Sovereign AI Act MCP

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

No annotations are provided, so the description carries full responsibility. It discloses that output is 'grounded verbatim in the law,' which is a useful behavioral trait, and it specifies the result categories. However, it does not mention limitations such as non-authoritative status, dependence on the quality of the description, or behavior for unclassifiable systems.

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

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

Two dense sentences with no wasted words. The core output is front-loaded (risk tier, Annex III, Articles), followed immediately by a practical usage trigger. Every clause earns its place.

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

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

Given there is no output schema or annotations, the description does a solid job of conveying what the tool returns and when to use it. It lacks detail on the response format or edge-case behavior, but for the purpose of selecting and invoking the tool it is nearly sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50%: the 'description' parameter is documented in the schema, and the tool description also references it as the input. The 'language' parameter is not described and has no default explanation beyond the schema default of 'en'. The description adds minimal semantic value beyond the schema, so a baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Classify'), a specific resource ('an AI system under the EU AI Act'), and the exact output dimensions (risk tier, Annex III category, binding Articles). It clearly differentiates from sibling tools like lookup_article and search_eu_ai_act, which focus on retrieval rather than classification.

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

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

The description gives an explicit trigger condition: 'Use whenever asked if an AI system is high-risk/prohibited or what obligations apply.' It does not explicitly list when not to use it or name alternatives, but the context is clear enough for an agent to select it appropriately.

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

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

No annotations are provided, so the description carries the behavioral burden. It discloses the scope of returned content (application dates and fine tiers) and indicates a read-only operation via the 'get_' naming convention, but it does not describe the return format, whether data is static or updated, or any access constraints.

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

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

A single sentence of nine words that front-loads the core purpose, adds a temporal qualifier ('post Digital-Omnibus'), and states the second content element ('fine tiers'). Every word earns its place with zero filler.

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

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

For a zero-parameter reference lookup with no output schema and no annotations, the description is nearly complete: it names the regulation, the temporal scope, and the content delivered. A minor gap is not describing the shape of the returned date/fine data, but this is acceptable for a simple canonical lookup tool whose siblings make its position in the toolset clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, the baseline is 4. The description correctly focuses on what the tool returns rather than parameters, and the 100% schema coverage means there is nothing left undocumented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific resource (EU AI Act application dates) and adds precise scoping ('post Digital-Omnibus') plus content type ('fine tiers'). It differentiates from siblings: search_eu_ai_act is a general search, lookup_article is article-level, while this tool is the canonical source for dates and penalties.

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

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

No explicit when-to-use or when-not-to-use guidance is given. However, the word 'Canonical' implies the agent should prefer this over search_eu_ai_act for authoritative deadline information, providing only implicit routing rather than explicit exclusions or alternatives.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose that the output is verbatim text and distinguishes original articles from inserted articles, which is useful. However, it does not mention language handling, response format, or any limitations such as availability of translated versions.

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

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

The description is a single, compact sentence that front-loads the key information about the tool's output. There is no wasted wording or redundant re-statement of the name. It is appropriately sized for a straightforward lookup tool.

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

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

For a simple 2-parameter lookup tool, the description covers the core resource and article-number constraints well. However, with no output schema and no annotations, it leaves gaps around language behavior and the return format, which an agent would need to invoke the tool with full confidence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for both parameters. It meaningfully explains the allowed values and scope of the `number` parameter, including inserted article identifiers. However, it says nothing about the `language` parameter, its supported values, or its role in retrieving translated text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the resource (EU AI Act Articles, including specific inserted articles) and the nature of the content (verbatim text). It does not use an explicit verb like 'retrieves' or 'returns', but 'Verbatim text of...' strongly implies lookup and retrieval. The scope is precise enough to distinguish it from the search sibling.

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

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

The description gives no explicit guidance on when to use this tool versus alternatives such as search_eu_ai_act. It only describes what is returned, leaving the agent to infer that this tool is appropriate when the article number is already known and verbatim text is needed. No exclusions or alternative conditions are stated.

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

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

With no annotations provided, the description carries the full burden and largely delivers: it discloses that the tool takes a feature description and returns a signal, binding Articles, obligations, red flags, and next steps, grounded verbatim in the regulation. It does not explicitly mention side effects, authentication, or rate limits, but as a review tool these are not material gaps.

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

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

The description is a single dense sentence with no filler, front-loading the purpose and then packing in the output contract and usage timing. It could be split for readability, but every clause contributes value.

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

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

Despite having no output schema or annotations, the description explains the outcome space, the regulatory grounding, and when to use the tool. The main gaps are the undocumented language parameter and lack of response-format details, but the core call contract is clear enough for an agent to proceed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input parameter is echoed with examples ('feature, spec or behaviour'), but the input schema already describes it. The language parameter is entirely undocumented in both the schema and the description, so the description only partially compensates for the 50% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'review a feature, spec or behaviour you are about to ship'. It enumerates the concrete verdict types (OK / TRANSPARENCY / REVIEW REQUIRED / STOP) and the outputs (binding Articles, obligations, red flags, next steps), which clearly distinguishes it from sibling lookup/search/classification tools.

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

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

Provides an explicit usage context: 'Use proactively while designing or coding an AI feature, before it ships.' It does not explicitly name siblings or say when not to use it, so it falls short of full routing guidance, but the timing and intent are clear.

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

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

No annotations are provided, so the description carries the full burden. It states the search scope but doesn't disclose behavior like whether results are ranked, whether it supports partial matches, language handling, or any limitations. For a search tool, this is a notable gap.

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

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

The description is a single concise sentence that front-loads the core action and scope. It earns its place with no wasted words, though it could add a brief usage note without becoming bloated.

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

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

With no annotations, no output schema, and 0% schema coverage, the description is too sparse. An agent doesn't know what the search returns, how language affects results, or how this differs from lookup_article. For a search tool with two parameters, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It mentions 'full-text search' and the corpus scope, which implies the query parameter is a search string, but it doesn't explain the language parameter or its default behavior. The description adds minimal value beyond the schema's bare parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs full-text search across the EU AI Act corpus and specifies the resource types (Articles, Recitals, Annexes). It distinguishes itself from siblings like lookup_article, which likely retrieves a specific article, though it doesn't explicitly name the alternative.

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

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

The description implies usage for searching the corpus, and the sibling list suggests alternatives like lookup_article for targeted retrieval. However, it doesn't explicitly state when to use this tool versus classify_ai_system or review_feature, leaving some inference to the agent.

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