swiss-electricity-mcp

Server Quality Checklist

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Latest release: v0.2.4
Disambiguation5/5
Each tool has a distinct purpose: dashboard tools cover different aspects of energy data, tariff tools handle various tariff queries, consumption search tools target specific catalogs, and health check is unique. No overlapping responsibilities.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case, using prefixes like dashboard_, tariff_, consumption_search_, and electricity_. No mixed conventions.
Tool Count5/5
12 tools is well-scoped for the Swiss electricity domain, covering production, consumption, storage, pricing, tariffs, dataset search, and system health. Each tool has a clear role.
Completeness4/5
The set covers key areas well (production mix, consumption forecast, storage, tariffs, dataset search). Minor gaps like historical consumption time series are mitigated by dataset search tools that can find additional data.
Average 4/5 across 12 of 12 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 62 commits in the last 12 weeks
- Last stable release on August 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. Description adds value by specifying the data source (Energiedashboard.ch) and the exact forecast types and comparison window, which clarifies the scope and nature of the output.
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, no wasted words. Key information is front-loaded: what the tool does, specific outputs, source. Highly efficient.
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 existence of an output schema (not shown), description does not need to explain return values. However, it lacks parameter semantics and usage guidelines, making it only minimally complete for a tool with two parameters and sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description does not mention the two parameters (limit_days, response_format) or their meaning. The agent cannot infer what limit_days controls (e.g., number of historical days? forecast horizon?) and how it relates to the 5-day-ahead forecast mentioned.
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?
Description clearly states verb 'get', resource 'current Swiss national electricity consumption forecast', and lists specific output components (today's value, 5-day-ahead forecast, trend signal, 5-year-window comparison series). It distinguishes from sibling tools like dashboard_get_production_mix by specifying the domain (consumption forecast).
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 explicit guidance on when to use this tool vs alternatives. The sibling tools are listed but the description does not mention them or provide usage context. An agent would need to infer from names alone.
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?
Annotations already declare readOnlyHint and openWorldHint. The description adds the constraint that canton names must be in German, which is useful, but does not disclose additional behavioral traits beyond what annotations provide. No contradictions with 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 very concise at three sentences, with the purpose stated first, followed by usage context and a parameter hint. No fluff or redundant information.
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?
With 6 parameters and only one partially explained, the description leaves out important details (e.g., what limit does, filtering options for category/period). The output schema exists but is not referenced. Overall, the description is too sparse for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 17% (only canton has a description). The description adds guidance for the canton parameter (German name examples) but offers no help for the other 5 parameters (limit, category, period_from, period_to, response_format). Given low coverage, the description should compensate more.
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 states the tool gets the cantonal median electricity tariff, with a specific verb and resource. It also explains its usefulness for positioning a municipality against its canton, distinguishing it from sibling tools like tariff_get_median_swiss or tariff_get_by_municipality.
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 (positioning a municipality) but does not explicitly state when to use this tool versus alternatives like tariff_get_median_swiss. No direct comparison or exclusion criteria are provided.
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?
Annotations already declare readOnlyHint and openWorldHint. Description adds that data is a monthly time series with a fixed index base, which provides context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, but missing parameter details. Conciseness is adequate but at the expense of completeness.
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?
Output schema exists, so return values don't need explanation. Description covers core data characteristics but omits parameter usage, which is needed for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description does not explain parameters (limit_months, response_format). The description adds no meaning beyond the schema; for a tool with two parameters, this is insufficient.
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?
Description clearly states the tool retrieves the Endverbraucher-Strompreis-Index with base 2020-01-01=100, a monthly time series. This verb+resource combination is distinct from siblings like dashboard_get_production_mix.
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?
No explicit guidance on when to use this tool versus alternatives. Usage is implied by the index name, but no context on exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description adds limited behavioral context beyond confirming it is a search. The description does not elaborate on pagination, result formatting, or potential limitations, but with annotations covering the core traits, a baseline score of 3 is appropriate.
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 sentence that efficiently states the tool's purpose and a key example. It is front-loaded with the verb 'Search' and contains no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and annotations, the description adequately defines the search scope. It mentions the specific dataset included, which adds context. However, it could briefly note that results are paginated via limit/offset, especially given openWorldHint, but overall it is reasonably complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters have no descriptions. The tool description does not explain individual parameters (query, limit, offset, response_format) beyond their implicit meaning from names and types. It adds minimal value, so a score of 2 reflects this gap.
