Civic Data MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation3/5
Tools are grouped by data source and purpose, but there is significant overlap. For example, get_country_indicators and compare_countries both use World Bank data, and get_weather_forecast and get_global_weather both provide weather information. Descriptions help clarify, but an agent might struggle to choose between overlapping tools like query_nasa and get_astronomy_photo.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., get_country_indicators, search_datasets, query_census). However, there are minor deviations, such as compare_countries (verb_noun but not 'get' or 'query') and get_astronomy_photo (abbreviated 'photo' instead of 'picture'). Overall, the naming is readable and predictable.
Tool Count2/5
With 22 tools, the count feels heavy for a 'Civic Data' server, as it spans multiple unrelated domains like astronomy, Mars photos, and global weather. This suggests a lack of focus; a more cohesive set might have 10-15 tools centered on government or economic data. The broad scope makes the toolset overwhelming and less coherent.
Completeness3/5
For each data source (e.g., World Bank, NASA, NOAA), there is basic coverage with get/search/query tools, but gaps exist. For instance, there are no update or delete operations, which is reasonable for read-only data, but some domains lack comprehensive endpoints (e.g., limited NASA tools beyond APOD and images). The surface is functional but not fully rounded for all implied use cases.
Average 3.4/5 across 22 of 22 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 4 of 7 community issues answered or closed in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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}
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'raw query' and 'raw JSON response' but doesn't disclose authentication requirements, rate limits, error handling, or whether this is a read-only operation versus a mutation. The description doesn't contradict annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately brief with a clear purpose statement followed by Args and Returns sections. However, the Args section could be more informative given the low schema coverage, and the structure is functional but not optimally front-loaded with critical usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It lacks details on authentication, error cases, response structure beyond 'raw JSON', and doesn't compensate for the missing parameter documentation, making it incomplete for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 but provides only basic examples ('/datasets', '/catalogues') without explaining parameter formats, constraints, or semantics. The 'params' field is particularly underspecified as 'Query parameters' with no guidance on structure or valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Make a raw query') and target resource ('EU Open Data Portal API'), distinguishing it from siblings like query_census or query_nasa. However, it doesn't explicitly differentiate from query_datagov or query_worldbank which might have similar raw query patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_eu_dataset_info or search_eu_datasets. The description mentions it's for 'raw query' but doesn't explain when a raw query is preferable over more structured sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool retrieves 'detailed information', but doesn't mention aspects like rate limits, authentication needs, error handling, or whether it's a read-only operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with clear sections for Args and Returns. It uses two sentences efficiently, though the second sentence could be more front-loaded. There's no unnecessary verbosity, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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, the description doesn't need to detail return values, which is appropriate. However, with no annotations and a simple parameter, the description provides basic purpose and parameter context but lacks behavioral details like error cases or usage prerequisites. It's minimally complete but could be more informative for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds minimal semantics beyond the input schema: it specifies that dataset_id is 'from search results', which provides context not in the schema. However, with 0% schema description coverage and only one parameter, this is adequate but not comprehensive. The baseline is 3 since the schema covers the parameter structure, but the description adds some value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'detailed information about a specific EU Open Data dataset', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'get_dataset_info' or 'query_eu_data', which might have overlapping functionality, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'search_eu_datasets' or 'query_eu_data'. It mentions the dataset_id is 'from search results', implying a prerequisite, but doesn't clarify the relationship or usage context compared to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool returns 'Raw JSON response from Data.gov API,' which hints at the output format, but fails to cover critical aspects like authentication requirements, rate limits, error handling, or whether it's a read-only or mutating operation. For a raw API query tool, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, with the core purpose stated first, followed by clear sections for 'Args' and 'Returns.' Each sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (raw API queries with 2 parameters), lack of annotations, and no output schema, the description is incomplete. It covers basic purpose and parameters but omits behavioral details like authentication, error handling, and usage context. For a tool that interacts with an external API, this leaves significant gaps in understanding for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds value by explaining that 'action' is a 'CKAN action' with examples (e.g., 'package_search'), and 'params' are 'Query parameters for the action.' However, it doesn't detail common actions or parameter structures beyond this, leaving gaps in understanding. This partial compensation justifies a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Make a raw query to the Data.gov CKAN API.' It specifies the verb ('query') and resource ('Data.gov CKAN API'), distinguishing it from siblings like 'search_datasets' or 'get_dataset_info' which likely provide higher-level abstractions. However, it doesn't explicitly contrast with these siblings, keeping it at a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 scenarios where a raw query is preferred over more specific sibling tools (e.g., 'search_datasets' for dataset searches), nor does it outline prerequisites or exclusions. This lack of contextual direction leaves the agent with minimal usage cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions that 'api_key will be added automatically,' which is useful context about authentication. However, it lacks details on rate limits, error handling, or response behavior beyond 'Raw JSON response,' leaving significant gaps for a tool that interacts with an external API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. The structured 'Args' and 'Returns' sections are efficient, though the example for 'endpoint' could be more concise. Overall, it avoids unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of querying an external API with no annotations and no output schema, the description is incomplete. It lacks details on authentication, rate limits, error cases, and the structure of the 'Raw JSON response,' making it inadequate for safe and effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 adds meaning by explaining 'endpoint' with examples (e.g., '/planetary/apod') and 'params' as query parameters with the note about api_key. This provides basic semantics beyond the bare schema, but it doesn't fully document all aspects like parameter formats or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Make a raw query to the NASA API.' It specifies the verb ('query') and resource ('NASA API'), though it doesn't explicitly differentiate from sibling tools like 'search_nasa_images' or 'get_astronomy_photo' beyond being a 'raw query' versus more specialized functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'search_nasa_images' or 'get_astronomy_photo', nor does it specify use cases or exclusions, leaving the agent to infer usage from the generic 'raw query' phrasing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states it returns 'Raw JSON response from NOAA API,' which hints at read-only behavior, but lacks details on authentication needs, rate limits, error handling, or what 'raw' implies (e.g., unprocessed data). This is inadequate for a tool with potential API complexities.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose followed by structured sections for Args and Returns. Each sentence adds value, with no wasted words, though it could be slightly more concise by integrating the examples more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (raw API queries with 2 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on authentication, rate limits, error cases, or how to interpret the raw JSON response, making it insufficient for safe and effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 'endpoint' with examples like '/points/38.8894,-77.0352' and '/alerts/active,' and clarifies 'params' as 'Optional query parameters as a dictionary,' compensating well for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Make a raw query to the NOAA Weather API,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_global_weather' or 'get_weather_forecast,' which likely provide more structured access to similar data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description mentions it's for 'raw' queries but doesn't explain scenarios where this is preferable over more specific tools like 'get_weather_forecast' or 'get_weather_alerts,' leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions that the tool returns a 'comparison table' but doesn't specify data sources (e.g., World Bank), time frames, error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with a clear purpose sentence, followed by concise sections for 'Args' and 'Returns'. Each sentence adds value without redundancy, making it easy to scan and understand quickly. No wasted words or unnecessary details are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 (implied by 'Returns' in the description), the description doesn't need to detail return values. However, with no annotations and only basic parameter semantics, it lacks information on data sources, constraints, or error conditions. For a comparison tool with 2 parameters, it's minimally adequate but could be more comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 'countries' should be a list of country codes with an example (['USA', 'CHN', 'IND']), and 'indicator' is a World Bank indicator code with a default (GDP). This compensates well for the schema's lack of descriptions, though it doesn't detail all possible indicator codes or country code formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Compare an economic indicator across multiple countries.' It specifies the verb ('compare') and resource ('economic indicator across multiple countries'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_country_indicators' or 'query_worldbank', which might offer similar functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 mentions a default indicator (GDP) but doesn't specify scenarios where this tool is preferred over siblings like 'get_country_indicators' or 'query_worldbank', nor does it outline prerequisites or exclusions. Usage is implied through the description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions what data is returned (median values, rent, vacancy rates) but lacks critical details: it doesn't specify data sources, timeframes, update frequency, rate limits, or error handling. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded, starting with the core purpose. It uses bullet points for 'Args' and 'Returns' to organize information efficiently, with no redundant sentences. Every part adds value, 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.
Completeness3/5
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 annotations, but with an output schema), the description is partially complete. It covers parameters and return types adequately, but lacks context on data sources, limitations, or sibling tool differentiation. The output schema likely details the return structure, so the description doesn't need to explain return values, but overall completeness is limited by missing behavioral and usage details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'state' is a 'Two-letter state code (e.g., 'CA', 'TX')' and 'county' is an 'Optional county FIPS code (3 digits)', clarifying format and examples not present in the schema. However, it doesn't detail validation rules or provide a full list of valid codes, leaving some ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get housing statistics for a US state or county.' It specifies the verb ('Get') and resource ('housing statistics'), and distinguishes it from siblings by focusing on US housing data. However, it doesn't explicitly differentiate from similar tools like 'get_demographics' or 'query_census', which might also provide related data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 siblings like 'get_demographics' or 'query_census', which could offer overlapping or complementary data. There's no context on prerequisites, such as data availability or limitations, leaving the agent to infer usage based on 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.
