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

With no annotations, the description carries the full burden and does so: it declares read-only semantics, that files are hashed and never imported or executed, that contents are not returned, and it distinguishes hard failures from warnings. That is exactly the safety and outcome profile an agent needs before invoking an artifact-inspection tool on shipping output.

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 dense sentences, front-loaded with what the tool checks, then how to enable the full check, then the safety profile. Every clause carries information, though the middle sentence is heavily comma-loaded and could be marginally tighter.

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?

An output schema exists, so return values need not be described, and the description covers purpose, trigger timing, optional inputs and safety. The remaining gap is behavior when the optional report/manifest is absent or malformed, which matters for a gating pre-ship check.

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 0%, so the description must compensate, and it largely does: 'path' is clarified as an output directory or a .whl, and build_report / provenance_manifest are tied to the build's --build-report and --provenance-manifest JSON artifacts. It stops short of specifying whether these are file paths or inline JSON, and any format expectations.

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?

Specific verb (check) plus resource (output directory or .whl) with an explicit temporal scope ('right before shipping') and concrete failure/warning conditions (pyobfus mapping file, private key, original source, unreported Python files, skipped Pro options). This distinguishes it from the adjacent pre-build sibling check_obfuscation_risks, which audits risks rather than validating a finished artifact.

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?

States when to use it ('right before shipping') and how to get the most out of it ('Pass the build's --build-report and --provenance-manifest JSON for the full check'), implying the no-report path is a degraded check. It does not name an alternative tool or an explicit when-not-to-use, 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 provided, the description fully carries the burden of behavioral disclosure. It explicitly states that no outbound network calls occur unless verify_dependencies_online=true, it explains the default config-honoring behavior and the legacy fallback, and it describes the returned data. This is unusually transparent for a scan 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 long but information-dense, and the core purpose is front-loaded. The caveats about network calls and config behavior earn their place. It could be broken into shorter sentences for easier parsing, but it does not waste words.

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 tool has no annotations, but the description covers the main purpose, parameter semantics, side-effect profile, and output summary (severity counts, frameworks, suggested preset). An output schema is present for exact return structure, so the description need not repeat that detail. This is fully adequate for an agent to decide whether to invoke the tool.

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%, and the description compensates thoroughly. It explains that path points to the Python project, use_project_config controls whether the project's pyobfus config is honored versus the legacy config-free scan, and verify_dependencies_online gates the PyPI check and clarifies the offline default. Every parameter gains meaning beyond its name.

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 ('Scan') and a clear resource ('a Python project'), then enumerates exactly what patterns are checked (eval/exec, dynamic attribute access, framework reflection, unsafe model loading). It also names distinguishable outputs (severity counts, detected frameworks, suggested preset), which differentiates it sharply from sibling tools like protect_project or generate_pyobfus_config.

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 gives a clear sense of when this tool is relevant: before obfuscation, to identify risk patterns and decide a preset. It also explains behavioral brancks like use_project_config and verify_dependencies_online. However, it does not explicitly contrast this tool with siblings or state when to prefer an alternative, 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 burden of behavioral disclosure. It adds useful context by specifying that Pro presets return 'full pro_unlock metadata (trial command, checkout URL, price, money-back guarantee)', which goes beyond the bare tool name. It does not explicitly state read-only behavior, but the term 'explain' and the nature of the described outputs make non-mutation apparent.

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 two sentences with no filler. The first sentence front-loads the primary purpose and key covered aspects, while the second adds a meaningful conditional detail about Pro presets. Every word 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?

The description covers the core purpose, key features, and special behavior for Pro presets, which is sufficient given a single simple parameter and the presence of an output schema. It could mention a usage workflow (e.g., 'use after list_presets') but is not incomplete without it.

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. It implicitly identifies the sole 'name' parameter as a preset name through the phrase 'named preset' and adds meaning by distinguishing Pro presets. However, it does not provide explicit parameter documentation, such as example names or format, leaving some room for interpretation.

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 clearly states the verb 'Describe' and the resource 'a named preset', and lists specific aspects ('exclude names count, exclude patterns, preserve_param_names, docstring handling') that differentiate it from siblings like list_presets. It is immediately clear what this tool accomplishes.

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 provides enough context to infer the tool is used for explaining preset details, and the presence of list_presets implies an alternative for listing. However, there is no explicit 'when to use' or 'when not to use' guidance, nor a direct reference to sibling tools, leaving it to the agent to infer.

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?

It discloses that it auto-detects frameworks and the default vs. write behavior, which is important for a generation tool, though it doesn't specify error handling or file overwrite policy.

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 two concise sentences, front-loading the purpose and then providing key behavioral details without unnecessary words.

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?

While the description covers the main action and default behavior, it omits details on the 'preset_override' parameter and error scenarios, making it adequate but not thorough for a tool with auto-detection logic.

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 description only explains the 'write' parameter; 'path' and 'preset_override' are not described despite 0% schema coverage, leaving the agent unclear on expected values and semantics.

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 clearly states the tool generates a pyobfus.yaml for a Python project, which is specific and distinguishes it from siblings like protect_project or recommend_tier.

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 explains the default behavior (returns YAML text) and when to use the write parameter, but does not explicitly state when not to use it or alternatives.

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 burden. It discloses the listing behavior and the grouping structure via 'grouped by tier'. The verb 'List' implies a read-only operation, and since an output schema exists, the return format need not be detailed further.

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 a single concise sentence that front-loads the verb 'List' and directly states the scope and grouping. No wasted words.

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 simple list tool with no params and an output schema, the description fully answers what the tool does. It covers the listing and the grouping, and there are no preconditions or side effects to mention.

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?

