't explicitly state when not to use it or name specific sibling tools as alternatives, keeping it at a 4.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns (price, area, bedrooms, price-per-m², location, and value insights) and the input constraints (2-4 UUIDs). However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by return details and usage instructions in two concise sentences. Every sentence adds necessary information without redundancy, making it 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 complexity (comparison tool with no output schema and no annotations), the description is mostly complete. It covers purpose, usage, inputs, and outputs. However, it lacks details on error cases (e.g., invalid UUIDs) and behavioral traits like performance or limitations, which would enhance completeness for a tool without structured annotations.

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 description coverage is 100%, so the schema already documents the parameter. The description adds value by specifying that UUIDs come from 'search_listings results', providing context beyond the schema. However, it doesn't elaborate on parameter semantics like format or validation beyond what the schema states.

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 specific action ('compare side-by-side'), resource ('active Evlek property listings'), and scope ('2-4 listings'). It distinguishes from sibling tools like 'search_listings' (which provides UUIDs) and 'compare_cities' (which compares cities, not properties).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('Pass UUIDs from search_listings results') and provides clear alternatives by referencing 'search_listings' as the source for inputs. It also specifies the context ('active Evlek property listings') and the number of listings required (2-4).

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 describes the return data comprehensively but lacks behavioral details like rate limits, authentication needs, or error handling. However, it does specify the tool's scope (single district) and data types, which adds some context beyond basic purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by specific data points and a usage guideline. Every sentence adds value: the first defines the tool, the second lists outputs, and the third provides critical context. 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 no annotations and no output schema, the description does well by detailing the return data (e.g., listing counts, prices, buyer personas). However, it doesn't cover potential errors, data freshness, or format of the output. For a tool with 2 parameters and no structured output, it's mostly complete but has minor 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 description coverage is 0%, so the description must compensate. It doesn't explicitly mention the 'city' and 'district' parameters, but the usage guideline ('after compare_cities narrows the city') implies the need for a city context. This adds some semantic meaning, though not detailed parameter explanations. With 0 parameters explicitly covered, baseline 4 is appropriate as it provides indirect guidance.

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 specific action ('Returns a comprehensive profile') and resource ('for a single district'), listing detailed data points like listing counts, prices, yields, and buyer personas. It distinguishes from siblings by focusing on district-level profiling rather than city comparison or property search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool: 'Use after compare_cities narrows the city.' This provides clear sequencing guidance and implies an alternative (compare_cities) for initial filtering, helping the agent choose appropriately.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clarifies the tool provides authoritative information and includes a disclaimer about consulting lawyers, which adds useful context. However, it does not detail response format, potential limitations (e.g., data freshness), or error handling, leaving gaps for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by a concise list of topics and a necessary disclaimer. Every sentence earns its place by adding clarity or caution without redundancy, making it efficiently structured and appropriately sized.

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 moderate complexity (single enum parameter, no output schema, no annotations), the description is largely complete. It covers purpose, topics, and a key disclaimer. However, without an output schema, it could benefit from hinting at return format (e.g., structured data vs. text), slightly reducing completeness for agent invocation.

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 100% description coverage and includes an enum for the single parameter 'topic', making the schema self-explanatory. The description adds value by listing specific topic examples (e.g., 'title deed types (koçan)', 'PTP process'), which helps users understand the scope, though it does not provide additional syntax or format details 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's purpose with specific verbs ('get authoritative legal/procedural info') and resources ('Northern Cyprus property topics'), listing concrete examples like title deed types and foreign purchase rules. It distinguishes itself from sibling tools (e.g., compare_cities, get_market_overview) by focusing on legal information rather than market data or listings.

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 this tool ('authoritative legal/procedural info on Northern Cyprus property topics'), but does not explicitly state when not to use it or name specific alternatives among siblings. The advice to 'consult a lawyer for specific cases' implies limitations but lacks direct sibling comparisons.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool as a read operation ('returns'), implying it is non-destructive, and specifies the scope of data (e.g., by major city, includes taxes and foreign ownership rules). However, it does not address potential limitations such as data freshness, rate limits, or authentication needs, which are gaps for a tool with no annotation coverage.

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-structured sentence that efficiently lists all key data points (average rents, sale prices, rental yields, investment highlights, key facts) without unnecessary words. It is front-loaded with the main purpose and provides complete information in a concise manner, with every element earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (providing a comprehensive market overview) and the absence of annotations and output schema, the description does a good job of specifying the data returned (e.g., by city, includes yields and legal facts). However, it could be more complete by mentioning the format of the output or any assumptions (e.g., currency, time period), which would help compensate for the lack of structured output information.

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 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the output content. This meets the baseline for tools with no parameters, as it avoids redundancy and adds value by detailing the returned data.

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 with specific verbs ('returns') and resources ('market overview for Northern Cyprus property'), listing detailed content areas like average rents, sale prices, rental yields, investment highlights, and key facts. It distinguishes itself from siblings by focusing on comprehensive overview data rather than comparisons, legal details, indices, estimates, listings, or neighborhood suggestions.

