eea-climate-data-mcp

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation3/5
The SQL-related tools (validate_sql, explain_sql, query_sql, preview_rows, list_distinct_values) have overlapping purposes, though descriptions help distinguish them. get_emissions_series and query_sql both return data but serve different intents, and get_measure vs list_measures is clear.
Naming Consistency5/5
Tool names follow a consistent verb_noun pattern: get_, list_, search_, describe_, validate_, explain_, query_, preview_. There are no mixed conventions or inconsistent styles.
Tool Count3/5
18 tools is on the heavier side, largely due to 12 SQL-focused tools. The server's purpose is broad (emissions, measures, SQL exploration), but the count feels slightly over-optimized for coherence.
Completeness4/5
The tool set covers emissions data, sector metadata, PaMs catalogue, and SQL exploration. Missing update/delete operations are appropriate for a read-only data server. Core workflows are supported with no dead ends.
Average 3.5/5 across 18 of 18 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on July 19, 2026
- 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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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

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 that the row set is 'small' and 'bounded,' implying a limit, and that provenance is included. However, it does not explicitly state that the operation is read-only, safe, or how limits are enforced, leaving room for ambiguity.
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?
The description is a single sentence with no wasted words, which is positive. However, it is so brief that it under-specifies key aspects, so the structure is 'short' rather than 'effectively concise.' It earns a middle score.
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 an output schema, return values are partially covered elsewhere. But the description omits essential context: what 'provenance' entails, how max_rows interacts with the bound, what the database/version/table refer to, and when this tool is preferred. This is inadequate for a 4-parameter exploratory 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?
Schema description coverage is 0%, and the description adds no explanation of the parameters (database, version, table, max_rows). The agent is left to infer meaning solely from parameter names, which is insufficient and does not compensate for the missing 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 uses a specific verb ('Preview') and resource ('rows') and adds 'a small bounded set' and 'with provenance,' distinguishing it from broadly querying or fetching full tables. It does not explicitly mention sibling tools, but the phrasing conveys a scoped, safe-read intent.
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 usage guidance is provided. The description does not state when to use this tool versus alternatives like query_sql, list_distinct_values, or get_provenance, nor does it specify when a larger query would be more appropriate.
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, the description carries the full burden of behavioral disclosure. It only says 'List tables' and does not mention whether the operation is read-only, any permission requirements, or return behavior beyond what the output schema provides.
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, concise sentence with no fluff. It conveys the core purpose efficiently, though it is minimal and does not earn extra points for explanatory richness.
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?
Even though the tool is simple and an output schema exists, the description is too terse to fully prepare an agent. It leaves ambiguities about what 'database version' means, how to specify the database, and whether all or only some tables are returned. With 18 sibling tools, more contextual differentiation would be expected.
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 description coverage is 0%, and the description does not explain what 'database' or 'version' mean, how they relate, or what values are expected. It adds no value beyond the parameter names and the default in 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 action ('List'), the resource ('tables'), and the scope ('in a database version'). This distinguishes it from sibling tools like list_databases and list_versions, which list different entities.
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 about when to use this tool versus alternatives such as search_tables or describe_table. There is no mention of prerequisites, exclusions, or typical scenarios.
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, the description carries the full burden. It only says 'Explain' without disclosing any behavioral traits such as whether it performs a read-only operation, what output format to expect, or any side effects. This lacks 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 a single, front-loaded sentence with no wasted words. It efficiently conveys the tool's 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 the tool has no parameters and an output schema exists, the minimal description might be adequate. However, it lacks clarity on what the returned explanation looks like and when to use it, making it minimally viable but with gaps.
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 tool has zero parameters, and schema description coverage is 100% (vacuously). No parameter information is needed, so the baseline is 4. The description adds subject context about the statuses being explained.
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 explains 'reported actual, projection, policy estimate, and derived statuses,' which is a specific verb+resource. However, it does not explicitly differentiate from sibling tools, though its focus on reporting statuses is distinct from the data query tools.
