mcp-nordic

Server Quality Checklist

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Latest release: v0.4.0
Disambiguation4/5
The tools are well-organized by country and domain (e.g., address, weather, company), making it easy to distinguish between them. However, some overlap exists within domains, such as multiple company search tools per country (e.g., dk_cvr_lookup and dk_cvr_search), which could cause minor confusion if an agent is unsure which to use for specific queries.
Naming Consistency5/5
The naming follows a highly consistent pattern: country code prefix (dk_, fi_, no_, se_) followed by a descriptive verb_noun format (e.g., address_search, current_weather). This predictability makes it easy for agents to understand and navigate the toolset without ambiguity.
Tool Count3/5
With 33 tools, the count is borderline high for a single server, potentially overwhelming for agents. While the tools cover multiple Nordic countries and domains comprehensively, the scope might be better split into sub-servers (e.g., by country or function) to improve manageability and reduce cognitive load.
Completeness5/5
The toolset provides complete coverage for its intended domain of Nordic data, including addresses, weather, and company registries across Denmark, Finland, Norway, and Sweden. It supports key operations like search, lookup, and comparison, with no obvious gaps that would hinder agent workflows for these use cases.
Average 3.4/5 across 33 of 33 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return includes 'detailed information including registers, addresses, and history,' which adds some context beyond the input schema. However, it lacks critical details such as error handling (e.g., for invalid IDs), rate limits, authentication requirements, or data freshness—important for a lookup tool with potential external dependencies.
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 front-loaded, consisting of two clear sentences: one stating the action and input, and another outlining the return information. There's no wasted text, though it could be slightly more structured (e.g., bullet points for return details) for a perfect score.
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 (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return scope but lacks usage guidelines, behavioral details (e.g., error cases), and output specifics. This makes it minimally viable but with clear gaps for an agent to operate effectively.
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 100%, with the parameter 'business_id' well-documented in the schema as 'Finnish Business ID (Y-tunnus), e.g., '0112038-9' or '2331972-6'.' The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where the schema does the heavy lifting.
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: 'Look up a Finnish company by Business ID (Y-tunnus).' It specifies the verb ('Look up'), resource ('Finnish company'), and key identifier ('Business ID'). However, it doesn't explicitly differentiate from sibling tools like 'fi_search_companies' or 'no_company_lookup', which would be needed for a score of 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 sibling tools (e.g., 'fi_search_companies' for broader searches or 'no_company_lookup' for Norwegian companies) or specify use cases like exact-match lookups versus fuzzy searches. This lack of comparative context leaves the agent without clear selection criteria.
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 what types of queries are supported (street names, full addresses, postal codes, municipality filtering) but doesn't describe response format, error conditions, rate limits, authentication needs, or whether this is a read-only operation. For a search tool with 5 parameters, this is insufficient.
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 concise - a single sentence that efficiently communicates the core functionality. Every word earns its place, with no redundant information or unnecessary elaboration.
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 search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (address objects? coordinates? partial matches?), error handling, or performance characteristics. The agent would need to guess about the response format and behavior.
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 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by listing the supported query types (street names, full addresses, postal codes, municipality filtering), which helps contextualize the 'query' parameter but doesn't provide additional semantics beyond what's in the 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: 'Search for Norwegian addresses by text query.' It specifies the resource (Norwegian addresses) and verb (search), but doesn't explicitly differentiate from sibling tools like 'no_municipality_addresses' or 'no_postal_code_lookup' that might overlap in 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. With multiple Norwegian address-related tools in the sibling list (no_municipality_addresses, no_postal_code_lookup, no_reverse_geocode), there's no indication of which scenarios favor this general search tool over more specific ones.
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 action and return type ('detailed info') but omits critical details like rate limits, authentication needs, error handling, or what 'detailed info' entails. This is insufficient for a tool with potential external API calls.
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 a single, efficient sentence that front-loads the core functionality without any fluff. Every word contributes to understanding the tool's purpose, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'detailed info' includes, potential errors, or behavioral traits like idempotency or side effects. For a lookup tool that likely interacts with external data, more context is needed.
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 100%, so the parameter 'org_number' is well-documented in the schema. The description adds minimal value by reinforcing it's for 'organization number' but doesn't provide additional context like format examples or validation rules beyond the schema.
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 ('look up'), resource ('Norwegian company'), and key identifier ('organization number'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'no_search_companies' or 'no_company_roles', which prevents 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?
No guidance is provided on when to use this tool versus alternatives such as 'no_search_companies' or 'fi_company_lookup'. The description lacks context about prerequisites, exclusions, or comparative use cases, leaving the agent without direction on 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 it's a 'Get' operation (implying read-only), but doesn't clarify permissions, rate limits, error conditions, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it 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 a single, efficient sentence that front-loads the core purpose. Every word earns its place by specifying the action, resource type, and geographic scope without redundancy or fluff.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., list of roles with details), potential errors, or operational constraints. For a tool that presumably returns structured data, this lack of output information is a significant gap.
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 100%, with the single parameter 'org_number' fully documented as a '9-digit organization number'. The description adds no additional parameter semantics beyond implying the tool uses this to identify a Norwegian company. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 roles') and target resource ('for a Norwegian company'), specifying the types of roles (board members, CEO, auditor, etc.). It distinguishes from most siblings by focusing on company roles rather than weather, addresses, or general company lookups. However, it doesn't explicitly differentiate from 'no_company_lookup' which might also retrieve company information.
