Rootpublish AI facts check

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

No annotations are provided, so the description carries the full disclosure burden. It reveals a meaningful behavioral trait — that the output is questions to be answered independently rather than facts to be trusted — but omits whether this is a read-only operation, latency/cost, or any 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 tightly written sentences with no filler. The core purpose is front-loaded and the follow-up instruction adds actionable 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?

With no output schema present, the description must explain the return, and it does say the tool returns the questions a buyer asks. It is nearly complete for a 4-param tool, though it could note that depth controls how many questions come back.

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 documents site, depth, offer, and company. The description adds no parameter detail beyond restating the general theme of questions about a company, so baseline 3 applies.

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: returns the questions a buyer asks about a company, framed as an 'AI facts check'. It hints at its position relative to reading company material but does not name either sibling (company_statements, verify_findings), so the differentiation is only implicit.

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?

Gives a clear workflow instruction: answer the returned questions yourself using web search before reading anything from the company. This establishes ordering versus company_statements, but it never names the alternative tools or states when NOT to use this one.

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: 'reads' implies a non-destructive fetch and it discloses that the return includes comparison instructions, which is real behavioral value. However, it never mentions that it retrieves live external web content, what happens if pages are unreachable, or any auth/rate 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 that front-loads the workflow precondition before the action. No wasted clauses, though the phrase 'instructions for comparing your answers' adds ambiguity rather than information.

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?

No output schema and no annotations, with two of three parameters undocumented — the description should therefore explain both inputs and the shape of the return. It only gestures at the return ('statements with instructions for comparing your answers') and says nothing about the required 'site' or optional 'company', leaving the definition incomplete for an agent to call it correctly.

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 only 33% — only 'pages' is documented in the schema, while 'site' (required) and 'company' are bare. The description does not clarify the roles of any of the three parameters or why both 'site' and 'company' exist, so it fails to compensate for the coverage gap.

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 concrete verb and resource ('reads the company's pricing and service pages and returns its statements') and positions itself in a workflow relative to buyer_questions, so an agent can distinguish it from its siblings. The trailing phrase 'with instructions for comparing your answers' is somewhat opaque, which keeps it 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.

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

Gives an explicit sequencing precondition ('After you have answered the buyer questions'), which tells the agent when in the workflow to invoke it. It does not name verify_findings as the follow-on, nor state any when-not conditions, so it falls short of a 5.

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. It does disclose the filtering behavior (quotes must appear in both the answer and the company page) and a detection behavior (flag cited pages with wrong figures), which is genuinely useful. However it says nothing about what is returned, whether anything is mutated, or how failures/empty results are surfaced.

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 tight sentences with no filler, and the primary keep/filter behavior is stated before the secondary page-flagging behavior. Slightly dense phrasing ('differences whose quotes are really in...') costs a point but nothing is wasted.

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 4-parameter verification tool with no annotations and no output schema, the description is too thin: it never explains the shape of the filtered result, what happens to rejected differences, or how the returned page list should be consumed. The agent can guess the intent but not the contract.

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 75%, so the schema documents pages, answer, and judgement already; only 'site' is bare. The description hints at the role of answer ('quotes in your answer') and pages ('the company's page') but adds no format, syntax, or validation detail 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 concrete operations (keep only differences with verified quotes, find cited pages carrying a wrong figure) and a distinct resource, so the agent can separate it from company_statements and buyer_questions. It loses a point because the key noun 'differences' is undefined domain jargon that only becomes clear after reading the 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?

'Call once per answer' gives a call-frequency constraint but no when-to-use versus the siblings, no prerequisite ordering (e.g., that it should run after company_statements/buyer_questions), and no when-not guidance. The agent is left to infer the workflow position entirely.

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