Behavior2/5
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 mentions the tool makes a 'raw query' and returns a 'Raw JSON response', which implies it's a read-only operation without side effects, but it doesn't cover critical aspects like authentication needs, rate limits, error handling, or API constraints. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose. It uses bullet points for Args and Returns, making it easy to scan. Every sentence adds value without redundancy, and there's no wasted text, earning a top score for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, no output schema), the description is partially complete. It covers the purpose and parameter meanings adequately but lacks behavioral details and usage guidelines. Without an output schema, it hints at the return type ('Raw JSON response'), but more context on response structure or errors would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'country' is a country code with examples ('USA', 'all'), 'indicator' is a World Bank indicator code with an example ('NY.GDP.MKTP.CD'), and 'params' are additional query parameters. This compensates well for the schema's lack of descriptions, though it could provide more detail on param formats.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Make a raw query to the World Bank API.' It specifies the verb ('query') and resource ('World Bank API'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'query_census' or 'query_nasa' beyond mentioning the specific API, which keeps it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 sibling tools like 'get_country_indicators' or 'get_population', which might offer similar or overlapping functionality, nor does it specify use cases, prerequisites, or exclusions. This lack of context leaves the agent to infer usage independently.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the return format ('List of matching EU datasets with titles and descriptions') but lacks details on permissions, rate limits, error handling, or pagination behavior. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by bullet-point-like sections for Args and Returns. Every sentence adds value without redundancy, making it easy to parse and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return format, though it lacks usage guidelines and detailed behavioral context. The presence of an output schema reduces the need to explain return values in depth.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds semantic context for both parameters: 'query' is explained as 'Search terms (e.g., 'environment', 'economy', 'transport')' and 'limit' as 'Number of results to return (default: 10, max: 50)'. With 0% schema description coverage, this compensates well, but it doesn't provide deeper insights like query syntax or result ordering.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as searching for datasets on the European Union Open Data Portal, using specific verbs ('Search for datasets') and identifying the resource ('European Union Open Data Portal'). However, it doesn't explicitly differentiate from sibling tools like 'search_datasets' or 'query_eu_data', which appear to have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With sibling tools like 'search_datasets', 'query_eu_data', and 'get_eu_dataset_info' available, there's no indication of context, prerequisites, or exclusions to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 fetches data (implied read-only) and specifies default indicators, but lacks details on rate limits, authentication needs, error handling, or data freshness. This is inadequate for a tool with external API dependencies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear opening sentence, followed by well-organized 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it easy to parse and front-loaded with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, external data source), the description covers purpose and parameters adequately, and an output schema exists to handle return values. However, it lacks behavioral details like rate limits or error cases, which are important for API-based tools, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% coverage. It explains 'country' accepts codes or names with examples ('USA', 'CHN'), clarifies 'indicators' is optional with defaults (GDP, population, poverty), and provides semantic context not in the schema. This compensates well for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get economic indicators') and resource ('for a country from the World Bank'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'query_worldbank' or 'get_population', which might offer overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'query_worldbank' or 'get_population'. The description mentions the data source (World Bank) but doesn't specify use cases, exclusions, or comparisons with siblings, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the tool searches and returns results, but lacks details on behavioral traits such as rate limits, authentication needs, pagination, error handling, or whether it's read-only/destructive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves operationally.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place by providing essential information without redundancy. It's appropriately sized for a tool with two parameters and an output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 annotations, but with an output schema), the description is reasonably complete. It covers the purpose, parameters, and return values. The output schema exists, so the description doesn't need to detail return structure. However, it lacks behavioral context and usage guidelines, which slightly reduces completeness for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 'query' accepts search terms with examples ('apollo 11', 'mars', 'hubble') and 'media_type' specifies types like 'image', 'video', or 'audio'. This clarifies parameter usage effectively, though it doesn't cover all possible nuances like format constraints or default behavior for 'media_type'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches NASA's image and video library, providing a specific verb ('search') and resource ('NASA's image and video library'). It distinguishes from siblings like 'get_astronomy_photo' or 'query_nasa' by specifying it's for searching images/videos/audio rather than fetching specific photos or general queries. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'get_astronomy_photo' or 'query_nasa'. It mentions the tool's function but doesn't specify scenarios, prerequisites, or exclusions. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool retrieves 'detailed metadata and download links', which is helpful, but it doesn't mention important behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise, with a clear purpose statement followed by brief sections for 'Args' and 'Returns'. Every sentence adds value without unnecessary elaboration, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there's an output schema (which handles return values), the description doesn't need to detail outputs. However, for a tool with no annotations and a simple input schema, the description adequately covers the basics but could be more complete by addressing behavioral aspects like read-only nature or error conditions, which are important for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'dataset_id' by specifying it should come 'from search results', which clarifies its source and usage beyond the schema's basic type definition. Since there's only one parameter and schema description coverage is 0%, this additional semantic information is valuable and compensates well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 a specific verb ('Get') and resource ('detailed information about a specific Data.gov dataset'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'query_datagov' or 'search_datasets', 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'dataset_id (from search results)', suggesting it should be used after searching for datasets. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'query_datagov' or 'search_datasets', leaving some ambiguity about the best context for its application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states what data is returned (age, race, income demographics) and the data source (American Community Survey), which is helpful. However, it doesn't describe important behavioral traits like whether this is a read-only operation (implied but not stated), potential rate limits, authentication requirements, error conditions, or how county-level data differs from state-level. The description adds some context but leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, then provides parameter details in a labeled 'Args' section, and concludes with return value information. Every sentence adds value: the first establishes scope, the parameter explanations are essential given schema gaps, and the return statement clarifies output content. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, US geographic focus), no annotations, and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers purpose, parameter semantics, and output content adequately. The main gap is lack of behavioral context like rate limits or error handling, but the output schema reduces the need for detailed return value explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial value beyond the input schema, which has 0% description coverage. It explains that 'state' requires a 'Two-letter state code' with examples ('CA', 'TX'), and 'county' is a 'FIPS code (3 digits)' and optional. This provides crucial semantic context that the bare schema lacks. The only minor gap is not clarifying if county codes are state-specific or national.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get demographic breakdown for a US state or county' with specific data sources (American Community Survey) and demographic categories (age, race, income). It distinguishes from siblings like 'get_population' or 'query_census' by focusing on detailed demographic breakdowns rather than general population counts or census queries. However, it doesn't explicitly contrast with all similar tools like 'get_housing_stats' which might overlap in socioeconomic data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the parameter descriptions (US state/county focus) and return data specification, suggesting it's for detailed demographic analysis rather than broader geographic or non-US queries. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_population' for basic counts or 'query_census' for raw census data, and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 the default and max for 'rows', which is useful, but doesn't cover other important aspects like rate limits, authentication requirements, pagination behavior, error handling, or what happens with invalid queries. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections (Args, Returns), uses bullet-like formatting, and contains no redundant information. Every sentence adds value: the purpose statement, parameter explanations with examples, and return value description. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (search with two parameters), no annotations, and the presence of an output schema (implied by 'Returns' statement), the description is reasonably complete. It covers purpose, parameters with semantics, and return values. However, it could benefit from more behavioral context (like rate limits or error cases) since annotations are absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 'query' accepts search terms with examples ('climate', 'census', 'health'), and specifies that 'rows' has a default of 10 and max of 50. This compensates well for the schema's lack of descriptions, though it doesn't detail parameter constraints beyond the max for 'rows'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for datasets on Data.gov' with a specific verb ('search') and resource ('datasets'). It distinguishes from siblings like 'get_dataset_info' (which retrieves specific dataset details) and 'query_datagov' (which might be broader). However, it doesn't explicitly differentiate from 'search_eu_datasets' (which searches a different platform).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the context of searching Data.gov datasets, but doesn't explicitly state when to use this tool versus alternatives like 'search_eu_datasets' (for EU data) or 'query_datagov' (which might have different functionality). It provides basic parameter guidance but lacks explicit when/when-not scenarios or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'raw query' and 'raw JSON response', which implies direct API interaction, but doesn't disclose important behavioral traits like rate limits, authentication requirements, error handling, or whether this is a read-only operation. The description provides minimal behavioral context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and front-loaded with the core purpose, followed by organized parameter documentation and return value clarification. Every sentence earns its place, with no wasted words. The Args/Returns formatting makes it easy to scan while maintaining complete information density.