Paperloo Compliance

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

With no annotations, the description carries the full burden, and it does disclose the inspection vectors (DOM/tracker checks, consent signals, headers), which is useful context beyond a bare name. However, it omits key behavioral facts: that auditing presumably loads the target page and fires third-party requests, whether it is non-destructive, how long a scan takes, and any auth or rate-limit constraints. Enough to know what happens, not enough to know the cost or side effects.

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, front-loaded with the core action and followed by the enumerated check list. No filler, no restating of the name or title.

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 two-parameter tool this is close to adequate, but with no annotations and no output schema, the description should say something about what comes back (a findings report? a pass/fail summary per check?) and about the deep_scan default. An agent knows what the tool does but not what it gets back or what calling it costs.

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 schema already explains both url and deep_scan; the baseline of 3 applies. The description adds no meaning beyond the schema — notably it never explains the trade-off of deep_scan (depth vs. speed/heaviness), which would have been a genuine value-add.

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 ('Performs multi-vector automated compliance auditing') on a specific resource ('target website URL') and enumerates the exact checks performed: cookie banner, Consent Mode v2, GTM tag firing, legal links, security headers. This is clearly distinguishable from siblings generate_documents and list_sites without opening any schema.

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 never states when to use this tool versus alternatives, nor any preconditions (e.g., whether the site must first be registered via list_sites). There is no guidance on when-not-to-use, expected runtime, or how deep_scan=true (the default) changes the decision to call it.

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 behavioral burden and mostly does not. It does not say whether the generated documents are returned inline, persisted to the site (despite the siteId parameter hinting at attachment), whether regeneration overwrites prior versions, or whether output is legally binding advice requiring review. Only the statutory provenance is disclosed.

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 dense sentence with the identifying noun phrase and provenance front-loaded and no filler. It is efficiently sized, though it spends its words on cataloging document types rather than on behavior an agent needs.

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 14-parameter generative tool with zero annotations and no output schema, the definition is thin on the things an agent cannot see structurally, namely what gets produced or returned and whether any persistence or overwrite occurs. The schema does cover every parameter fully, which keeps this from being inadequate.

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 all 14 parameters, their defaults, and their meaning are already documented in the schema, which sets the baseline at 3. The description adds only domain framing (which document suite is produced and what guidance it draws on) and no additional meaning about how jurisdictions, boilerplate defaults, or conditional flags change the generated output.

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 ("Generates") and a specific resource ("tailored legal document suites"), then enumerates the document types and the regulatory basis (EDPB, CPPA, ICO). An agent can tell this apart from audit_site and list_sites, though the description makes no explicit reference to those siblings.

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 by naming the inputs it expects a site and company to supply, but gives no explicit when-to-use criteria, no prerequisites, and no routing advice versus the sibling tools. An agent must infer from the parameter list that this is the authoring step in a generate/audit/list workflow.

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: it does disclose what data comes back (grades, banner status, GTM container state, document count), which is useful behavioral context for a read tool. However, it says nothing about scope (workspace/account), result volume, pagination, or ordering, and never states explicitly that the call is non-mutating.

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?

One dense, front-loaded sentence with no filler; every clause names something the caller will actually receive. Minor tension between 'all' and the existence of a filter parameter keeps it from a 5.

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 one-parameter read tool with no output schema, the description is largely sufficient because it enumerates the returned fields the agent would otherwise have to guess. Gaps around pagination, scope, and result size are minor but real.

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% and the single 'filter' parameter is fully documented in the schema, so the baseline is 3. The description adds nothing about the filter and in fact says 'Lists all', which slightly undersells the filtering capability rather than clarifying it.

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?

Uses a specific verb plus a well-scoped resource ('Lists all monitored websites') and enumerates the returned attributes (grades, banner status, GTM state, document count), so an agent knows exactly what this retrieves. It does not explicitly differentiate itself from siblings audit_site and generate_documents, which is the only thing keeping it from a 5.

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?

There is no explicit when-to-use guidance, no mention of when not to use it, and no reference to the sibling tools (audit_site for auditing, generate_documents for document work). The only signal is the implied 'browse/inventory' semantics of a list call.

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