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 prerequisites (e.g., needing an org_number), exclusions, or comparisons with sibling tools like 'no_company_lookup' or 'no_search_companies'. The agent must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 lists included weather metrics (temperature, wind, precipitation, humidity, cloud cover) but doesn't cover critical aspects like rate limits, authentication needs, error handling, data freshness, or response format. For a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise and front-loaded, with the core purpose stated first followed by details on included metrics. Both sentences earn their place by clarifying scope and data elements. It avoids redundancy and waste, though it could be slightly more structured (e.g., separating usage context from data details).
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 lack of annotations and output schema, the description is incomplete for effective tool use. It covers what data is included but omits behavioral traits (e.g., rate limits, errors), response structure, and usage guidelines relative to siblings. For a tool with no structured support, the description should provide more comprehensive context to compensate.
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 input schema has 100% description coverage, with the 'location' parameter clearly documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples of valid location formats beyond those implied). Since the schema does the heavy lifting, the baseline score of 3 is appropriate, though the description doesn't compensate or enhance parameter understanding.
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 current weather for a location in Norway using MET Norway (yr.no).' It specifies the action ('Get'), resource ('current weather'), and geographic scope ('Norway'), distinguishing it from sibling tools like 'dk_current_weather' or 'se_current_weather'. However, it doesn't explicitly differentiate from 'no_weather_forecast', which is a minor gap.
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 the data source ('MET Norway (yr.no)') but doesn't specify when to choose it over sibling tools like 'no_weather_forecast' or other country-specific weather tools. There's no mention of prerequisites, exclusions, or comparative contexts.
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 full burden. It mentions searching but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior, or what the output format looks like. For a search tool with no annotation coverage, this 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 a single, efficient sentence that clearly states the tool's purpose without unnecessary words. 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what results look like, how filtering works, or any constraints beyond the basic purpose. The context signals indicate significant complexity that isn't addressed in the description.
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 100%, so the schema already documents all four parameters well. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'query' interacts with 'parent_org_number' or format requirements). Baseline 3 is appropriate when schema does the heavy lifting.
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 for 'sub-units (underenheter) - branches, offices, departments of Norwegian companies', specifying the verb (search), resource (sub-units), and geographic scope (Norwegian). However, it doesn't explicitly differentiate from sibling tools like 'no_search_companies' or 'no_company_lookup', which likely search for different entity types.
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 searching for sub-units of Norwegian companies, but doesn't specify prerequisites (e.g., needing a parent company identifier) or contrast it with sibling tools that might handle similar Norwegian business data.
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 data source ('SMHI') and that it returns hourly forecast data, but lacks details on error handling, rate limits, authentication needs, data freshness, or what happens with invalid inputs. For a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that efficiently convey the core functionality. There's no wasted verbiage, and it directly addresses what the tool does and what it returns. However, it could be slightly more structured by explicitly separating purpose from output details.
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 output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output format but lacks details on behavioral traits, error cases, or usage context. Without annotations or an output schema, the agent has incomplete information about how to handle the tool effectively in real-world scenarios.
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 100%, with both parameters ('location' and 'hours') well-documented in the schema. The description adds minimal value beyond the schema, only implying that 'hours' controls the forecast duration ('next N hours'). No additional syntax, format details, or examples are provided, so the baseline score of 3 is appropriate given the schema does the heavy lifting.
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 weather forecast for a location in Sweden using SMHI. Returns hourly data for the next N hours.' It specifies the verb ('Get'), resource ('weather forecast'), geographic scope ('Sweden'), data source ('SMHI'), and output format ('hourly data'). However, it doesn't explicitly differentiate from sibling tools like 'se_current_weather' or other country-specific forecast tools, which prevents 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 'se_current_weather' (for current weather vs. forecast) or other country-specific forecast tools (e.g., 'dk_weather_forecast'), nor does it specify any prerequisites, exclusions, or contextual triggers for usage. The agent must infer usage from the description 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 output fields (city/area name, bounding box, municipalities) but lacks details on permissions, rate limits, error handling, or response format. For a lookup tool with zero 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 a single, efficient sentence that front-loads the core purpose and output details without any wasted words. It's appropriately sized for a simple lookup tool with one parameter.
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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output fields but lacks behavioral context and usage guidelines. Without an output schema, it should ideally describe the return format more explicitly, but it's not critically incomplete.
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 no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100% (the 'postal_code' parameter is well-documented with type, pattern, and an example), the baseline score of 3 is appropriate. The description doesn't compensate but doesn't need to given the schema's completeness.
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: 'Look up a Danish postal code to get the city/area name, bounding box, and associated municipalities.' It specifies the verb ('look up'), resource ('Danish postal code'), and output details. However, it doesn't explicitly differentiate from sibling tools like 'no_postal_code_lookup' or 'dk_address_search', 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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like 'dk_address_search' or 'dk_municipality_lookup' that might serve similar purposes, nor does it specify prerequisites or exclusions. Usage is implied but not explicitly defined.
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 'current weather' but doesn't specify what data is returned (e.g., temperature, humidity), format, or any limitations like rate limits or authentication needs. For a tool with zero 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('compare current weather', 'two Finnish locations', 'side by side') contributes directly to understanding the tool's function.
