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

With no annotations, the description must carry the full behavioral burden, but it only adds the whitelist/domain concept. It does not disclose what flags are tunable in effect (leetspeak detection, unicode normalization, confidence thresholds), whether results are scored, or how whitelisted terms are treated, leaving the mutation-free analysis behavior largely opaque.

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?

It is a single, front-loaded sentence with no filler, which is good. However, the brevity is under-specification rather than disciplined concision, given the tool's eight parameters and complex behavior.

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 annotations, no output schema, eight parameters, and only 63% schema coverage, the description should explain return values, default behaviors, and the whitelist mechanism. It does none of these, leaving the agent unable to predict outcomes.

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 63% and the description only illuminates the 'domain' parameter via the whitelist mention. Key parameters (contextWindow, confidenceThreshold, caseSensitive, detectLeetspeak) get no explanatory value from the description, so it fails to compensate for the coverage gap on an 8-parameter tool.

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 verb ('Analyze') and resource ('text') but 'analyze' is generic and never says what analysis is produced (profanity matches? risk score?). The 'context awareness / domain-specific whitelists' phrase hints at filtering while allowing domain-specific exceptions, but it does not distinguish this tool from siblings like check_profanity or validate_content.

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?

There is no guidance on when to use this tool versus analyze_corpus, check_profanity, compare_strictness, or detect_obfuscation. The only implicit cue is 'domain-specific whitelists,' which is not enough to disambiguate among eight related analysis 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?

No annotations are provided, so the description carries the full burden and does very little: it doesn't say what 'profanity statistics' contains, whether results are per-text or aggregate, whether processing is costly, or what happens at the 500 boundary. The 500 limit is repeated from the schema rather than adding behavioral insight.

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?

A single compact sentence with the resource and the cap front-loaded; there is no filler. Its brevity is appropriate, though it comes at the cost of missing detail captured in other dimensions.

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 annotations, no output schema, and an undefined output concept ('profanity statistics'), the definition is not complete enough for an agent to predict the response shape or interpret the optional flag. A batch-analysis tool needs more than one sentence of explanation.

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 only 50%: the required 'texts' parameter is described in the schema, but 'includeWordFrequency' has no description anywhere. The tool description mentions 'statistics' but never explains that word-frequency data is opt-in or what toggling that flag does, leaving a real gap.

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 (Analyze) and resource (collection of texts) with the outcome (profanity statistics) and a hard limit (max 500). It doesn't distinguish itself from the close sibling batch_check, which also handles multiple texts, so an agent cannot reliably choose between them from the description alone.

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?

There is no when-to-use guidance at all – nothing says whether to prefer this over batch_check, stream_batch, or check_profanity. The only constraint mentioned (max 500) is a limit, not a selection rule.

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 behavioral burden. It discloses the batch limit of 100 texts but says nothing about return shape, error handling, per-text results, rate limits, or whether any texts are skipped or combined during checking.

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 front-loaded sentence with no wasted wording. It communicates purpose and an important limit immediately.

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 annotations, no output schema, and six parameters, the description is too thin for an agent to fully understand what a batch profanity check returns or how per-text results are represented. The max-100 rule is useful, but the broader behavioral contract is largely 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 high at 83%, so the schema already documents nearly all parameters, including the texts array and language options. The description adds no parameter-level meaning beyond the max-100 batch size, which the schema also captures.

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 a specific verb and resource: check multiple texts for profanity in one operation. It implies a batch-oriented counterpart to single-text checks, though it does not explicitly distinguish itself from siblings like check_profanity or stream_batch.

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 tool's usage is implied by 'multiple texts' and 'single operation,' and the max-100 limit gives useful context. However, it does not explicitly say when to use this versus check_profanity, stream_batch, or validate_content, nor does it state any exclusions or prerequisites.

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 does disclose the core behavior (matched words are replaced rather than merely flagged), which is the key thing an agent needs. It does not say what the response contains (censored string, match counts, modified vs. original), nor whether the input text is mutated in place, leaving meaningful gaps for a write-style 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?

A single front-loaded sentence with no filler. Slight redundancy in restating the replacement-character mechanic, which the replaceWith parameter already documents, keeps it from a 5.

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?