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 usage guidance is provided. The description does not indicate when to use this tool versus siblings, nor does it mention any alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only lists content categories and does not state whether the operation is read-only, whether permissions are required, or what side effects might occur. The verb 'describe' weakly implies non-mutation, but this is not explicit.
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 front-loads the key components. Every word contributes directly to the tool's purpose, with no wasted filler.
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?
Though an output schema exists and the tool is a simple describe operation, the absence of parameter documentation and usage guidance leaves gaps. The description is minimally adequate for basic selection but lacks depth needed for full contextual 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?
Input schema has 0% description coverage, and the tool description does not explain the roles of database, version, or table parameters. The tool name implies 'table' is the focus, but no additional meaning is given beyond the parameter names themselves.
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 uses the specific verb 'Describe' and clearly enumerates the scope: schema, grain, keys, joins, reporting cycle, and caveats. This distinguishes it from sibling tools like list_tables or preview_rows, which serve different purposes.
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 conditions, exclusions, or compare with sibling tools such as list_tables or search_tables.
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 must carry the full burden. It does not disclose whether the tool performs a live network call, caches results, or has any side effects. The phrase 'documented and live-tested' hints at verification but does not elaborate on behavior or resource implications.
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, concise sentence that efficiently states the tool's purpose. There is no wasted wording, and the key information (what it returns) is front-loaded.
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 parameters and includes an output schema, the description adequately covers the basics. However, it lacks contextual guidance on how and why this tool fits into the broader workflow (e.g., before querying or validating SQL), which is important given the large sibling set.
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 tool has zero parameters, so the schema is trivially fully covered. The description adds no parameter-specific information (because none is needed), and the baseline for zero-parameter tools is 4, which is appropriate here.
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 action ('Return') and resource ('Discodata SQL capability matrix'), making its purpose comprehensible. However, it does not explicitly distinguish this tool from its siblings (e.g., validate_sql, query_sql), so it lacks the differentiation seen in top-tier descriptions.
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 gives no guidance on when to use this tool versus the sibling tools. It does not mention that this might be a prerequisite for understanding SQL feature support before running validation or queries, nor does it provide any context for selection.
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 burden. It does disclose a meaningful behavioral trait: validation against metadata before listing values. However, it does not explain error behavior, rate limits, or other side effects. This is a minimal but nonzero addition.
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, 10 words, front-loaded with the core action. Every word earns its place with no 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?
Despite having an output schema, the tool has 5 parameters with no param descriptions and no usage guidance. The description is too minimal to give an agent sufficient context for correct invocation, especially regarding what 'validating against metadata' entails and how max_rows behaves.
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 for parameter understanding. It adds no meaning for database, version, table, or max_rows; only the column concept is implicit. The 'bounded' hint is already in the schema via max_rows, so it adds little value.
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 action ('List bounded distinct values') and specifically targets a column, which distinguishes it from sibling tools like list_tables or preview_rows. The mention of validation adds specificity to the resource being operated on.
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?
There is no guidance on when to use this tool versus alternatives like query_sql or preview_rows. The description does not mention exclusions, prerequisites beyond validation, or context for selecting this tool over others.
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, and the description does not disclose whether the tool executes the query, is read-only, or has any side effects. The term 'explain' implies analysis, but the nature of the operation is not made explicit.
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, focused sentence that leads with the verb and lists the key aspects without wasted words.
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 description covers the tool's primary scope but omits parameter semantics and usage guidance. Since an output schema exists, return values are not required, but the behavioral constraints and parameter details are incomplete for a 2-parameter tool with no annotations.
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?
The description implicitly indicates that the 'sql' parameter is the query to explain but does not name it or describe its format. The 'max_rows' parameter is entirely absent, and with 0% schema coverage, this is a significant 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 uses the specific verb 'Explain' and clearly states the resource (referenced objects, joins, filters, aggregations, query risks). It distinguishes from siblings like query_sql (executes) and validate_sql (validates syntax).