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 searches the Stadt Zuerich OGD catalogue for energy datasets, specifically mentioning the quarter-hour consumption time series for grid levels NE5 and NE7. This specifies the resource and scope, and distinguishes from the sibling consumption_search_bfe_datasets which likely targets a different catalogue.
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: when searching for energy datasets from the Zurich OGD catalogue, particularly consumption time series. It implicitly contrasts with the sibling tool by specifying the catalogue, but does not give explicit exclusions or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=false, and the description adds that it is 'static data, no upstream call,' which aligns. It could be more transparent about the structure of the returned list, but the output schema covers that.
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 three sentences, each serving a purpose: stating what it lists, giving a usage example, and noting static nature. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the resource and usage guidance. Parameter semantics are lacking but the parameter is simple. The output schema exists, so return structure details are covered. Overall, it is adequate for a list-categories tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'response_format' has no description in the schema (0% coverage) and the description does not mention it at all. No additional meaning is provided beyond the enum definition.
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 'List' and the resource 'standard ElCom Verbrauchskategorien (H1-H8 households, C1-C7 commercial)'. It provides concrete examples and distinguishes itself from siblings by specifying it lists categories, not tariffs or other data.
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 gives explicit usage guidance: 'Use C3 for typical school buildings (~150'000 kWh/year)' and notes it's static data with no upstream call. It lacks explicit exclusion criteria or alternatives but context from sibling names helps.
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?
Annotations already indicate readOnlyHint=true and openWorldHint=true, so the description does not need to repeat safety information. It adds behavioral context by specifying the data is aggregated across all operators and filtered by year and category. This sufficiently discloses the tool's behavior beyond the 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 two sentences, front-loading the purpose in the first sentence and usage in the second. Every sentence adds value, with no extraneous information. It is highly concise and well-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?
The tool has 5 parameters, a read-only annotation, and an output schema. The description provides a high-level purpose but lacks details on how parameters affect results (e.g., limit controls result count) and the output format. Given the low complexity of the tool, the description is minimally complete but could be improved by explaining the remaining parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. The description mentions 'by year and category', which maps to period_from/period_to and category parameters, but it does not explain 'limit' (default 200) or 'response_format' (json/markdown). It also lacks detail on valid category values or year formatting. With 0% schema coverage, this is insufficient to fully convey parameter semantics.
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', the resource 'Swiss median electricity tariff', and the scope 'across all distribution operators' with dimensions 'by year and category'. It distinguishes from sibling tools like tariff_get_by_municipality and tariff_get_median_canton by specifying it's the Swiss-wide median, useful as a benchmark.
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 explicitly states the tool is 'Useful as benchmark for individual municipality tariffs', providing clear guidance on when to use it. While it does not list alternatives or when not to use, the context from sibling tools implies that more granular queries exist. A score of 4 reflects the clear usage context given.
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?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is clear. The description adds that it queries CKAN and filters by BFE organisation, providing useful behavioral context beyond the 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?
Two sentences, front-loaded with action, no redundant information. Every word adds 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?
For a search tool with output schema and adequate annotations, the description provides sufficient context about the data source and purpose. Could mention response format options, but schema already provides enum.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 20% (only query has a description: 'Free-text search'). The tool description does not elaborate on the other parameters (limit, offset, bfe_only, response_format) despite low coverage, leaving gaps for the agent.
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?
Clearly states the verb (search), the resource (opendata.swiss CKAN for energy/electricity datasets), and distinguishes it from siblings by noting it covers raw datasets not handled by other tools. The mention of 'BFE organisation' adds specificity.
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?
Explicitly says 'Use to find raw datasets not covered by other tools', which guides when to use and implies alternatives. Does not explicitly state when not to use, but the context is sufficient.
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?
Annotations already provide readOnlyHint=true and openWorldHint=true. Description adds beyond annotations by specifying the exact data returned (current fill, 5-year envelope, time series). No contradictory information. It would benefit from mentioning pagination or limits, but overall transparent.
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?