Seven parameters, no annotations, and no output schema: the description is one sentence and says nothing about the return value, how match counts or unchanged text are reported, or how the boolean toggles interact. For a tool with this much surface area and zero structured behavioral metadata, it is underspecified.

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% and every one of the 6 optional parameters carries its own default and explanation, so the schema does the heavy lifting. The description's mention of 'replacement character' maps to replaceWith but adds nothing beyond the schema text; 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 ('censor') plus resource ('text') and the mechanism ('replacing matched words with a replacement character'). It implicitly distinguishes itself from detection-only siblings like check_profanity, but never names them, so an agent must infer the distinction.

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?

There is no explicit when-to-use statement and no mention of alternatives, even though the sibling set contains close calls (check_profanity for detection, redact_pii for PII, detect_obfuscation for leetspeak). The agent must infer that this is the mutating counterpart to those detection tools from the verb 'censor' alone.

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. It usefully discloses that results include matched words, severity, and positions, which is real behavioral context. However, it omits whether the operation is purely read-only, any rate limits or auth requirements, and how large inputs are handled.

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 efficient sentence with the purpose front-loaded. It could be slightly richer for an eight-parameter tool, but there is no wasted wording.

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 and previews return fields, and the schema fully documents parameters. Given the absence of annotations, an output schema, and sibling differentiation, it is adequate but not complete for an agent choosing among many overlapping profanity/content tools.

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 eight parameters are already documented with defaults and meanings. The description adds no parameter-level detail beyond the schema, which is acceptable given full schema coverage but not additive.

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 and resource: 'Check text for profanity.' It also previews the return payload as including matched words, severity, and positions. It does not differentiate the tool from siblings like analyze_context, explain_match, or batch_check, which likely overlap in purpose.

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?

There is no guidance on when to use this tool versus censor_text, batch_check, explain_match, analyze_context, or stream_check. The description does not state when-not to use it or what prerequisites apply. Usage must be inferred from the name alone.

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 well: it discloses the decision model (ALLOW/HITL/BLOCK), risk-score range, matched categories, per-match positions, and operational limits (20 patterns, 200 chars, 50 ms budget). It does not state that the operation is read-only or describe overall latency, but the return contract and constraints are unusually explicit.

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 dense sentences, front-loaded with the core operation and followed by return values and constraints. No filler or repetition.

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 5-param, single-required-parameter tool with no output schema, the description supplies return shape and key limits, which is the essential missing context. It stops short of sibling routing guidance, but the definition is otherwise self-sufficient for correct 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 description coverage is 100%, so all five parameters (text, hitlAt, blockAt, strictness, customPatterns) are already documented in the schema. The description only restates the custom-pattern limits, adding no new semantics 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?

States a specific verb+resource: 'Scan text for prompt injection attacks using rule-based pattern matching.' This clearly separates it from profanity/secrets/PII scanners, but it does not explicitly name a sibling or distinguish itself from validate_content or detect_obfuscation.

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?

Usage is implied by the purpose (use when you need to detect prompt injection), but there is no explicit when-to-use, when-not-to-use, or alternative-tool guidance despite many related sibling scanners.

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 behavioral burden. It does not disclose whether the operation is read-only, what strictness levels are compared, what the response contains, or whether there are side effects such as storing the input text.

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 front-loaded sentence with no wasted words. It is appropriately sized for a simple one-parameter tool and immediately communicates the comparison focus.

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?

There is no output schema and no annotations, and the description does not explain what the comparison returns, which strictness levels are included, or how the results are structured. For a comparison tool, this leaves important context 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 100% and the single parameter 'text' is documented in the schema as 'The text to analyze.' The description adds no parameter-level meaning beyond that, which is acceptable at the baseline for fully covered schemas.

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 and resource: 'Compare detection results across different strictness levels.' This is clear enough to understand the core operation, but it does not distinguish the tool from siblings like check_profanity or analyze_context, nor does it clarify what kind of detection is being compared.

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 phrase 'across different strictness levels' implies the tool is used when strictness comparison is needed, but there is no explicit when-to-use guidance, no exclusions, and no named alternatives. Usage is only inferred from the description.

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 behavioral burden. It does not disclose the output format, whether the generated regex is returned as a string, how variant aggressiveness affects the pattern, or any limitations, permissions, or side effects.