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 output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks details on output format, error handling, or sibling differentiation. Without annotations or output schema, more behavioral context would improve completeness.
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 value beyond the input schema, which already has 100% coverage with clear parameter descriptions. It implies both parameters are locations but doesn't elaborate on valid formats (e.g., 'Helsinki' vs coordinates) or constraints. Baseline 3 is appropriate since the schema does most of the work.
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 ('compare current weather') and resource ('between two Finnish locations'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'fi_current_weather' or 'dk_compare_weather', which would require mentioning the side-by-side comparison aspect as unique.
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 'fi_current_weather' (for single location) or 'fi_weather_forecast' (for future predictions). It also lacks context about prerequisites or limitations, such as whether both locations must be in Finland.
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. The description mentions the TOL 2008 classification system and suggests use cases, but it doesn't disclose important behavioral traits such as whether this is a read-only operation, potential rate limits, authentication requirements, pagination behavior (implied by the 'page' parameter but not explained), or what the output format looks like. For a search tool with no annotation coverage, this 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that each serve a purpose: the first states the core functionality, and the second suggests use cases. It's front-loaded with the main purpose. There's no unnecessary repetition or fluff, though it could potentially be more structured with explicit guidance.
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 a company search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (company details, counts, etc.), how results are structured, pagination behavior, or error conditions. The use case hint is helpful but insufficient for an agent to fully understand the tool's behavior and output.
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 100%, meaning all parameters are documented in the input schema. The description adds minimal value beyond the schema: it mentions the TOL 2008 classification for the 'industry' parameter and suggests use cases, but doesn't provide additional semantic context for parameters like 'location' or 'company_form'. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Find Finnish companies by industry (TOL 2008 classification).' It specifies the verb ('Find'), resource ('Finnish companies'), and classification system. However, it doesn't explicitly differentiate from sibling tools like 'fi_search_companies' or 'fi_company_lookup', which appear to be related company search tools.
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 provides implied usage context: 'Useful for market research and competitor analysis.' This suggests when the tool might be appropriate, but it doesn't offer explicit guidance on when to use this tool versus alternatives like 'fi_search_companies' or 'fi_company_lookup', nor does it specify any exclusions or prerequisites for usage.
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 forecast range ('Up to 16 days ahead') but lacks details on rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this is insufficient to inform safe and effective use.
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 concise and front-loaded with essential information in two sentences. It efficiently conveys the core functionality without unnecessary details, 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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and scope but lacks behavioral details and usage guidelines, which are needed for a fully informed agent. The absence of an output schema means the description should ideally hint at return values, but it does not.
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 100%, so the input schema fully documents parameters. The description adds minimal value beyond the schema by implying the tool supports both hourly and daily forecasts and a 16-day limit, but does not provide additional semantic context or examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 hourly or daily weather forecast for a location in Finland. Up to 16 days ahead.' It specifies the action ('Get'), resource ('weather forecast'), and scope ('Finland'), but does not explicitly differentiate it from sibling tools like 'fi_current_weather' or 'fi_compare_weather', which prevents a score of 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 does not mention sibling tools (e.g., 'fi_current_weather' for current conditions or 'fi_compare_weather' for comparisons), nor does it specify prerequisites or exclusions, leaving usage context unclear.
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 full burden. It states it's a listing tool with filtering, implying read-only behavior, but doesn't disclose pagination, rate limits, authentication needs, error conditions, or output format. For a tool with no annotations, this leaves significant behavioral 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 extremely concise—two short sentences with zero wasted words. It's front-loaded with the core purpose and efficiently adds the filtering capability. Every sentence earns its place.
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 no annotations and no output schema, the description is incomplete for a tool that returns address data. It covers the basic purpose and one parameter aspect but lacks details on output structure, error handling, or behavioral constraints. It's minimally adequate but has clear gaps.
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 100%, so parameters are well-documented in the schema. The description adds minimal value beyond the schema by mentioning the street filter, but doesn't provide additional context like format examples or usage tips. Baseline 3 is appropriate given high 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 tool's purpose: 'List addresses in a Norwegian municipality' specifies the verb (list) and resource (addresses), with geographic scope (Norwegian municipality). It distinguishes from some siblings like 'no_address_search' by focusing on municipality-based listing rather than general search, though not all sibling distinctions are explicit.
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 minimal guidance: it mentions filtering by street name but doesn't explain when to use this tool versus alternatives like 'no_address_search' or 'no_postal_code_lookup'. No context on prerequisites, typical use cases, or exclusions 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?
No annotations are provided, so the description carries full burden. It mentions the data source (SMHI) and what data is included, but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.
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 a single, efficient sentence that front-loads the core purpose. It includes necessary details (Sweden, SMHI, weather elements) without unnecessary elaboration. Could be slightly improved by structuring usage guidance separately.
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 no annotations and no output schema, the description provides basic purpose and data elements but lacks important context about behavior, response format, and error conditions. It's minimally adequate for a simple weather lookup tool but leaves gaps that could hinder effective use.
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 100%, so the schema already documents the 'location' parameter. The description doesn't add any parameter-specific information beyond what's in the schema. Baseline score of 3 is appropriate when the schema does the heavy lifting.