Description is two sentences, front-loaded with the main action and key details, with zero wasted words. Every part adds 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 that an output schema exists (context signals indicate so), description need not explain return values. It covers the tool's purpose and scope well. However, it could mention default behaviors or limitations (e.g., max limit_weeks) for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33% (only 'region' has a description: 'Region selector.'). Description does not explain the other parameters (limit_weeks, response_format) beyond their existence in the schema. Since coverage is low, the description should compensate but fails to do so.
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?
Description clearly states specific verb (Get) and resource (storage-lake fill levels) with scope (Switzerland or region) and data details (% and GWh, 5-year envelope, full time series). Sibling tools like dashboard_get_production_mix are distinct, so no confusion.
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?
Description implicitly signals usage context by calling it a critical indicator for winter supply security. While it does not explicitly state when not to use or list alternatives, the siblings are clearly different data domains, so agents can infer usage easily.
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?
Annotations already indicate readOnlyHint and openWorldHint. The description adds useful behavioral context: return value breakdown and filterability. However, it omits details on data freshness, possible empty results, or pagination behavior, which would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each adding value: purpose, output breakdown, and filter options with example. No extraneous information; front-loaded with the core action.
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 presence of an output schema and moderate complexity, the description covers the main functional aspects and provides a practical example. It lacks guidance on optional parameters like limit and response_format, but the example compensates for typical 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 only 33%, and the description compensates partially by explaining bfs_nr with an example and mentioning category and year range. However, it does not clarify limit, period_to, period_from, or response_format beyond what the schema provides.
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 it retrieves ElCom tariff observations for a specific Swiss municipality, detailing the output breakdown and filter options. This distinguishes it from sibling tools like tariff_get_median_swiss or tariff_compare_municipalities that operate at different levels.
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 for obtaining detailed tariff data for a single municipality, but it does not explicitly contrast with sibling tools nor specify when not to use it. The example and filter mentions provide context but lack direct guidance on alternatives.
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?
Annotations (readOnlyHint, openWorldHint) cover safety and data volatility; description adds source and data specifics, enhancing transparency beyond 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?
Two sentences, no fluff, front-loaded with purpose and data details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity, single optional parameter, and presence of output schema, description is complete and sufficient.
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 coverage is 100% and parameter description is already clear; description doesn't add extra meaning but is consistent.
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?
Description clearly states it retrieves Swiss electricity production mix by year with TWh and percentages, distinguishing it from sibling tools about consumption, storage, tariffs, etc.
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?
Purpose is obvious but no explicit when/not to use or alternatives. Still clear given sibling names.
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?
Annotations already mark it as readOnly and openWorld. The description adds 'per municipality, operator' row structure, but no additional behavioral traits beyond what annotations cover.
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 concise sentences with no redundancy. Front-loaded with action and key constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present, description covers all necessary context: inputs, output structure, and use cases. Complete for the tool's complexity.
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 coverage is low (25%), but description clarifies that bfs_numbers refers to municipalities, category is one category, period is a year. It adds meaningful context for the main parameters, though response_format is not explained.
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?
Clearly states the tool compares electricity tariffs across multiple municipalities by BFS numbers, category, and year. It uses specific verbs and resource, and distinguishes from sibling tools like tariff_get_by_municipality (single) and tariff_get_median_* (aggregated).
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?
Explicitly mentions use cases: procurement, benchmarking, school-network analysis. Does not explicitly state when not to use or list alternatives, but context from siblings makes it clear.
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?
Annotations already provide readOnlyHint and openWorldHint. The description adds value by specifying the return fields (HTTP status, latency, overall-healthy flag). No contradictory or hidden behaviors are implied.
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, front-loaded with verb, no unnecessary words. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and existence of output schema, the description is complete. It mentions all key output fields. No gaps are apparent.
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?
No parameters exist (0 params), and schema description coverage is 100% (vacuously). Baseline is 4, and the description does not need to add parameter info.
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?
Description clearly states the verb 'check liveness' and the specific resource 'all four upstream sources'. It unambiguously distinguishes from sibling tools which deal with production mix, consumption, tariffs, etc.
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?
While no explicit when-to-use or alternatives are given, the purpose is so distinct among siblings that usage guidance is implicitly clear. The description could mention that this is for health-checking upstream electricity sources, but it's not necessary.
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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