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

With no annotations, the description carries the full burden and discloses meaningful behavior: in-RAM processing, zero persistent retention, and idempotent scanning. It still does not clarify whether the input text is mutated in place or whether a redacted copy/report is returned, which is important for a redaction 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?

The description is three tight sentences, front-loading the core action and credential examples. Every sentence contributes: the first states what it does, the second states when to use it, the third states an exclusion and key behavioral traits.

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 scanner with no output schema, the description is largely complete: it explains purpose, timing, exclusions, patterns, and retention behavior. The main gap is the lack of return-value description, since there is no output schema to define whether the caller receives redacted text, a report, or both.

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 single 'text' parameter is already documented in the input schema. The description adds useful context about the credential patterns being detected, but it does not add syntax, formatting, or validation details beyond the schema baseline.

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 pair, 'Inspect and redact,' on a clear resource: exposed private credentials in prompt instructions. It lists concrete credential patterns (OpenAI sk-*, AWS AKIA*, GitHub ghp_*, etc.), so an agent can immediately distinguish this sanitization tool from its transpile/validate 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?

It gives explicit usage timing: 'before committing, sharing, or deploying AI prompts' and a clear exclusion: 'Do not use for cryptographic key generation or data encryption.' It does not name alternatives such as promptshift_validate_ast or promptshift_transpile, so it stops short of full when/when-not/alternative routing.

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 discloses important traits: operates 100% in-memory, uses deterministic AST mapping, and performs active secret sanitization. It does not describe output shape or error behavior, but the key operational model is stated.

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 three focused sentences. It front-loads the core action and supported platforms, then adds usage boundaries and behavioral context with no wasted text.

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 purpose, usage boundaries, and key behavioral traits for a 5-parameter tool with full schema coverage. The main remaining gap is that, with no output schema, it does not explain the return format or result structure.

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 five parameters are already documented in the input schema. The description does not add meaning beyond the schema for source_platform, target_platform, system_prompt, rules, or license_key, so the baseline 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 gives a specific verb (Transpile) and resource (agent instructions, master prompts, modular rules) and names supported source/target formats. It clearly distinguishes the tool from general translation or non-AI refactoring, but it does not explicitly differentiate from sibling tools promptshift_sanitize_secrets and promptshift_validate_ast.

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 when to use the tool (migrating or porting AI coding configurations between environments) and when not to use it (general natural language translation or non-AI code refactoring). It does not name alternative sibling tools or conditions for using them 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?

With no annotations, the description carries the full burden, and it does disclose that the operation is a pure analysis (explicitly not compilation or syntax execution) and that it returns a normalized fidelity score on a 0-100% scale. It does not say what happens on malformed input, whether the analysis is deterministic, or how drift is computed, so the behavioral picture is good but not complete.

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, each doing distinct work: what it measures, when to use/exclude, and what it returns. The core operation is front-loaded in the first clause and 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 two-parameter, no-output-schema tool with no annotations, the description covers purpose, trigger conditions, exclusions, and the shape of the return value — enough for an agent to call it correctly. It stops short of explaining the score's interpretation or failure modes, which would be needed for a full 5.

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 source_text and target_text are already fully documented as the original and converted documents. The description restates their relationship (original vs transpiled counterpart) but adds no formatting, size, or validation detail beyond the schema, 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?

The description gives a precise verb+resource pair — evaluate semantic AST fidelity and measure prompt drift between an original instruction document and its transpiled counterpart — so the agent knows exactly what operation is performed. It implicitly separates itself from promptshift_transpile (conversion) and promptshift_sanitize_secrets (redaction) by framing itself as post-conversion verification, but never names those siblings to make the routing explicit.

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?

"Use this tool to verify cross-IDE parity and detect omitted constraints after rule conversion" gives a clear positive trigger, and "Do not use for code compilation or syntax execution" gives an explicit exclusion. It lacks a named alternative (e.g. 'use promptshift_transpile to produce the target first'), which keeps it 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.