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 a use case (understanding query internals and risks) but provides no explicit when-to-use or when-not-to-use guidance, and does not mention alternatives. The context is clear from the wording but not 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 full responsibility for behavioral disclosure. It mentions 'allowlisted' implying restricted access, but does not disclose any other behavior such as caching, pagination, authentication needs, or side effects. For a read-only listing tool, more transparency is expected.
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, concise sentence that is front-loaded with the action and key terms. Every word is informative, with no filler or redundancy.
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 is simple and an output schema exists, which helps document return values. However, the description omits explanation of the no_cache parameter and offers no usage guidance, leaving some gaps for an agent trying to invoke the tool correctly.
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?
The schema has one parameter (no_cache) with 0% description coverage. The description does not mention this parameter or explain its semantics. The name 'no_cache' provides some hint, but the tool description fails to compensate for the schema's lack of parameter documentation.
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 uses a specific verb ('List') and identifies the exact resource: 'allowlisted EEA climate databases and available versions.' This clearly distinguishes it from sibling tools like list_tables or list_versions, which have different scopes.
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 context about what is listed (allowlisted EEA climate databases) but gives no explicit guidance on when to use this tool versus alternatives, nor any exclusions. Usage is implied but not clearly stated.
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, the description discloses important behavioral traits: it clarifies that certain fields are substring matches, and it explains the meaning of missing quantified effect values. This goes beyond the basic search action and helps avoid misinterpretation.
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 compact and front-loaded with the core purpose, followed by targeted parameter clarifications. Every sentence adds value 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?
Despite providing useful caveats, the description is incomplete for a 9-parameter tool. Several parameters are not explained, and there is no mention of result ordering, pagination, or how max_rows is applied. The output schema may cover returns, but usage guidance is 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?
Schema coverage is 0%, so the description must compensate. It adds meaning for country and status with examples, and explains matching semantics for four fields. However, max_rows, start_year, and necp_reference remain undocumented, leaving gaps in 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 searches the Policies and Measures catalogue, with a specific scope ('descriptive, not a ranking source'). It distinguishes from siblings like get_measure by indicating a catalogue search/list operation, though it does not explicitly name alternatives.
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. It implies usage through search fields, but does not state exclusions or when to prefer sibling tools like get_measure or query_sql.
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?
No annotations are provided, so the description carries the behavioral disclosure burden. It offers useful context: 'locally validated' indicates a pre-execution check, 'bounded' signals that limits apply, and 'returning provenance' tells the agent what the result will include. However, it does not disclose error handling, mutability implications, or permission requirements.
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 entire description is a single, front-loaded sentence that packs core behavior, validation, bounding, and return content. No wasted words, and the structure makes the purpose immediately clear.
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 five parameters, no annotations, and no schema descriptions, this short description is not enough. It lacks usage guidance, exclusions, and parameter details. The existence of an output schema covers return values, but the operational context is incomplete for an execution tool.
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%, and the description does not compensate. The phrase 'bounded' hints at some parameter constraints but does not explain the sql, page, timeout, max_rows, or page_size semantics. The agent must rely on parameter names alone, which is insufficient for a fully correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Execute' with resource 'a locally validated and bounded SELECT', and includes the output scope 'returning rows and provenance'. It is specific but does not explicitly distinguish from sibling tools like preview_rows or validate_sql, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is for read-only SQL execution with validation and limits, but gives no explicit guidance on when to use it versus alternatives like validate_sql or explain_sql. No exclusions or alternative tool mentions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure burden. It only states 'allowlisted database' as a condition, but does not explain what happens for non-allowlisted databases, read-only nature, error behavior, or output format. This is a significant gap for a simple tool.
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, front-loaded sentence with no wasted words. It efficiently communicates the core purpose and the key constraint.
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 is simple with one parameter and an output schema, so return value explanation is not needed. However, the description omits behavioral details such as error handling for non-allowlisted databases and does not clarify what 'aliases' comprises, leaving some gaps for a complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by clarifying that 'database' refers to an allowlisted database, but does not specify format, identifier type, or valid values. This provides partial but not sufficient compensation.