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 4-parameter tool with no annotations and no output schema, the description provides adequate parameter documentation but lacks important contextual information. It doesn't explain the nature of the 'raw JSON response' structure, doesn't mention rate limits or authentication, and provides no guidance on error scenarios. While the parameter coverage is excellent, other contextual gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for all 4 parameters. Each parameter gets specific examples and context: dataset paths like 'acs/acs5', variable codes as a list, geography specifications with examples, and year with default value. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Make a raw query') and target resource ('Census API'), distinguishing it from sibling tools that query different data sources like NASA, NOAA, or WorldBank. It provides a precise verb+resource combination that leaves no ambiguity about what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. While it's clear this is for Census data, there's no mention of when to choose it over other demographic tools like 'get_demographics' or 'get_population', nor does it specify prerequisites or constraints for effective use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states what the tool returns ('List of photo URLs') but lacks information about rate limits, authentication requirements, error conditions, pagination, or API constraints. The description doesn't contradict any annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, args, returns) and uses bullet-like formatting. Every sentence adds value, though the rover list could be more concise. It's appropriately sized for a 4-parameter tool with no annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (4 parameters, no annotations, but has output schema), the description provides good coverage. The output schema handles return values, so the description's brief 'Returns' statement is sufficient. It explains all parameters meaningfully, though could benefit from more behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 provides meaningful explanations for all 4 parameters: rover name options, sol definition, earth_date format, and camera examples. This adds substantial value beyond the bare schema, though it doesn't cover all possible camera values or parameter interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get photos') and resources ('from Mars rovers') with explicit rover names listed. It distinguishes this tool from sibling tools by focusing on Mars rover photos specifically, unlike other tools that handle astronomy photos, weather, census data, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through parameter explanations (e.g., 'alternative to sol'), but doesn't explicitly state when to use this tool versus alternatives like 'get_astronomy_photo' or 'search_nasa_images'. No explicit guidance on prerequisites or when-not-to-use scenarios is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only minimally describes behavior. It states what data is returned but doesn't cover error handling, rate limits, authentication needs, or whether this is a read-only operation (though 'Get' implies read). More behavioral context would be helpful 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured with a clear purpose statement followed by organized Args and Returns sections. Every sentence earns its place, providing essential information without redundancy. The formatting with clear section headers enhances readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 don't need description) and the description covers parameter semantics well, this is reasonably complete. The main gap is lack of behavioral context that would be important for a tool with zero annotation coverage, but the core functionality is well-described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by explaining both parameters: 'state' as 'Two-letter state code or state FIPS code' and 'county' as 'Optional county name or FIPS code'. It clarifies format examples and optionality, adding significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 verb ('Get') and resource ('population data for a US state or county'), distinguishing it from siblings like 'get_demographics' or 'query_census' by specifying geographic scope and data source (American Community Survey).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'US state or county' and 'American Community Survey', but provides no explicit guidance on when to use this tool versus alternatives like 'get_demographics' or 'query_census'. It doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions that it returns 'List of active weather alerts,' which indicates a read-only operation, but lacks details on permissions, rate limits, error handling, or data freshness. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 structured sections for 'Args' and 'Returns' that are efficient and waste-free. Every sentence earns its place by providing essential information without redundancy, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose, parameter semantics, and return type adequately. However, it lacks behavioral details like error cases or usage constraints, which slightly reduces completeness for a tool with no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining the 'state' parameter as 'Two-letter state code (e.g., 'CA', 'TX', 'NY'),' adding crucial semantic context beyond the schema's basic type. Since there is only one parameter, this is sufficient to achieve a high score, though not perfect due to lack of further details like validation or examples beyond the few given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get active weather alerts') and resource ('for a US state'), distinguishing it from sibling tools like 'get_weather_forecast' or 'get_global_weather' which serve different weather-related purposes. It precisely defines the tool's function without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'for a US state,' which provides some context, but it does not explicitly state when to use this tool versus alternatives like 'get_weather_forecast' or 'get_global_weather.' No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 mentions the tool fetches data from NASA's APOD service and describes the return format, but lacks details on rate limits, authentication needs, error handling, or data freshness. It adequately covers basic behavior but misses operational constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by labeled sections for arguments and returns. Every sentence adds value without redundancy, and it's front-loaded with the core functionality, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 optional parameter) and the presence of an output schema (which handles return values), the description is largely complete. It covers purpose, parameter usage, and return content. However, it could improve by