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 obtaining a broad market overview, but does not explicitly state when to use this tool versus alternatives like 'compare_cities' or 'get_price_index'. It provides context by listing the types of data returned, but lacks explicit guidance on exclusions or named alternatives, leaving usage somewhat inferred rather than clearly defined.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the data source ('Based on all active listings on evlek.app') and output format ('aggregated average, median, min, max prices'), but lacks details on rate limits, freshness of 'live' data, error handling, or authentication needs, leaving gaps for a tool with no annotation coverage.

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 appropriately sized and front-loaded, with the first sentence stating the core purpose, followed by details on data aggregation and usage context. Every sentence adds value without redundancy, making it 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's moderate complexity (2 parameters with enums, no output schema, no annotations), the description provides a good overview but lacks completeness. It covers purpose and data source but omits details on output structure (e.g., format of aggregated data), error cases, or how parameters affect results, which could hinder precise agent invocation.

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, but the description compensates by explaining the parameters' context: 'per city' implies the 'city' parameter filters results by city, and the mention of 'prices' relates to the 'type' parameter (sale/rent). However, it does not detail the specific enum values or default behavior, leaving some semantic gaps.

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 with specific verbs ('Returns the live Evlek Price Index') and resources ('aggregated average, median, min, max prices per city and top districts'), distinguishing it from siblings like 'compare_cities' or 'search_listings' by focusing on aggregated index data rather than comparisons or individual listings.

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 context ('Useful for market analysis and investment decisions') but does not explicitly state when to use this tool versus alternatives like 'get_market_overview' or 'get_yield_estimate'. It provides general applicability without specific guidance on exclusions 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool returns (breakeven years, city averages comparison) but doesn't cover important behavioral aspects like whether this is a read-only calculation, what assumptions are made, data freshness, error conditions, or rate limits.

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 perfectly concise - two sentences that directly state what the tool does and what it returns. Every word earns its place with no redundancy or unnecessary elaboration.

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 calculation tool with 3 parameters, no annotations, and no output schema, the description provides basic purpose and return information but lacks details about calculation methodology, assumptions, error handling, or output format specifics that would be needed for complete understanding.

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 only 33% (only purchasePrice has a description), but the description adds meaningful context by explaining that parameters are used to calculate rental yield. It doesn't detail individual parameters beyond what's in the schema, but for a calculation tool with 3 parameters, this provides adequate semantic framing.

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 specific action ('calculate estimated gross and net annual rental yield'), resource ('a property'), and scope ('for a Northern Cyprus property'). It distinguishes from siblings like compare_cities or get_market_overview by focusing on yield calculation rather than comparison or market data.

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 versus alternatives. It doesn't mention prerequisites, when not to use it, or how it differs from siblings like get_price_index or suggest_neighborhood in practical scenarios.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully adds context about the return format ('up to 10 matching properties with title, price, location, and direct link') and the live nature of listings, but doesn't mention rate limits, authentication needs, or error conditions. It adequately describes the core behavior but lacks operational details.

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 efficiently structured in two sentences: the first states the purpose and filters, the second specifies the return format and limit. Every sentence adds essential information with zero wasted words, making it easy to parse and front-loaded with key details.

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 moderate complexity (6 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose, filters, and return format, but lacks usage guidelines, error handling, and detailed parameter semantics. It's adequate for a search tool but leaves gaps in operational 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 description coverage is 67% (4 of 6 parameters have descriptions), so the description must compensate. It adds value by explicitly listing filterable fields ('city, type, bedrooms, and price range'), which helps interpret the schema parameters. However, it doesn't clarify parameter interactions or provide examples, leaving some semantic gaps.

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 specific action ('Search live property listings on Evlek'), resource ('property listings'), and scope ('Northern Cyprus'), distinguishing it from siblings like compare_cities or get_market_overview which focus on different aspects of the property market. It uses precise verbs and identifies the target platform.

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 versus alternatives like compare_cities or get_market_overview. It lists filtering capabilities but doesn't explain scenarios where this search is preferred over other tools, 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 provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns recommendations based on 'expert knowledge', but fails to disclose critical traits like whether it's a read-only operation, potential rate limits, authentication needs, or how it handles invalid inputs, leaving significant gaps for a tool with 3 parameters.

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 appropriately sized and front-loaded, starting with the core function and ending with the knowledge source. Both sentences earn their place by specifying inputs and outputs, though it could be slightly more structured by explicitly separating required and optional parameters.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and inputs but lacks details on output format, error handling, or behavioral constraints, making it adequate but with clear gaps for effective agent 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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'persona' is a buyer type (e.g., retiree), 'budget' is optional and in GBP, and 'preferences' are optional criteria, clarifying the purpose of each parameter that the schema alone does not provide.

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 with specific verbs ('return 2-3 best-matched neighborhoods with rationale') and resources ('buyer persona', 'optional budget/preferences'), distinguishing it from siblings like search_listings or get_market_overview by focusing on personalized recommendations rather than data retrieval or comparisons.

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 by mentioning 'buyer persona' and 'optional budget/preferences', suggesting it's for matching neighborhoods to user profiles, but lacks explicit guidance on when to use this tool versus alternatives like search_listings or compare_cities, 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.