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 efficient sentence with no wasted words and is front-loaded with the core action. It is appropriately concise, though it could include one more sentence of essential guidance without becoming bloated.

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 two parameters, no annotations, no output schema, and partial schema descriptions, the description is too sparse. It omits variant semantics, expected output, and practical usage context needed for reliable 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 description coverage is only 50%: 'word' is documented as 'The base word,' but 'includeVariants' has an enum with no explanation. The description adds no meaning beyond the schema and does not explain what basic, moderate, or aggressive variants actually produce.

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 and resource: 'Generate regex patterns,' scoped to 'custom profanity detection.' It is clear enough to distinguish from checking or censoring, but it does not explicitly differentiate itself from siblings such as check_profanity or detect_obfuscation.

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?

There is no explicit guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The word 'custom' implies a use case, but the agent must infer that this is for building reusable regexes rather than checking content directly.

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 disclosure burden, yet it says nothing about what the detection returns, whether matches include spans or confidence scores, thresholds, or size limits. Only the implied read-only nature of 'detect' gives the agent any behavioral signal.

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 front-loaded sentence with zero filler, naming the action and the technique categories up front. It is efficient, though its brevity borders on under-specification rather than tightness.

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 one-parameter analysis tool this is the minimum viable: the caller knows what to pass in. But with no annotations and no output schema, the description should say something about the shape of the detection result, which it omits.

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 is a single parameter with 100% schema description coverage, so the schema already documents 'text: the text to analyze' fully. The description adds no format, encoding, or length constraints beyond that, matching the baseline-3 case 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 ('detect') and a specific resource ('text obfuscation techniques'), and the examples leetspeak/homoglyphs carve out a distinct domain relative to siblings like check_profanity or scan_pii. It stops short of explicitly naming a sibling it differs from, so it lands at 4 rather than 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?

There is no guidance on when to reach for this tool versus alternatives such as validate_content, analyze_context, or check_prompt_injection, and no mention of prerequisites or exclusions. The agent must infer the call context entirely from the tool name.

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 behavioral burden. It only says the tool returns detailed reasoning and does not disclose read-only behavior, output shape, permissions, rate limits, or how context affects the explanation.

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 front-loaded sentence with no wasted words. It states the action and outcome efficiently.

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 no annotations, no output schema, and many sibling moderation tools, the description is too sparse. It does not explain what the detailed reasoning contains or how the optional word parameter affects results when omitted.

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 documents both parameters, including that 'word' is the specific word to explain. The description adds no parameter-level detail beyond that, 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?

States a specific verb and resource: explain why a flagged word was flagged, with reasoning. It is clear what the tool does, but it does not differentiate itself from siblings like check_profanity or analyze_context.

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 it is used after a word has been flagged, but gives no explicit when-to-use guidance, prerequisites, or alternatives. It does not say when to choose this over analyze_context or check_profanity.

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 implies a read operation but says nothing about ordering of results, pagination behavior, what a 'risk score' measures, or any permission/auth requirements for viewing risk data.

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?

A single front-loaded sentence with no filler; the key qualifier ('high risk scores') comes early. It's efficient, though its brevity leaves gaps rather than being wasteful.

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 two-parameter read tool with fully described params and no output schema, the description is minimally sufficient. It stops short of explaining return shape/ordering or what defines risk, which an agent would still need to interpret 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 description coverage is 100%, so both limit and minRiskScore are fully documented with defaults and ranges in the schema. The description only loosely echoes the threshold concept ('high risk scores') without adding syntax or behavioral detail beyond the schema.

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 ('Get list of users') plus the qualifying attribute ('high risk scores') and the intent ('for review'). It's unambiguous what the tool returns, though it doesn't distinguish itself from nearby profile-oriented siblings like get_user_profile.

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?

'for review' hints at the use case, but there is no explicit guidance on when to choose this over get_user_profile or when not to call it. The selection criteria (risk threshold) are implied rather than stated.

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 at all, the description carries the full burden and discloses very little. 'Real-time' hints that the data is live and ephemeral, but there is no mention of whether an active stream is required, whether results are cumulative or per-window, whether the call is read-only, or whether it is rate-limited.

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?