addressing behavioral aspects like rate limits or error scenarios, which are not covered by annotations or schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for the single parameter, the description compensates by specifying the parameter's purpose ('Optional date in YYYY-MM-DD format'), default value ('default: today'), and format. This adds meaningful context beyond the bare schema, though it doesn't detail edge cases like invalid dates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get NASA's Astronomy Picture of the Day') and identifies the exact resource (APOD). It distinguishes itself from sibling tools like 'get_mars_rover_photos' or 'query_nasa' by focusing on a specific NASA service rather than general queries or other NASA data sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving APOD data, but provides no explicit guidance on when to use this tool versus alternatives like 'query_nasa' or 'search_nasa_images'. It lacks any mention of prerequisites, exclusions, or comparative context with sibling tools, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 API key requirement and that 'appid will be added automatically', which are useful behavioral details. However, it lacks information on rate limits, error handling, authentication specifics, or response structure beyond 'Raw JSON response'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear opening sentence stating the purpose, followed by bullet-point-like sections for Args and Returns. Every sentence adds value without redundancy, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a raw API query tool with no annotations and no output schema, the description is moderately complete. It covers the basic purpose, parameters, and return type, but lacks details on error cases, rate limits, or example usage that would help an agent invoke it correctly in various scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining both parameters: 'endpoint' is described with examples (e.g., '/data/2.5/weather'), and 'params' is clarified as query parameters with the note that 'appid will be added automatically'. This adds meaningful context beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Make a raw query') and target resource ('OpenWeather API'), distinguishing it from sibling tools like 'get_global_weather' or 'get_weather_forecast' by emphasizing its raw, direct API access nature rather than processed weather data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 (for raw API queries to OpenWeather) and mentions a prerequisite (requires OPENWEATHER_API_KEY), but does not explicitly state when not to use it or name specific alternatives among the sibling tools for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the data source (NOAA) and return content (current conditions + 7-day forecast), which is useful behavioral context. However, it doesn't mention rate limits, error conditions, authentication needs, or whether this is a read-only operation (though 'Get' implies read-only).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement followed by organized sections for Args and Returns. Every sentence adds value - no redundant information. The formatting with bullet-like sections enhances readability without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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 (which handles return values), the description provides adequate context for a weather forecast tool. It covers purpose, parameters with examples, and return content overview. However, for a tool with no annotations, it could benefit from mentioning operational constraints like rate limits or error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 provides concrete examples for both parameters (Washington DC coordinates) and clarifies they represent geographic coordinates for US locations. This adds meaningful context beyond the bare schema, though it doesn't explain parameter constraints like valid ranges.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 verb ('Get') and resource ('weather forecast'), specifying geographic scope ('US location by coordinates') and data source ('NOAA'). It distinguishes from siblings like 'get_global_weather' by focusing on US locations and from 'get_weather_alerts' by providing forecasts rather than alerts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly indicates when to use this tool (for US weather forecasts via coordinates) but doesn't explicitly state when not to use it or name alternatives. It doesn't provide guidance on prerequisites or comparisons with similar tools like 'get_global_weather' or 'query_noaa'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 API key requirement (important authentication context) and specifies 'current weather' (temporal scope), but doesn't cover other behavioral aspects like rate limits, error handling, response format details, or whether it's a read-only operation. It adds some value but leaves significant gaps in behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and front-loaded: the first sentence states the core purpose, followed by well-organized sections for prerequisites, parameters, and returns. Every sentence earns its place with no wasted words, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
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, API dependency), no annotations, but with an output schema (implied by 'Returns' section), the description is mostly complete. It covers purpose, prerequisites, parameters, and return concept, though it could benefit from more behavioral context like rate limits or error scenarios. The output schema reduces the need to detail return values, keeping it appropriately scoped.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing clear parameter documentation in the Args section. It explains both parameters (city and country_code) with examples and indicates country_code is optional, adding essential meaning beyond the bare schema. This is exactly what's needed when schema coverage is low.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get current weather'), resource ('for any city worldwide'), and scope ('worldwide'), distinguishing it from siblings like get_weather_forecast (future weather) and get_weather_alerts (alerts). It uses precise language that immediately communicates the tool's function without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying 'requires OPENWEATHER_API_KEY' as a prerequisite and indicating it's for 'current weather,' which implicitly distinguishes it from forecast tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings, leaving some room for improvement in direct comparison guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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