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 current weather for a location in Sweden using SMHI' with specific details like temperature, wind, precipitation, and humidity. It distinguishes from siblings by specifying Sweden and SMHI, though it doesn't explicitly contrast with other weather tools like 'se_weather_forecast'.
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 current weather in Sweden, but provides no explicit guidance on when to use this tool versus alternatives like 'se_weather_forecast' or other country-specific weather tools. It mentions the data source (SMHI) which gives some context, but lacks clear when-to-use or when-not-to-use instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 is for 'exploring an area or finding what's around a location,' which implies a read-only, non-destructive operation, but it doesn't specify details like rate limits, authentication needs, error handling, or what the output looks like (e.g., format, pagination). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, consisting of two sentences: one stating the purpose and another providing usage context. Every sentence earns its place without redundancy, making it easy to scan and understand quickly. It's appropriately sized for the tool's complexity.
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 no annotations and no output schema, the description is somewhat incomplete. It covers the basic purpose and usage but lacks details on behavioral traits (e.g., performance, errors) and output format. However, the input schema is well-documented, and the description provides adequate context for a simple query tool, though it could benefit from more completeness given the absence of structured fields.
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 input schema has 100% description coverage, with clear parameter descriptions (e.g., 'Center latitude', 'Search radius in meters'). The description adds minimal value beyond the schema by implying the tool uses a coordinate and radius, but it doesn't provide additional semantics like units clarification or usage examples. Given the high schema coverage, a baseline score of 3 is appropriate as the schema does most of the work.
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: 'Find addresses within a radius of a coordinate.' It specifies the verb ('Find'), resource ('addresses'), and scope ('within a radius of a coordinate'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'dk_address_search' or 'dk_reverse_geocode', which might have overlapping functionality, so it doesn't reach a score of 5.
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 provides implied usage guidance by stating it's 'Useful for exploring an area or finding what's around a location.' This gives context for when to use the tool, but it doesn't explicitly mention when not to use it or name alternatives among the many sibling tools. For example, it doesn't clarify if this is better than 'dk_address_search' for radius-based queries, so it's not fully explicit.
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 is for 'tracking new business formation trends,' which hints at read-only behavior, but doesn't explicitly state whether it's a read operation, its data source, rate limits, or error handling. This leaves significant gaps in understanding how the tool behaves beyond its 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste: the first states the purpose, and the second adds contextual value about usage. It's front-loaded with the core function and efficiently structured, earning a top score for brevity and clarity.
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 (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and hints at usage but lacks details on behavioral traits, output format, or error handling. With no annotations to fill gaps, it's complete enough for basic understanding but leaves room for improvement in operational context.
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 input schema has 100% description coverage, with each parameter clearly documented (e.g., 'days_back' for time range, 'location' for filtering). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating 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 tool's purpose: 'Find recently registered Finnish companies.' It specifies the verb ('Find') and resource ('recently registered Finnish companies'), making the action and target explicit. However, it doesn't differentiate from sibling tools like 'fi_search_companies' or 'fi_search_by_industry,' which limits its score to 4 instead of 5.
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 provides implied usage context with 'Great for tracking new business formation trends,' suggesting it's suited for trend analysis. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'fi_search_companies' for broader searches) or any exclusions, so it doesn't fully meet the criteria for higher scores.
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 data source ('MET Norway (yr.no)') and that it 'Returns hourly data for the next N hours,' which adds some context. However, it lacks details on rate limits, authentication needs, error handling, or what the return format looks like (e.g., JSON structure). For a tool with no annotations, this leaves significant behavioral 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 concise and front-loaded, consisting of two sentences that efficiently convey the core functionality: getting weather forecasts for Norway with hourly data. There is no wasted language, and every sentence contributes essential information, making it easy 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 output schema, no annotations), the description is adequate but incomplete. It covers the purpose and basic behavior but lacks details on output format, error cases, or usage compared to siblings. Without annotations or an output schema, more context on what the return data looks like would be beneficial for an agent to use this tool effectively.
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 input schema has 100% description coverage, with clear documentation for both parameters ('location' and 'hours'). The description adds minimal value beyond the schema, only implying that 'hours' controls the forecast duration ('the next N hours'). Since the schema already fully describes the parameters, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.
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 weather forecast for a location in Norway using MET Norway (yr.no).' It specifies the verb ('Get'), resource ('weather forecast'), and geographic scope ('Norway'), distinguishing it from sibling tools like 'dk_weather_forecast' or 'se_weather_forecast'. However, it doesn't explicitly differentiate from 'no_current_weather' or 'no_compare_weather', which are related weather tools for Norway.
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 'for a location in Norway' and 'Returns hourly data for the next N hours,' suggesting this is for future forecasts rather than current conditions. However, it doesn't explicitly state when to use this tool versus alternatives like 'no_current_weather' or 'no_compare_weather', nor does it provide exclusions or prerequisites. The guidance is present but not comprehensive.
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 returns addresses within a specified radius, but doesn't disclose important traits like whether it's read-only, rate limits, authentication needs, error conditions, or what format the addresses are returned in. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just two sentences that efficiently communicate the core purpose and key constraint. Every word earns its place, and the information is front-loaded with the main functionality stated first.