A single tight sentence with no filler, and the key qualifier ('real-time') is placed early. It is efficient but so short that it omits rather than organizes information.

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 zero-param tool with no output schema and no annotations, the description is the only source of information about what comes back, and it never names a single metric or return shape. An agent cannot tell whether it gets latency, throughput, error rates, or connection counts.

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 takes zero parameters, so the baseline is 4 and there is nothing the description could add about argument meaning. The only residual question is what scope (global vs. per-stream) the parameterless call defaults to, which is a minor gap.

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 ('Get') and resource ('real-time streaming statistics and performance metrics'), so the agent knows this is a metrics-retrieval tool rather than an analysis one. It does not, however, differentiate itself from siblings like stream_check or stream_batch, which also operate on the streaming path, so the boundary between 'stats' and 'check' is left to inference.

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?

There is no explicit guidance on when to call this versus stream_check, stream_batch, or analyze_corpus. The agent must guess whether stats are needed before, during, or after a stream, and whether any prerequisite stream must exist.

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 burden. It implies a pure read and tells the agent the fixed size of the result (24), which is useful signal that no pagination or filtering applies, but it says nothing about ordering, format of entries, or whether the list is static.

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?

A single short sentence that front-loads the action and the key fact (24 languages). Nothing extraneous or repeated.

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, parameterless lookup with no output schema, the description conveys what the agent gets back and how many items. Only minor gaps remain, such as the shape of each language entry, which the agent can discover on first call.

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 takes zero parameters, so the schema has nothing to explain. The description correctly implies no input is needed, which is the right baseline for a no-arg tool.

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 ('Get the list of all 24 supported languages') and even quantifies the result set. It is unambiguously distinct from every sibling, none of which deals with language enumeration, though it does not explicitly say so.

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?

No guidance on when to call this versus any alternative, no mention of how the result relates to other tools' language parameters. Usage is only implied by the name and description.

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 behavioral burden. It implies a read operation via 'Get' but does not state whether it is read-only, what permissions are required, what the risk assessment contains, or how includeHistory affects behavior.

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?

A single front-loaded sentence that is appropriately sized. It is efficient but underspecified, which is a completeness issue rather than a conciseness flaw.

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 annotations and no output schema, the description should explain return values, read-only behavior, and the effect of includeHistory. It omits all of these, leaving an agent with important gaps for a tool that retrieves moderation and risk data.

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 adds only the high-level concepts 'moderation profile' and 'risk assessment' without elaborating on userId or includeHistory beyond what the schema provides. 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?

States a specific verb ('Get') and resource ('a user's moderation profile and risk assessment'). It is clear enough to distinguish from listing tools like get_high_risk_users, but it does not explicitly name siblings or explain how it differs from them.

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 no guidance on when to use this tool versus alternatives such as get_high_risk_users, track_user_message, or reset_user_profile. There are no exclusions, prerequisites, or contextual triggers mentioned.

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 well: it discloses that original values never leave the server, the exact placeholder format produced, and the three things returned (sanitized text, vaultId, placeholder+type pairs). It omits vault lifetime/expiry, error behavior, and the 50,000-character input limit, which are meaningful gaps for a security-sensitive transform.

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, front-loaded with the operation and mechanism, then return shape, then the restoration pointer. No redundancy and every sentence adds new information.

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?

There is no output schema, so the description must describe returns — it does so precisely, naming all three returned components and the placeholder format. For a two-parameter transform tool this is complete enough to invoke 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?

Schema coverage is 100%, so both parameters are already documented, establishing a baseline of 3. The description goes further by tying vaultId to the round-trip workflow, making clear it must be reused in restore_pii — a semantic connection the schema alone does not spell out.

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 (redact) plus resource (PII in text) and immediately names the mechanism (server-side vault) and the outcome (reversible round-trip with [REDACTED_<TYPE>_N] placeholders). This clearly separates it from sibling scan_pii (detection only) and restore_pii (the reverse direction).

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 tells the agent to call restore_pii with the same vaultId to recover originals, which establishes the correct follow-up path. It does not explicitly contrast with scan_pii for detection-only needs or state when not to use it, so it stops short of full when/when-not coverage.

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 behavioral burden. It never says what state is destroyed by default, whether the operation is reversible, what permissions are required, or how the keepHistory flag changes the outcome, which are exactly the facts an agent needs before mutating a user record.

