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

With no annotations, the description carries the full burden and largely meets it: it discloses no auth/account/key requirement, local-only execution ('nothing is sent anywhere'), the return shape (summary line plus per-issue fail/warn/pass/info findings), and the scope boundary. It never explicitly states read-only/idempotent behavior, though 'check' and 'nothing is sent anywhere' strongly imply it.

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 paragraph that front-loads the purpose and the check taxonomy before moving to usage, guarantees, and exclusions. It is on the long side, but each clause adds routing or behavioral value, so there is little 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?

Despite having no output schema and no annotations, the description explains the return format, the execution model, the scope limits, and when to reach for it. An agent has everything needed to call it correctly and interpret the results.

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 parameter's schema already documents the string-or-array oneOf form with a per-line convention. The description body adds no further syntax, size limits, or encoding detail, so the schema does the heavy lifting — the baseline 3 for high 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 (check) and resource (Unified Namespace topic paths) and enumerates the exact class of problems it detects (hierarchy depth, casing, whitespace, case-collisions, duplicates, empty levels, wildcards). It explicitly distinguishes itself from the sibling lint_sparkplug_topic, so an agent can route without opening either 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?

Gives explicit triggering contexts (reviewing a proposed namespace, an ISA-95 hierarchy, a broker topic dump, a tag export) plus timing guidance (before anyone builds on top of it). It also names the exclusion and the alternative: it does not validate Sparkplug grammar, use lint_sparkplug_topic instead.

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 and does so: it discloses the local-only execution model (no API key, nothing sent anywhere), the exact grammar areas checked, the return shape (ordered fail/warn/pass/info findings with parsed parts, not a boolean), and three things it explicitly does not do.

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?

Front-loaded with purpose and cost model, then scope, then return shape, then exclusions; every sentence contributes. It runs slightly long and the message-type enumeration is dense, but there is no 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?

With no output schema and no annotations, the description fully compensates by describing the return format, severity vocabulary, and negative scope. An agent has everything needed to decide to call it and to interpret the result.

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?

One parameter with 100% schema description coverage; the schema already documents the string-or-array union and the newline-splitting behavior. The description only restates 'one or more MQTT topic strings', adding nothing the schema does not already say, so the 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 ('Validate ... MQTT topic strings against the Sparkplug B topic grammar') with version scope (Sparkplug 3.0). The scope is narrow enough that an agent can distinguish it from check_uns_namespace and run_readiness_diagnostic 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?

Gives an explicit trigger list (topic appears in a config, broker trace, code review, or user question) and explicit non-goals (does not connect to a broker, decode payloads, or check device existence). It never routes to the sibling tools, so the alternative-selection clause is missing, but the when/when-not guidance is unusually 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 present, so the description carries the full burden, and it does: free, no API key or account, DOES call the dxpert.ai API, requires connectivity, and is rate limited. It also warns the verdict is only as good as the intake and that the 'scope' field must be reported as a preliminary self-reported screening, not an audit.

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?

Front-loaded with verb, resource, and the input/output pairing, and every sentence carries non-redundant information. It is dense and the sentences run long, which slightly taxes readability, but there is no 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 single-parameter tool with no output schema, the description covers connectivity, rate limits, auth (none), what comes back, and how to frame the result to the user. Nothing an agent needs to call it correctly is missing.

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 nested intake object is fully documented, so the baseline is 3. The description earns above baseline by telling the agent to ask the user for missing values rather than guess them and by warning that the result quality depends on honest intake answers, adding actionable semantics the schema cannot express.

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?

Precise verb+resource (runs the dxpert.ai industrial AI-readiness diagnostic), with the input stated (a 16-answer self-reported intake) and the output characterized (ten axes 0-5, maturity stage, foundation gaps, confidence). It also explicitly contrasts itself with 'the two validators,' letting an agent separate it from its 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?

Gives concrete triggering conditions ('how ready a plant or site is for AI/analytics, what is blocking them, or where to start') plus a precondition (you can supply honest answers to the intake). It distinguishes itself from the validators by noting it calls the network while they do not, but does not name those alternatives explicitly, so it stops short of a full 5.

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