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 has no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (address format, structure), error conditions, or important behavioral aspects. For a 4-parameter tool with no structured output documentation, the description should provide more context about what to expect from the operation.
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 mentions 'within the specified radius' which corresponds to the 'radius' parameter, but doesn't add meaningful semantic context beyond what the 100% schema coverage already provides. The schema descriptions fully document each parameter's purpose, constraints, and defaults, so the description adds minimal value here.
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 ('Find Norwegian addresses near a geographic point') and resource ('addresses'), distinguishing it from siblings like 'no_address_search' or 'no_postal_code_lookup' by specifying reverse geocoding. It explicitly mentions the geographic scope (Norway) and the operation type (reverse geocoding).
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 stating 'Find Norwegian addresses near a geographic point (reverse geocoding)', suggesting it should be used when you have coordinates and need nearby addresses. However, it doesn't explicitly state when NOT to use it or name alternatives like 'dk_reverse_geocode' for Denmark or 'no_address_search' for address-based searches, leaving some ambiguity.
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 full burden. It mentions 'current weather' and 'side by side' comparison, which implies read-only behavior and output format. However, it lacks critical behavioral details: whether it requires authentication, rate limits, error handling, data freshness, or what specific weather metrics are compared (temperature, precipitation, etc.). For a tool with no annotations, this is insufficient disclosure.
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 concise: two sentences with zero waste. The first sentence states the core functionality, and the second provides usage context. Every word earns its place, and it's front-loaded with the essential purpose.
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 no annotations and no output schema, the description is moderately complete. It covers the basic purpose and usage context but lacks behavioral details (e.g., output format, error cases) and doesn't fully compensate for the missing structured data. For a comparison tool with two simple parameters, it's adequate but has clear gaps in transparency.
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 100%, with both parameters clearly documented in the schema. The description adds no additional parameter information beyond implying two locations are needed for comparison. Since the schema already fully describes the parameters, the baseline score of 3 is appropriate—the description doesn't add value here but doesn't need to compensate.
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 current weather between two Danish locations side by side.' It specifies the verb ('compare'), resource ('current weather'), and scope ('Danish locations'), distinguishing it from generic weather tools. However, it doesn't explicitly differentiate from sibling 'fi_compare_weather' (Finnish version) beyond the 'Danish' qualifier.
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 usage context: 'Useful for deciding between destinations or comparing conditions across the country.' This gives practical scenarios when to use the tool. However, it doesn't specify when NOT to use it (e.g., vs. 'dk_current_weather' for single locations or 'dk_weather_forecast' for future conditions), nor does it explicitly name 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?
No annotations are provided, so the description carries full burden. It mentions what data is included but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, error handling, or response format. For a tool with no annotations, this leaves significant gaps in understanding how it 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 a single, efficient sentence that front-loads the core purpose and lists included data without unnecessary words. Every part earns its place by clarifying scope and content.
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 no annotations and no output schema, the description is moderately complete for a simple query tool but lacks details on behavioral aspects and return values. It covers the basic purpose and data scope but doesn't compensate for missing structured information, leaving room for improvement in context.
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 100%, with the parameter 'location' fully documented in the schema. The description adds no additional parameter semantics beyond implying Finnish locations, which is already covered by the schema's examples. Baseline 3 is appropriate as the schema does the heavy lifting.
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 verb ('Get') and resource ('current weather conditions for a location in Finland'), with specific details on included data (temperature, wind, precipitation, humidity, cloud cover). It distinguishes from sibling tools like 'fi_weather_forecast' by specifying 'current' conditions rather than forecasts.
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 current weather in Finland but doesn't explicitly state when to use this vs. alternatives like 'fi_compare_weather' or 'fi_weather_forecast'. No exclusions or prerequisites are mentioned, leaving usage context partially implied rather than fully guided.
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 lacks behavioral details. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication requirements, or what happens with partial/no matches. The mention of 'search' implies querying, but no further behavioral traits are described.
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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and lists search criteria clearly without unnecessary elaboration. Every word earns its place.
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 search tool with 7 parameters, 100% schema coverage, and no output schema, the description is adequate but has gaps. It clarifies the registry source and searchable fields, but without annotations or output schema, it doesn't address behavioral aspects like result format, pagination, or error handling. Minimum viable for a read operation.
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 100%, so the schema fully documents all 7 parameters. The description adds minimal value beyond the schema by listing searchable fields (name, industry, municipality, organization form) but doesn't provide additional syntax, format details, or usage examples. Baseline 3 is appropriate when schema does the heavy lifting.
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 ('Search Norwegian company registry') and resource ('Enhetsregisteret'), with explicit search criteria ('by name, industry, municipality, or organization form'). It distinguishes from siblings like 'no_company_lookup' (likely for specific lookups) and 'no_search_subunits' (different scope).
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 searching Norwegian companies with various filters, but provides no explicit guidance on when to use this tool versus alternatives like 'no_company_lookup' or 'fi_search_companies'. It mentions searchable fields but doesn't specify prerequisites, exclusions, or comparative contexts with 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 mentions the tool finds cheapest hours 'today/tomorrow,' which hints at temporal scope, but does not disclose critical behavioral traits such as data sources, update frequency, rate limits, authentication needs, or error handling. The description adds minimal context beyond the 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two concise sentences: the first states the core purpose, and the second adds practical use cases. Every sentence earns its place without redundancy or unnecessary elaboration, 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.