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 uses a specific verb 'List' and resource 'version and revision aliases' for a database, making its purpose clear. It is distinct from sibling tools, which are about measures, SQL, and tables, so no ambiguity exists.
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 listing aliases on an allowlisted database but provides no explicit guidance on when to prefer this over alternatives or any exclusion criteria. The context is clear but lacks directional statements.
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 for behavioral disclosure. It only lists searchable fields and does not mention return format, case sensitivity, read-only nature, or any limitations. This minimal disclosure is inadequate for a complete 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 a single, focused sentence that succinctly states what the tool does without unnecessary words or repetition. It is appropriately sized for the tool's simplicity.
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 simple single-parameter input and presence of an output schema, the description meets minimum viability. However, without annotations, it could provide more context about what results are returned (e.g., matching table names or full metadata) and how the search behaves, leaving some 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?
The schema provides no description for the 'text' parameter (0% coverage). The description implies that 'text' is the search term applied across the listed fields, but does not specify matching semantics such as substring vs exact match. This adds some meaning beyond the schema but leaves ambiguity.
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 identifies the tool as a search operation over table metadata (names, descriptions, columns, and column descriptions). This specific verb+resource combination distinguishes it from sibling tools like list_tables (which lists all tables) and describe_table (which describes a specific table).
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 finding tables based on keyword search, but does not explicitly state when to use this tool versus alternatives. No exclusions or alternative guidance is provided, so the usage context is implied rather than stated.
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, the description carries the full burden. It discloses the returned fields and gives a meaningful example, but does not explain matching behavior such as case sensitivity, ordering, or how max_rows affects results.
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, with only three sentences, front-loaded with the core action, and includes a helpful example without unnecessary detail.
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 existence of an output schema and the simple read-only nature of the tool, the description covers the main purpose and return values well. It only lacks explicit mention of max_rows semantics and differentiation from describe_emission_sector.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It clearly explains that query is a code prefix or name substring, but max_rows is left implicit except for the schema default of 50.
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 finds IPCC inventory sector codes by code prefix or name substring, with an example ('transport' -> 1.A.3). It specifies the return fields, distinguishing it from sibling tools like describe_emission_sector.
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 intended use is clear: search for sectors by prefix or name substring. However, there is no explicit guidance on when to use this tool versus describe_emission_sector or other siblings, and no exclusions 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, the description carries the full burden of disclosure. It transparently reveals a key behavioral trait: parents already include children, so summing a sector with its parent or children causes double counting. It also lists the output components. It does not mention error behavior or permissions, but for a read-only description tool this is sufficient.
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 with no filler. The purpose is front-loaded, and the critical double-counting warning is presented as a separate, clear sentence. Every word earns its place.
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 tool is simple (one parameter) and has an output schema, so the description does not need to explain return values. It covers the essential behavior and the important aggregation caveat. It does not mention how to find sector codes (e.g., via search_emission_sectors), but that is a minor omission.
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?
The schema provides only a string type with no description for sector_code, and schema description coverage is 0%. The description adds that it is an 'IPCC sector code,' which gives some semantic context, but it does not provide format, examples, or how to obtain valid codes. This is minimal compensation for the complete lack of schema documentation.
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 function: it describes one IPCC sector code and lists the exact output fields (name, level, parent, direct children). The verb 'describe' is specific, and the resource is unambiguous, distinguishing it from sibling tools like search_emission_sectors or get_emissions_series.
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 its usage (for retrieving details of a single sector) but does not explicitly contrast it with alternatives or provide when-to-use/when-not-to-use guidance. The double-counting warning offers a usage caveat for aggregation, but it does not address tool selection.
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?
The description discloses the return structure in detail: status values ('reported'/'not_reported'), ex-ante values by scope, and ex-post average if reported. With no annotations provided, this gives useful transparency about quantification semantics.
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; the first clearly states the purpose, the second provides necessary detail. No fluff.