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?

A single front-loaded sentence with the verb and resource first and the usage cue trailing. There is no filler, though the parenthetical is terse enough to feel like an afterthought rather than structured guidance.

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 destructive mutation with zero annotation coverage and no output schema, the description is thin: it doesn't say what is reset, what happens to history by default, or what the caller gets back. Enough to guess the intent, not enough to call it safely.

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% and both parameters (userId, keepHistory) are documented in the schema itself, so the baseline is 3. The description adds no interpretation of keepHistory beyond what the schema already states.

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 (reset) and resource (a user's moderation profile), which is enough to distinguish it from read-oriented siblings like get_user_profile or get_high_risk_users. It stops short of naming what 'reset' actually clears (risk score, flags, history), leaving the exact effect slightly ambiguous.

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 parenthetical '(use after appeals or timeouts)' gives a genuine situational trigger, better than nothing. However, it offers no exclusions and names no alternative action for cases that don't warrant a full reset, so the agent must infer boundaries.

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. It usefully discloses that the vault is maintained server-side and originals never reach the AI client, which is important security context. However, it omits behavior such as vault expiry, required permissions, error handling, or whether restored values can be re-redacted.

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 tight sentences with zero filler. The primary action and prerequisite are front-loaded, and the security context follows naturally.

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 the core mechanism and a key privacy property, but for a PII-restoration tool with no annotations and no output schema, it should also clarify the return value (presumably restored text), failure modes such as expired vaults, and any authorization requirements.

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 documents vaultId, sanitized, and strategy in detail. The description adds no parameter-level meaning beyond reinforcing that vaultId comes from redact_pii; 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?

Uses a specific verb ('Restore') and resource ('PII placeholders in text'), and explicitly connects to the sibling tool redact_pii that creates the vault. An agent can distinguish this from redaction and other PII tools without opening the 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?

Clearly states the prerequisite: a vault session created by redact_pii. It does not enumerate when-not-to-use cases or alternatives, but the context for invocation is clear and actionable.

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 and does reasonably well: it discloses the return shape ('full ScanResult with per-match details') and the key behavioral caveat that in-line redaction produces placeholders that are not reversible. It omits auth, rate limits, and performance characteristics for large inputs, leaving some gaps.

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 short sentences: what it scans, what it returns, and where to go instead. The capability statement is front-loaded and 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?

For a two-parameter, non-mutating scan tool with no output schema, the description supplies return-shape information that would otherwise be missing and covers the reversibility trade-off. Only secondary operational details are absent, which are not essential to calling 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?

Schema coverage is 100%, and the schema's own description of 'redact' already explains the placeholder behavior and points to redact_pii. The description therefore adds no parameter meaning beyond the structured field, so 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 ('Scan text for PII') and enumerates the detected entity classes so the agent knows exactly what comes back. It also names the sibling it is not (redact_pii), making it distinguishable 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?

Explicitly routes the agent to redact_pii when a reversible vault-backed redaction is wanted, establishing the main selection criterion. It stops short of stating exclusions or the alternative paths among the other siblings (scan_secrets, detect_obfuscation), so it is clear but not exhaustive.

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 exist, so the description carries the full burden. It does add useful context about the return shape ('full ScanResult with per-match details including pattern id, family, and character positions'), but says nothing about the BLOCK decision driven by blockOnAny, the 50,000-character input cap, or cost/latency behavior.

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, zero filler, with the action front-loaded and the return format trailing. Every clause carries information.

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, the description correctly compensates by describing the ScanResult contents, which is the key missing piece. For a simple 3-parameter read-only scanner this is close to complete; only the BLOCK-decision semantics and input size limit are left unmentioned.

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% — all three parameters (text, blockOnAny, minEntropy) are documented in-schema with defaults. The description adds nothing about parameter effects (e.g. what entropy threshold actually filters out), so 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: 'Scan text for leaked credentials, API keys, tokens, and other secrets.' The scope (secrets/credentials) is distinct from the PII-flavored sibling scan_pii, but the description never names that sibling, so differentiation is implicit rather than 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?