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 (3 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers the purpose and usage context well, but lacks details on behavioral traits, output format, or error handling. Without annotations or output schema, the description should do more to compensate, but it meets minimum viability.
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 100%, so the schema already documents all parameters (area, count, consecutive) with their types and defaults. The description does not add any meaning beyond what the schema provides, such as explaining parameter interactions or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 a specific verb ('Find') and resource ('cheapest hours to use electricity today/tomorrow'), and distinguishes it from siblings like 'dk_electricity_prices' by focusing on identifying optimal time slots rather than just providing price data. The inclusion of use cases (EV charging, laundry, etc.) further clarifies its practical application.
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 ('useful for scheduling EV charging, laundry, dishwasher, heat pumps, etc.'), but does not explicitly state when not to use it or name alternatives. It implies usage for cost optimization in electricity consumption, though lacks explicit exclusions or comparisons to siblings like 'dk_electricity_prices'.
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 it 'Returns full company details', which adds some behavioral context about output, but it doesn't disclose other traits like rate limits, authentication needs, error handling, or whether it's read-only/destructive. This is inadequate 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 a single, efficient sentence that front-loads the key information (look up by CVR, returns details). There's no wasted text, and it's appropriately sized for the tool's complexity.
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 no annotations and no output schema, the description is minimal but covers the basic purpose. It doesn't fully compensate for the lack of behavioral details or output information, making it adequate but with clear gaps for a lookup tool.
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 100%, so the schema fully documents both parameters. The description doesn't add any parameter semantics beyond what's in the schema (e.g., it doesn't explain the 'country' parameter's relevance or default behavior). Baseline 3 is appropriate when the schema does the heavy lifting.
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 a specific verb ('Look up') and resource ('Danish company'), and it distinguishes from sibling tools like 'dk_cvr_search' by specifying it's for looking up a specific company by CVR number rather than searching.
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 implies usage context by specifying 'Danish company' and 'CVR number', but it doesn't explicitly state when to use this tool versus alternatives like 'dk_cvr_search' or other country-specific company lookup tools. It provides clear prerequisites but lacks explicit 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool returns (company details) and supports (Norwegian companies), but lacks critical information such as rate limits, authentication requirements, error handling, or pagination. For a search 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 appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose, search capabilities, return details, and country support. Every sentence adds value without redundancy, making it easy 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 complexity of a search tool with no annotations and no output schema, the description is moderately complete. It covers the basic purpose and usage but lacks details on behavioral traits (e.g., rate limits) and output structure. Without an output schema, it should ideally describe return values more explicitly, but it does mention key details like address and industry, which helps partially compensate.
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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the search scope and Norwegian support, but it does not provide additional syntax, format details, or examples. This meets the baseline for high schema coverage without enhancing parameter understanding.
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 verbs ('search') and resources ('Danish CVR registry for a company'), and it distinguishes itself from siblings like 'dk_cvr_lookup' by emphasizing search functionality across multiple fields (name, CVR, P-number, phone) rather than a direct lookup. It also explicitly mentions support for Norwegian companies, which is unique among the sibling tools.
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 on when to use this tool by specifying the types of queries (name, CVR, P-number, phone) and countries (Denmark, Norway). However, it does not explicitly state when not to use it or name alternatives, such as 'dk_cvr_lookup' for direct CVR lookups or other country-specific tools, leaving some ambiguity in sibling differentiation.
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 full burden. It mentions returning 'the closest address with full details,' which gives some behavioral insight, but does not disclose potential limitations (e.g., accuracy, rate limits, authentication needs, error handling, or what 'full details' entails). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with zero waste: the first states the purpose and input, the second clarifies the output. It is front-loaded with the core functionality and efficiently structured.
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 no annotations and no output schema, the description is moderately complete for a simple lookup tool. It covers the basic purpose and output intent ('full details'), but lacks details on return format, error cases, or operational constraints. For a tool with 2 parameters and 100% schema coverage, it meets minimum viability but could be more informative.
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 100%, so the schema already documents both parameters thoroughly with descriptions and numeric ranges. The description adds minimal value by specifying 'latitude/longitude coordinate' but does not provide additional semantics beyond what the schema offers. With 0 parameters needing extra explanation, a baseline of 4 is appropriate as it slightly reinforces the purpose.
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 ('Find the nearest Danish address') with the resource ('given latitude/longitude coordinate') and distinguishes it from siblings like 'dk_address_search' and 'dk_nearby_addresses' by specifying reverse geocoding to a single nearest address rather than searching or listing multiple addresses.
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 converting coordinates to addresses in Denmark, but does not explicitly state when to use this tool versus alternatives like 'dk_nearby_addresses' (which might return multiple addresses) or 'no_reverse_geocode' (for Norway). It provides basic context but lacks explicit exclusions or comparisons.
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 is 'free', which hints at no cost, but does not disclose other traits like rate limits, authentication needs, response format, or pagination behavior (beyond the 'page' parameter in the schema). It adds some context (e.g., data source) but lacks details on operational constraints or expected outcomes.