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 simple 2-parameter tool and presence of an output schema, the description covers the core functionality and return semantics, making it sufficiently complete for a fetch operation.
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 the description does not elaborate on the parameters beyond their names; no format or validation details are given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Fetch one policy or measure with explicit quantification semantics' – a specific verb+resource, and the phrase 'one' clearly differentiates this from sibling tools like list_measures.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving a single measure via country and measure_id but does not explicitly mention alternatives or provide when/when-not 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?
With no annotations provided, the description carries the full burden and discloses important behavioral traits: it is read-only, bounded, only allows one query, and only accepts allowlisted queries. This goes beyond a generic 'validate' description, though it does not specify return format or error 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, tightly worded sentence that front-loads the verb and packs in constraints with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the existence of an output schema, the description provides sufficient context about the tool's safety and scope. However, it does not mention return behavior or how the tool integrates with sibling tools like query_sql, leaving minor gaps.
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. It mentions 'bounded' which hints at max_rows, but does not explicitly explain the purpose or acceptable values of either parameter. The sql parameter is obvious from context, but max_rows remains unclear.
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 function: parsing and validating a SELECT query. It specifies key constraints (allowlisted, read-only, bounded) that distinguish it from execution tools like query_sql and analysis tools like explain_sql.
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 pre-validation of SELECT queries before execution, but it does not explicitly state when to use this tool versus alternatives. No exclusions are given, so the guidance is only implied.
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 disclosing behavior. It explicitly states that the query is not executed, which is a crucial safety trait for an AI agent to know. It also uses 'preview' to imply a read-only, non-mutating operation, though it doesn't elaborate on permissions or the nature of the classification.
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, concise and front-loaded with the action verb. It contains no filler and every word contributes to understanding the tool's core 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 has an output schema and only two parameters, the description covers the primary purpose and key behavioral trait. However, it lacks any mention of how this tool relates to siblings like explain_sql or validate_sql, and the 0% schema coverage leaves a gap in understanding parameter usage. It is minimally adequate but not rich enough for a novel context.
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%, and the description provides no parameter details beyond the tool's general purpose. While 'sql' is self-explanatory, 'max_rows' is not described, and the description fails to explain how parameters affect the preview, leaving the agent to guess or inspect schema properties without semantic guidance.
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 'previews provenance classification for a query without executing it,' providing a specific verb (preview), resource (provenance classification), and a key distinguishing scope (without executing). This differentiates it from sibling tools like query_sql and explain_sql.
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 phrase 'without executing it' implies the tool is for safe, non-executing analysis, establishing clear context for when to use it. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full exclusionary 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?
With no annotations, the description carries the burden. It discloses non-obvious behavior: avoids certain variants (TREND/BASE_YEAR_AVG/PREV_SUBMISSION), sorts client-side by year, and restricts accounting_scope to totals. These are valuable insights beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: a leading high-level summary followed by parameter-specific guidance and a behavioral note. Every sentence adds value, and the warning about not summing sectors is a critical, concise inclusion. No fluff.
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 tool has 6 parameters and an output schema, so return values are covered. The description explains the domain-critical specifics (curation rules, sector codes, accounting scope) and points to the sibling tool for sector discovery. It is sufficient for an agent to select and invoke correctly, though a bit more detail on gas values could complete it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning for the key parameters: country format (ISO-style Discodata codes), sector hierarchy and valid depths, and accounting_scope options. It omits gas/start_year/end_year, but those are self-explanatory from names and defaults.
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 identifies the tool as providing curated national GHG inventory time series. The 'statistically correct variable' phrasing is somewhat opaque, but the resource and action are clear, and it distinguishes itself from sibling data access tools by highlighting the curated nature.
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?
Provides concrete usage guidance: how to discover sector codes via search_emission_sectors, warnings against summing sectors with parents/children, and clarifying that accounting_scope applies to totals only. This gives the agent context on when and how to use the tool, though it doesn't explicitly contrast with alternatives beyond the sector discovery hint.
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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