There are zero parameters, so schema coverage is trivially 100%. The description adds no parameter information because none is needed; the baseline for 0 params is 4, and there is nothing to compensate for.

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 clearly states the tool lists every pyobfus preset and groups them by tier (community / framework-aware / Pro). This is a specific verb+resource+scope, and it differentiates from siblings like explain_preset and recommend_tier.

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 the use case of discovering all available presets, which is clear enough from 'List every'. It does not explicitly name alternatives or exclusions, but the context of sibling tools (e.g., explain_preset) makes it obvious when this tool is the right choice.

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. It details the entire workflow: scans risks, picks a preset, obfuscates, byte-compiles, runs import-smoke-tests in isolated subprocesses, and returns verified:true/false. It also mentions writing a private de-obfuscation mapping alongside the output. It does not specify permissions or failure modes, which is a minor gap, but overall it provides substantial behavioral context.

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 sentences long with no filler. The first sentence is a dense, front-loaded summary of the entire workflow. The second adds a key behavioral detail (mapping placement). The third provides concise usage guidance. Every sentence 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's complexity (8 params, 1 required, output schema present) and 0% schema coverage, the description covers the core workflow and usage well. It explains the main steps and expected outcome. It does not detail the output schema or all parameter behaviors, but the output schema likely handles return value documentation. Slight gap in parameter descriptions, but overall complete enough for an AI to determine appropriate invocation.

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 0%, so the description must compensate. It provides high-level context for some parameters (e.g., 'path' as project path, 'preset' as framework-aware, 'verify' as the verification step, 'save_mapping' for the mapping file), but it does not explicitly detail all 8 parameters. For example, 'timeout', 'output_dir', 'verify_cmd', and 'trace_marker' are only briefly implied. This is minimally adequate but not thorough.

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 clearly states the tool's purpose: 'protect a Python project end-to-end AND verify it still works'. It distinguishes itself from sibling tools by emphasizing it is a combined protect+verify operation that 'returns verified:true/false', not just a transform. The context of sibling tools like check_obfuscation_risks and generate_pyobfus_config reinforces that this is a higher-level, end-to-end tool.

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 explicitly advises when to use: 'Use this when the user wants to protect/obfuscate before shipping and expects a green check, not just a transform.' While it doesn't name specific alternatives, the sibling list implies that other tools are for more granular steps (e.g., risk scanning or preset generation). The guidance is clear but could be more explicit about when not to use.

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?

Discloses it combines a preflight scan with a sensitive-string-literal heuristic and returns concrete actions. No annotations exist, so description carries the burden; it could mention if it modifies anything or prerequisites.

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 concise sentences, front-loaded with purpose, method, and output. Efficient but could be more structured.

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?

Covers purpose, method, and output types (free_action, pro_action), but lacks usage guidelines and parameter details. Adequate for a simple tool with one parameter.

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 sole parameter 'path' has no description in the schema (0% coverage) and the tool description only implies it's the project path without explicit semantics or format details.

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?

Clearly states it analyzes a Python project and recommends a tier (community vs Pro) with reasons, using a heuristic. However, it does not explicitly differentiate from sibling tools like check_obfuscation_risks.

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?

Implies usage when a tier recommendation is needed, but no explicit when-not or alternatives to sibling tools are provided.

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 fully conveys behavioral traits: it returns structured guidance, does not invoke side effects, detects active trials, and surfaces next steps plus purchase URL. This is transparent and leaves no ambiguity about what the tool does or 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?

Two sentences, no wasted words. The first sentence states purpose and critical side-effect disclaimer; the second adds behavioral detail. Highly efficient and front-loaded.

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 zero parameters, no annotations, and the presence of an output schema, the description is complete. It covers purpose, non-side-effect nature, detection capability, and what guidance is returned. No missing information for an agent to correctly select and invoke this tool.

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 and schema coverage is 100%, so baseline is 4. The description does not need to add parameter information; it implicitly covers the lack of parameters by describing the tool's function without mentioning inputs.

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 clearly states the tool's purpose: return structured guidance for starting the 5-day Pro trial. It specifies the resource ('pyobfus Pro trial') and action ('return structured guidance'), and distinguishes itself from sibling tools by noting it does not invoke the side effect.

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?

Provides explicit when-to-use guidance: it is for guidance only, not for actual trial activation, as the user must run `pyobfus-trial start` in their shell. It also mentions detecting active trial status, which helps an agent decide whether to call this tool.

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 must carry the full burden for behavioral transparency. It does not disclose whether the tool is read-only, requires any permissions, has side effects, or error handling behavior. The description only states the action and inputs, leaving gaps about what happens internally or under failure conditions.

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 concise, consisting of two sentences that deliver all necessary information without fluff. It is front-loaded with the action and efficiently explains inputs.

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 two simple parameters and an output schema, the description is fairly complete. It explains the purpose, inputs, and the origin of the mapping file. It could briefly mention what the output looks like (e.g., unmapped trace), but the output schema handles that. Overall, it provides sufficient context for a tool of this complexity.

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?

With 0% schema description coverage, the description effectively compensates by naming both parameters and providing context: 'trace' is described as 'plain text', and 'mapping_path' is described as 'the path to a mapping file produced by --save-mapping.' This adds meaningful usage guidance beyond the bare schema titles.

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 clearly states the tool's purpose: 'Reverse obfuscated identifiers in a stack trace using a pyobfus mapping.json.' It specifies the verb (reverse), resource (stack trace), and method (mapping file), which distinguishes it from sibling tools like check_obfuscation_risks and generate_pyobfus_config.

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 stating what it does and what inputs are needed (trace as plain text, mapping file path). However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or when-not-to-use guidance. Given the sibling tools, usage context is implied but not explicit.

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