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

Annotations already flag destructiveHint=true and idempotentHint=true, and the description enriches this: removal iterates 'до неподвижной точки', protected areas are reported as untouched, and the run deliberately does NOT claim full cleanliness (residual findings, code 1). This adds real behavioral context beyond the annotations. It does not detail auth or performance, so not a 5.

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?

Purpose is front-loaded, but the body is one very long semicolon-chained run-on that is hard to parse and repeats the check-before/check-after cycle. Some clauses earn their place (the exclusions, the residual-finding caveat), yet overall it is denser and longer than necessary.

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. The description covers scope (Russian connected text), language caveats, the fixed-point removal loop, protected-area reporting, and residual-finding semantics — enough for an agent to call it correctly. Only minor gaps (no explicit mention of sibling alternates by name) remain.

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 both parameters are already documented in the schema. The description reiterates the language profile behavior ('en/auto do not claim English stylistic detection'), reinforcing the enum semantics but adding little 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 states a specific verb+resource ('явная очистка поддерживаемых артефактов чат-вставки') and enumerates the concrete steps (detector check, removal of invisible marks and visible class-A MARKUP). It explicitly separates itself from stylistic rewriting, which distinguishes it from siblings like humanizer_polish. The dense single-sentence phrasing slightly blurs the core purpose, but it is identifiable.

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 an explicit 'Когда не использовать' section: not stylistic rewriting, not meaning verification, and it routes container files (PNG/DOCX/PDF) to scripts/filemarks. It cites conditions (unsupported findings class B, wiki markup, placeholders remain as residual code 1). No explicit routing to humanizer_polish/facts by name, but the exclusions are 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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description reveals that the tool only looks at connective-word frequency, that it is not validated for essays/fiction, and that it never returns an authorship verdict. These are meaningful behavioral constraints an agent needs before calling.

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 compact single sentence in Russian with a leading definition followed by exclusions. Every clause carries information, and the structure front-loads the purpose before restrictions, making it 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?

With a complete input schema, a present output schema, and annotations covering safety, the description fills the remaining gaps: applicability domain, invalid genres, and the no-authorship-verdict limit. Nothing needed to decide whether to call the tool 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%, so parameters are already documented. The description adds value by mapping the effective genre domain ('чистая проза, инструкции') to the genre parameter, helping an agent choose appropriate values, though it does not describe the text parameter beyond what the schema already says.

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 states the core signal ('частота связок') and its purpose ('признак машинного текста') within a restricted domain ('чистая проза, инструкции'), making the tool's function clear. However, it never names sibling tools, so an agent must infer differentiation from the domain restrictions rather than an explicit comparison.

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 an explicit positive domain ('чистая проза, инструкции') and a detailed 'Когда не использовать' list covering essays, fiction, and web text with artifacts, plus a hard boundary that no authorship verdict is ever produced. This is strong when/when-not guidance, even without naming alternative tools.

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?

Goes well beyond the readOnly/idempotent annotations: it explains that comparison is multiset-based, that preserved fact sets do not equal preserved relations, and gives a concrete counterexample (swapping 100/200 rubles between Ivan and Maria yields an empty diff). It also warns that a successful comparison does not prove meaning was preserved — exactly the kind of caveat annotations cannot carry.

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?

Purpose and the scope caveat are front-loaded, but the body is a single semicolon-chained run-on mixing fact classes, non-goals, CLI flags and semantic caveats, which makes it hard to scan. Nothing is truly wasted, yet the structure could be split for far better readability.

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 an output schema already defines the return shape and annotations cover the safety profile, the description fills the remaining gaps: scope limits, failure modes, the meaning-preservation caveat, and the exit-code implications of strict mode. An agent has everything needed to decide when and how to invoke 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 100%, so the baseline is 3, but the description adds real semantics: --no-additions turns additions into a violation (exit 1 / counts.added>0) while by default they are visible but non-blocking, and it clarifies that the language profile governs which stylistic heuristics are applied. That meaning is not derivable from the enum alone.

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+resource ('сверка фактов двух версий текста') and enumerates exactly which fact classes are compared (numbers with units, numerals, dates, URLs, e-mail, capitalized sequences, quotes, negations/modalities) plus the output shape (lost/added/changed with positions, identical). It implicitly separates itself from humanizer_detect/humanizer_report by disclaiming authorship, quality and style verdicts, though it never names those siblings directly.

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?

Explicitly provides a 'Когда не использовать' block: not for text outside the ru/en/auto profile, not for source code, no authorship/quality/style verdicts. It also documents the --no-additions strict-mode alternative and notes that additions do not affect the exit code by default, giving the agent clear routing and mode-selection criteria.

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?

Annotations already carry readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety bar is lower. The description adds valuable boundary context: the --remove (marker-stripping) mode exists but is console-only and 'по MCP не вызывается' (not invoked via MCP), and the MCP schema intentionally accepts only text and marker_class. This prevents an agent from assuming it can modify text through this tool. No contradiction with annotations; the description reinforces the read-only MCP surface.

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?

