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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does this well by explaining that it walks all Ignition OPC tags, normalizes OPC item paths, maps them to L5X counterparts, and returns both matched and unmatched sets. It does not mention side effects or failure modes, but the core behavior is transparent.

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 front-loaded with the main purpose, followed by behavioral detail and the Args section. The Args block is justified because the schema lacks property descriptions, and the prose is efficient overall, though the first two sentences are slightly redundant.

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 file-analysis tool that already has an output schema, the description covers the purpose, matching strategy, inputs, and output categories. The main gaps are the exact JSON structure expected for mapping_file and an explicit note that the operation is read-only, but these are minor relative to the given context.

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 fully compensates by documenting each parameter in plain language. It explains ignition_path can be a directory or .zip export, l5x_path is a Studio 5000 .l5x file, and mapping_file is an optional JSON override. This adds real meaning beyond the bare schema property definitions.

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 opens with a specific verb and resource: 'Build a full correlation map between an Ignition project and an L5X PLC project.' It then details the algorithm and output, which clearly distinguishes it from the sibling find_unmapped_tags, since this tool returns both matched pairs and unmatched tags on both sides.

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 clearly conveys that this tool is for full correlation mapping and that it reports matched and unmatched tags on both sides, giving an agent a clear sense of when to choose it. It does not explicitly name alternatives or state when not to use it, so it stops 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 burden of behavioral disclosure. It goes beyond a vague 'finds tags' by stating the bidirectional comparison behavior and the exact categories of results returned. It does not explicitly confirm that this is read-only or describe error behavior, but the analysis-oriented wording is reasonably transparent.

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-loaded: the first sentence states the core purpose, the second defines the output, and the Args list adds parameter semantics without redundancy. Every line 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 the tool has an output schema, the description does not need to detail return formatting, but it still covers the operation, both result directions, and every parameter. It could be slightly stronger with explicit guidance about when to prefer a sibling tool or more detail on mapping_file behavior, but nothing essential 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 description coverage is 0%, so the description must fully compensate for missing parameter meaning. It does so by documenting all three parameters: ignition_path as a directory or .zip, l5x_path as a Studio 5000 .l5x file, and mapping_file as an optional JSON file with explicit tag mappings.

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 opens with a specific verb and resource—'Find tags that exist on one side but not the other'—and clearly identifies the tool as a commissioning gap analysis. It also states the concrete outputs by naming both result classes: Ignition OPC tags with no L5X match and L5X tags with no Ignition reference.

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?

'Commissioning gap analysis' provides clear context for when the tool is appropriate, and the return description clarifies what kind of mismatch it detects. It does not explicitly name sibling tools or state when not to use it, but the context is strong enough for an agent to select it in typical scenarios.

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 disclosure burden. 'Health check — verify the server is running' communicates that this is a non-mutating status probe. It doesn't detail response contents, but the output schema covers that.

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 exceptionally lean—five words of substance—and every word adds meaning. The 'Health check' label is front-loaded and the explanatory clause follows directly.

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 parameterless status probe with an output schema, this description is complete. It states the tool's purpose and implied read-only nature; nothing else is required 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?

The tool has zero parameters, so parameter documentation is not needed. The baseline of 4 applies because there is no semantic gap for the description to fill.

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, 'verify,' and a clear resource, 'the server is running.' It also establishes the tool as a health check, which sets it apart from sibling tools like correlate_projects and trace_tag.

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 clearly implies when to call it: any time the agent needs to confirm the server is up. It doesn't contrast against alternatives, but none of the sibling tools are plausible substitutes for a health check, so no exclusion is needed.

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 of behavioral disclosure. It clearly states what the tool returns (the signal chain and referenced PLC logic) and 'Trace' suggests a read/analysis operation, but it never explicitly states that the tool is read-only or describes side effects, failure modes, or environment expectations. This is adequate but not fully transparent.

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 front-loaded with purpose and return behavior, followed by a clean, structured Args block. There is no filler or irrelevant detail; every sentence earns its place, and the parameter list makes the interface immediately scannable.

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 four parameters and an output schema, the description covers the main operational needs: what the tool does, what it returns, and what each argument means. It omits some edge-case context such as expected file validity, behavior when the tag is not found, or the exact format of the mapping file, but the presence of an output schema reduces the burden for return-value details.

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, and it does: all four parameters are listed with meaningful explanations. It communicates path formats, the optionality of mapping_file, and gives a concrete example for tag_name, fully covering what the schema omits.

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 and resource: 'Trace a single tag end-to-end' and lays out the exact chain (Ignition config → OPC path → L5X tag → PLC logic). It clearly distinguishes this as a single-tag tracing tool, though it does not explicitly name or contrast sibling tools like find_unmapped_tags.

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 a use case—when you need to trace one tag's full signal chain—but provides no explicit guidance on when to choose this tool over alternatives. It gives no exclusions, prerequisites, or references to sibling tools, leaving the selection decision mostly to inference.

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