Regulatory Intel MCP

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

No annotations are present, so the description carries the burden of behavior. It correctly implies a read-only lookup and discloses that results include agencies and slugs, but it does not specify what happens when no query is passed, whether matching is case-insensitive, or the output structure. These are moderate gaps for a simple lookup tool.

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 short sentences with no wasted words. The core purpose and the optional parameter behavior are both front-loaded, and the examples are directly useful.

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 tool with a single optional parameter and no output schema, the description covers the purpose, the parameter's role, and the kind of data returned (agencies and slugs). It falls slightly short only by not explicitly stating the default behavior when no query is provided, though that is easily inferred from 'optionally'.

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 schema already documents the query parameter at 100% coverage, so the baseline is 3. The description adds value by explicitly noting the parameter is optional, giving concrete examples ('environmental', 'defense'), and explaining that matching is by agency name.

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 uses a specific verb ('look up') with a concrete resource ('US federal agencies and their slugs') and explains why they matter ('used to filter the other tools by issuing agency'). This clearly distinguishes it from the sibling document-search tools and tells an agent exactly what the tool provides.

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?

It states the primary use case (getting slugs to filter other tools) and explains the optional query behavior. It does not explicitly name sibling tools or give exclusions, but the lookup nature of the tool makes the intended context 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?

With no annotations, the description carries the burden. It clearly indicates this is a read operation (no side effects), but it doesn't disclose potential behavior such as error handling for invalid/unknown document numbers, rate limits, or whether the response is paginated. The description is safe but thin on behavioral caveats.

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 compact and front-loads the main action. The list of returned fields is useful but slightly long; while it adds clarity, it could be trimmed to key categories. Overall, no waste.

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 single-parameter read tool with 100% schema coverage, the description is nearly complete. It could improve by briefly describing the output structure (e.g., JSON object) and handling of not-found cases, but nothing essential is missing for basic invocation.

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 schema covers the single parameter fully (name, type, required, and a clear example). The description reinforces the parameter by showing a sample document number and telling where to get one. Since schema coverage is 100%, the added context is helpful but not strictly necessary.

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 retrieves full details for a single Federal Register document by document number. It enumerates the returned fields (abstract, action, effective date, etc.), which sharpens the purpose. It also distinguishes itself from siblings by emphasizing a single document lookup by number, not a search or list.

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 fetching details on a specific document and even hints at the alternative when you lack a number: get one from search_documents or recent_documents. However, it does not explicitly state when to avoid this tool or compare it directly with 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 behavioral burden. It discloses that the tool returns EO number, dates, title, and links, and implies a read-only search. However, it does not clarify what 'recent' means, whether results are paginated, or any limits beyond the schema's max per_page.

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 sentences with no filler: the first states the primary action and resource, the second summarizes filters and output. Every clause earns its place, and the most important information is front-loaded.

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?

The description covers the tool's core purpose, filters, and return fields, which is helpful given no output schema. However, it leaves gaps: no definition of 'recent' date range, no default behavior when filters are omitted, and no guidance on choosing this over the sibling 'search_documents' for broader Federal Register searches.

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 coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond echoing 'keyword' and 'date' filters already covered by the schema; it does not explain formats, defaults, or edge cases beyond what schema descriptions provide.

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 names a specific verb ('Search') and a clear resource ('US presidential Executive Orders published in the Federal Register'), and indicates the returned fields. It is distinct from the generic 'search_documents' sibling by targeting a specific document type, though it does not explicitly name sibling tools to differentiate them.

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 clear context: it is for searching recent executive orders, with optional keyword and date filters. It does not explicitly state when NOT to use it or point to alternatives, so it stops short of a 5, but the intended use case is not ambiguous.

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 burden of behavioral disclosure. It communicates that results are recent and time-bounded via a look-back window, and that filtering is optional. However, it does not describe the sort order, return shape, or any edge-case behavior such as what happens when no documents are found, which leaves some behavioral ambiguity.

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 two sentences with no wasted words. The first sentence states the action, resource, and optional filters, and the second sentence gives a concrete use case, making it easy to parse quickly.

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 listing tool with all-optional parameters and a complete parameter schema, the description covers the essential purpose and a primary use case. It does not fully describe the returned data structure, but no output schema exists and the tool is straightforward enough that the description is largely 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 100%, so the parameters days, type, agency, and per_page are already documented in the input schema. The description adds minimal extra meaning beyond highlighting the type and agency filters, so the baseline score of 3 is appropriate.

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

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

The description uses a specific verb ('List') with a clear resource ('most recent Federal Register documents') and mentions the optional filters by type and agency. It is easy to understand what the tool does, though it does not explicitly distinguish itself from sibling tools like search_documents.

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 provides a clear use case: 'Use this to monitor newly published rules/notices from an agency.' This gives the agent a strong contextual signal for when to choose this tool, but it does not explicitly state when not to use it or mention alternative tools such as search_documents.

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 is the sole behavioral source. It states the operation is a search and lists the exact fields returned (title, type, agencies, abstract, links), which clarifies both effects and output shape. It does not discuss ordering or search semantics, but for a public read-only search this is a minor 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?

Three sentences, zero filler: action and resource first, then filters/resulting fields, then an example. The structure makes it easy to scan and every sentence 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?

With no output schema, the description compensates by naming the returned item fields, and it gives enough filter information for a correct call. It doesn't cover pagination beyond the schema's per_page default/max, and there is no explicit note on ordering or empty results, but the tool remains callable without guessing.

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 coverage is 100%, so the schema already documents all six parameters. The description roughly restates filters and agency-slug usage, but adds no material meaning beyond the schema; 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 names the resource and action clearly: searching the US Federal Register. It then specifies filter dimensions and gives a canonical example query, making it immediately distinguishable from sibling tools like document or recent_documents.

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 'Great for...' example tells an agent exactly the kind of question this tool answers, and the pointer to the agencies tool for slug values is helpful routing. It does not explicitly contrast with document, recent_documents, or executive_orders, so it lacks explicit exclusion guidance.

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