Every clause carries information with no filler, and the core purpose is front-loaded. However, the content is packed into a single dense paragraph where topics (purpose, --remove mode, risk classification, exclusions, MCP restriction) flow together via semicolons and em-dashes, making it harder to scan than it should be for an agent parsing the description.

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 moderately complex tool that already has an output schema and rich annotations, the description covers purpose, scope (Russian coherent text, out-of-scope handling in the schema), exclusions, alternatives, and the MCP/console boundary. Minor gaps remain — full semantics of class A vs B markers and how to interpret findings are only partially explained — but return values are covered by the output schema.

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 both parameters are well described. The description adds meaning beyond the schema by naming the two marker classes (A and B), which explains why the enum only offers 'a' (class A only) versus 'all' — implicitly there is no 'b'-only option. It also states that MCP accepts only these two parameters, clarifying the boundary between the tool and the console command.

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 states a specific action and resource: it 'находит и показывает' (finds and shows) '40 маркеров классов A и B' (40 markers of classes A and B) in 'артефакты копипасты и чат-интерфейсов' (copypaste and chat-interface artifacts). It also distinguishes itself from siblings by explicitly declaring 'не детектор генерации' (not a generation detector) and routing cleaning use-cases to humanizer_clean.

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 an explicit 'Когда не использовать' (when not to use) section with concrete exclusions: it is not a generation detector (absence of markers does not prove human authorship) and container files (PNG/DOCX/PDF) belong to scripts/filemarks. It also routes explicit artifact cleaning to the humanizer_clean sibling tool. This is actionable routing guidance, not just a purpose statement.

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?

Annotations declare destructiveHint=true and idempotentHint=true. The description adds crucial context that the default mode is destructive for Markdown and specifies boundaries (strip removes ##, **, ёлочки, тире, многоточие). Doesn't restate everything, so a 4. With annotations covering safety, this is strong.

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-loads the what, then when-not, but is dense and mixes English and Russian, with limited spacing. It contains useful info but is not optimally structured for an agent to parse quickly.

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 3-param tool with 100% schema coverage, an output schema, and annotations, the description covers purpose, boundaries, and non-goals. It could mention sibling tools more directly, but is largely complete.

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 parameters are fully documented. Description adds only marginal detail about language profile. Baseline 3 is appropriate as the schema does the heavy lifting.

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 operation on a specific resource: typographic normalization (dashes, quotes, ellipses, invisible chars, markup markers, LF). Concrete and nouny. It doesn't distinguish itself from siblings like humanizer_clean or humanizer_markers, so it falls 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?

Explicitly names when NOT to use it (Markdown/default mode) and names the alternatives (--preserve-markup, --typographic). Also states that it does not touch lexicon or meaning, and that en/auto do not do English style/authorship. Clear when/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?

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses important behavioral details: 'added' does not affect exit code, facts.unchanged ignores additions while facts.identical gives the full comparison, and en/auto profiles do not claim English stylistic or authorship detection. This adds substantial context an agent would not get from structured fields alone.

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 core purpose and front-loads the key limitations, but it is dense and mixes Russian and English in a single paragraph. Most sentences earn their place, though the run-on semicolon structure reduces scannability.

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 an existing output schema, the description is complete enough: it names the report's contents, clarifies scope restrictions, language-profile implications, exit-code behavior, and fact-comparison nuances. An agent has what it needs to invoke it and interpret the key outcomes without relying solely on the output schema.

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 parameter meanings are already documented in the input schema. The description reinforces the language profile behavior ('Language profile is explicit in --language; en/auto do not claim English stylistic or authorship detection'), but adds only marginal semantics beyond the schema's own descriptions.

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 that the tool produces a machine-readable edit report and enumerates its contents (keep/add/delete tokens, adapted SARI components, edit classes, fact categories, MTLD before/after). It is specific and distinguishable from a simple facts or detection tool, though it does not name any sibling alternative for direct comparison.

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 exclusions under 'Когда не использовать': not for unsupported language profiles or source code, and not for authorship/quality/style verdicts. It also clarifies language-profile behavior and exit-code semantics, but does not explicitly compare itself to sibling tools such as humanizer_facts or humanizer_detect.

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?

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context: it is a category counter, it calibrates editing volume, and it deliberately refrains from authorship judgments. This goes beyond the annotations without contradicting them.

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 with the core purpose, followed by an important exclusion. It has no fluff, but the two clauses run together without clear separation, slightly reducing structural clarity.

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 presence of a full input schema, an output schema, and strong annotations, the description supplies the key missing contextual information: what the tool measures, how it is meant to be used, and what it must not be used for. It is sufficient for correct invocation, though it could explicitly mention alternatives.

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 explains both parameters and the genre enum's effective values. The description does not add parameter-level detail, but the baseline of 3 applies because the schema carries the full semantic burden.

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 states a clear function: detecting 'soft signs' of machine writing and providing a category counter that calibrates the volume of editing. It also distinguishes itself from verdict-style tools by explicitly denying authorship verdicts, though it does not name sibling tools or use an explicit verb like 'scan'.

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 negative usage boundary: it does not provide an authorship verdict in any combination of features. This tells the agent when not to rely on it, but it does not name alternatives or explicitly state when to prefer sibling tools, so it falls short of full guidance.

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