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 a single, efficient sentence that front-loads key information (action, resource, search criteria, source) with no wasted words. It is appropriately sized for the tool's complexity, making it easy to parse 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 tool's moderate complexity (7 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and source well but lacks details on behavioral traits (e.g., rate limits, response format) and does not fully compensate for the absence of annotations and output schema, leaving the agent with incomplete operational context.
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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by listing search criteria ('by name, location, business ID, or company form'), but it does not provide additional syntax, format details, or usage examples. This meets the baseline for high schema coverage.
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 ('Search Finnish company registry'), resource ('companies'), and scope ('by name, location, business ID, or company form'), distinguishing it from sibling tools like 'fi_company_lookup' and 'fi_search_by_industry'. It also mentions the data source ('PRH/YTJ') and API nature ('Free government API'), making the purpose highly specific and well-defined.
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 listing search criteria (e.g., name, location) and mentioning it's for the Finnish registry, but it does not explicitly state when to use this tool versus alternatives like 'fi_company_lookup' or 'no_search_companies'. It provides some guidance through the search parameters but lacks explicit when/when-not instructions 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 but only states it 'lists' addresses without disclosing behavioral traits like whether this is a read-only operation, potential rate limits, authentication needs, error conditions, or what the output format looks like. It adds minimal behavioral context beyond the basic action.
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?
Two sentences, zero waste. First sentence states the core functionality, second provides usage context. Perfectly front-loaded and appropriately sized for this tool.
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 read operation with no annotations and no output schema, the description adequately covers the basic purpose but lacks important context about what the output looks like (address format, fields returned) and behavioral constraints. Given the 3 parameters with full schema coverage, it's minimally viable but has clear gaps in behavioral transparency.
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 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter semantics beyond what's in the schema (e.g., doesn't explain Norwegian postal code format beyond what the schema says, doesn't clarify street filtering behavior). Baseline 3 is appropriate when schema does all the work.
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 ('List') and resource ('addresses in a Norwegian postal code area'), and distinguishes it from siblings by specifying Norwegian postal codes (unlike the 'dk_postal_code_lookup' sibling for Denmark).
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 ('Useful for exploring what's in a given postal district') but doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools (e.g., no_address_search, no_municipality_addresses).
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 discloses the tool's behavior by mentioning the return type ('addresses with coordinates') and use cases ('autocomplete and address validation'), but lacks details on rate limits, authentication needs, error handling, or pagination. This is adequate but leaves 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 front-loaded with the core purpose, followed by output details and use cases in two concise sentences. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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 (4 parameters, no output schema, no annotations), the description is reasonably complete for a search tool. It covers purpose, output, and use cases, but lacks details on response format, error scenarios, or performance characteristics, which would be helpful for an agent to use it effectively.
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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the query parameter's flexibility ('free-text query') and the tool's general purpose, but does not provide additional semantic context for parameters like municipality or postal_code. This meets the baseline for high schema coverage.
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 verbs ('Search for Danish addresses') and resources ('addresses'), including the scope ('by free-text query') and output ('Returns matching addresses with coordinates'). It distinguishes itself from siblings like dk_postal_code_lookup or dk_reverse_geocode by focusing on free-text search rather than specific lookups.
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 the tool ('Great for autocomplete and address validation'), which helps differentiate it from siblings. However, it does not explicitly state when not to use it or name specific alternatives (e.g., dk_nearby_addresses for proximity-based searches), which prevents a perfect score.
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. It discloses the lookup/search behavior and return data structure, but doesn't mention rate limits, authentication needs, error conditions, or whether this is a read-only operation. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that efficiently communicates the tool's functionality, query methods, and return values with zero wasted words. It's front-loaded with the core purpose.
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 lookup tool with no annotations and no output schema, the description provides adequate basic information about what the tool does and what it returns. However, it lacks details about behavioral aspects like whether both parameters can be used simultaneously, how name matching works, or what happens when no results are found.
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 100%, so the schema already documents both parameters well. The description adds value by clarifying these are alternative ways to query (code OR name search) and providing context about what 'municipality' means in the Danish context ('kommune'), but doesn't add significant semantic details beyond the 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 verbs ('look up', 'search') and resources ('Danish municipality'), and distinguishes it from siblings by focusing on municipality data rather than addresses, weather, or companies. It explicitly mentions what information is returned (name, code, region, geographic bounds).
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 stating two ways to query (by code or name), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'dk_postal_code_lookup' or 'no_municipality_addresses'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key behavioral traits: the tool returns data (not modifies), specifies temporal scope ('today and tomorrow when available'), clarifies content ('raw spot price, not taxes/tariffs'), and mentions availability constraints ('when available'). It doesn't address rate limits, authentication needs, or error conditions, but provides substantial operational context.
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 in two sentences that front-load the core purpose and progressively add important qualifications. Every phrase adds value: the first sentence establishes what the tool does, the second clarifies temporal scope and content limitations. There's no wasted text or redundancy.
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?
For a read-only data retrieval tool with no output schema, the description provides good contextual completeness. It covers what data is returned (electricity spot prices), temporal scope (today/tomorrow hourly), content specifics (raw prices only), and availability conditions. It doesn't describe the return format structure, but given the tool's relative simplicity and the absence of an output schema, this is a minor gap.
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 100%, providing complete parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema (area and hours parameters are fully described there). According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without parameter details in the description.
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 and upcoming Danish electricity spot prices'), identifies the resource ('Elspot'), and distinguishes it from siblings by focusing on electricity prices rather than weather, address, or company data. It specifies the scope ('hourly prices for today and tomorrow') and clarifies what's included ('raw spot price, not taxes/tariffs').
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 mentioning 'current and upcoming' prices and 'today and tomorrow', but doesn't explicitly state when to use this tool versus alternatives like 'dk_cheapest_hours' or 'dk_energy_mix'. No explicit exclusions or prerequisites are provided, leaving usage guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context beyond what the schema provides. It discloses the forecast models used (DMI HARMONIE 2km for first 2.5 days, ECMWF for up to 16 days), which helps the agent understand data sources and temporal accuracy limitations.
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 concise with two sentences that each earn their place. The first sentence states the core functionality, and the second provides important technical context about forecast models without redundancy or unnecessary elaboration.
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?
For a 3-parameter tool with no annotations and no output schema, the description provides good context about geographical scope (Denmark) and forecast models. However, it doesn't describe the return format or structure, which would be helpful given the absence of an output schema.
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 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data alone.
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 verbs ('Get hourly or daily weather forecast') and resource ('for a location in Denmark'). It distinguishes itself from siblings like dk_current_weather (current vs forecast) and dk_compare_weather (comparison vs single forecast).
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 'Denmark' and mentioning the forecast models, but doesn't explicitly state when to use this tool versus alternatives like dk_current_weather or dk_compare_weather. No explicit exclusions or prerequisites are provided.
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 discloses the data source ('DMI HARMONIE high-resolution model (2km)') and acceptable input types ('city names, coordinates, or postal codes'), which adds useful context. However, it does not mention behavioral traits like rate limits, error handling, or response format, leaving gaps 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose, data source, and input requirements. Every sentence earns its place without redundancy or unnecessary details.
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 no annotations and no output schema, the description is moderately complete. It covers purpose, data source, and input semantics adequately, but lacks details on behavioral traits (e.g., rate limits, errors) and output format. For a tool with no structured support, it should do more to compensate.
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 100%, so the schema already documents the 'location' parameter. The description adds value by specifying acceptable input types ('city names, coordinates, or postal codes') and examples ('e.g. 'Copenhagen', 'Aarhus', 'Gilleleje''), which enhances understanding beyond the schema. With 0 parameters beyond the schema, this exceeds the baseline of 3.
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 conditions'), target resource ('for a location in Denmark'), and distinguishes it from siblings like 'dk_weather_forecast' and 'dk_compare_weather' by specifying 'current' conditions. It also mentions the data source ('DMI HARMONIE high-resolution model'), which further differentiates it.
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 ('Get current weather conditions for a location in Denmark'), but does not explicitly state when not to use it or name alternatives. It implies usage for current conditions versus forecast tools, but lacks explicit exclusions or comparisons.
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 effectively describes the tool's function and update cadence, but does not cover other behavioral aspects such as rate limits, authentication requirements, error conditions, or response format. The description does not 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the tool's purpose, scope, and update frequency without any redundant information. It is front-loaded with the core functionality and every element (components listed, update cadence) adds clear value.
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 (real-time data retrieval with one optional parameter), no annotations, and no output schema, the description provides a solid foundation by explaining what data is returned and how often it's updated. However, it lacks details on the response structure (e.g., units, format) and potential limitations (e.g., data sources, historical availability), which would enhance completeness for 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 input schema has 100% description coverage, with the single parameter 'area' well-documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, but with only one optional parameter and high schema coverage, the baseline is appropriately high. No compensation is needed for missing parameter details.
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') and resource ('real-time Danish electricity production mix'), listing the exact components (wind, solar, conventional, cross-border exchange) and update frequency. It distinguishes itself from siblings like dk_electricity_prices (which focuses on prices rather than production mix) and dk_co2_emissions (which focuses on emissions).
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 the data domain (Danish electricity production) and update frequency (every 5 minutes), which helps determine when to use this tool. However, it does not explicitly state when not to use it or name specific alternatives among siblings (e.g., dk_electricity_prices for price data instead of production mix).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a read-only operation (implied by 'Get'), provides real-time data updated every 5 minutes, and has a temporal scope (history up to 24 hours). It doesn't mention rate limits or authentication needs, but covers the essential operational context.
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?
Two sentences with zero waste: the first states purpose and key attributes (real-time, update frequency, unit), the second provides usage guidance. Every element earns its place, and it's 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?
For a read-only tool with 2 parameters, 100% schema coverage, and no output schema, the description is nearly complete. It covers purpose, behavior, and usage context well. The main gap is lack of output format details (what the return data looks like), but otherwise it provides sufficient context for effective use.
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 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (like explaining 'area' options beyond DK1/DK2 or 'hours' constraints). Baseline 3 is appropriate when the schema does the heavy lifting.
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 real-time CO2 emission intensity'), resource ('Danish electricity production'), and unit ('g CO2/kWh'), distinguishing it from sibling tools like dk_electricity_prices or dk_energy_mix by focusing on carbon intensity rather than prices or energy sources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool ('Useful for timing energy-intensive tasks to low-carbon periods'), providing clear context for its application. While it doesn't name specific alternatives, the purpose differentiates it from siblings like dk_cheapest_hours (which might focus on cost rather than emissions).
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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