Usage is only implied by the purpose statement — an agent can infer 'use this when text may contain secrets.' There is no explicit guidance on when to prefer scan_pii, check_prompt_injection, or detect_obfuscation instead, and no stated prerequisites, so the agent must work it out from sibling names.

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 bears the full disclosure burden. Beyond 'real-time' and 'high-throughput' it says nothing about ordering guarantees, partial-failure behavior within a batch, rate limits, or what happens if some messages fail - all material for a streaming batch endpoint.

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?

A single short sentence with no filler and the high-level mode stated up front. It is tight, but its brevity is partly under-specification rather than economy.

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?

Five parameters, a nested message array, no annotations, and no output schema - yet the description never says what the result looks like or what the messages are checked against (the languages enum implies matching against something undefined). For a batch/streaming mutation-shaped tool this leaves too much unresolved.

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%, including defaults for caseSensitive, detectLeetspeak, and normalizeUnicode and the min/max item bounds, so the schema already documents every parameter. The description adds no semantics beyond it, which puts this at the baseline 3.

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 verb is 'process', which is generic and never states what processing actually is - profanity checking, content validation, PII scanning, or something else. Given siblings named stream_check, batch_check, and check_profanity, an agent cannot tell what stream_batch produces that those do not; only 'multiple messages' and 'batch mode' hint at scope.

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?

'for high-throughput scenarios' is the only usage hint, and it describes a performance profile rather than a decision rule. It never names stream_check or batch_check as alternatives, so there is no guidance on when this tool should be chosen over them.

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. It reveals only that a queue is involved and results are 'instant', but says nothing about latency/rate limits, whether it is stateful (a track_user_message sibling hints at per-user state), what happens on queue backpressure, or what the result actually contains.

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?

A single compact sentence with no filler, and the real-time/queue framing is front-loaded. It is efficient but thin — nothing superfluous, but also almost nothing extra.

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 7-parameter tool with no annotations and no output schema, one sentence is not enough. The agent cannot tell what the returned 'instant result' looks like (score, boolean, match list), nor how this differs from check_profanity or validate_content, which is essential for correct selection.

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 7 parameters including the language enum and leetspeak/unicode flags are already documented in the schema. The description adds no parameter-level meaning beyond that, so the baseline of 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 states a verb ('add message to queue', 'get instant result') but never says what is being checked — profanity, content risk, PII, injection? Given the sibling set, an agent can guess it is a content check, but the name 'stream_check' and the description both leave the actual resource/violation type unstated. It also doesn't differentiate from check_profanity or validate_content.

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?

'Optimized for chat streams' implies a when-to-use context versus batch-oriented siblings like batch_check and stream_batch, but it is implied rather than stated. There are no explicit exclusions or conditions ('use X instead when Y'), so the routing signal is weak.

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 supplied, so the description bears the full burden, yet it says nothing about what the tool returns, whether it is read-only, how it behaves on already-clean input, or any cost/latency characteristics. Only the barest statement of intent is given.

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?

A single tight sentence with no filler and the purpose front-loaded. It is efficient, though the brevity contributes to the other gaps.

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 two parameters, no annotations, and no output schema, the description should explain the tone control and what a response looks like. As written it omits both, so an agent cannot predict the output shape or the effect of the unlabeled enum.

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 50%: the 'text' parameter is documented in the schema, but 'tone' (an enum of formal/casual/humorous/professional) has no description anywhere, and the tool description never mentions it. The description therefore does not compensate for the coverage gap.

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 pairs a specific verb ("Suggest") with a specific resource ("clean alternatives for profane content"), so an agent can tell what it produces. It does not, however, distinguish itself from close siblings like censor_text or check_profanity, leaving the boundary implicit.

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?

Usage is implied: you have profane text and want replacement suggestions rather than detection or blanket censorship. There is no explicit statement of when to prefer this over censor_text, nor any exclusion or prerequisite.

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 behavioral-disclosure burden. It does mention that the user's risk profile is updated, which signals a mutation, but it omits persistence details, permission requirements, reversibility, rate limits, and what the tool returns.

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 efficient sentence that front-loads the action and side effect, then adds the purpose. There is no wasted text or redundancy.

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 annotations and no output schema, the description should do more to explain the persistent state change, expected return values, and operational constraints. It states that a risk profile is updated but leaves the agent without enough context to know what happens after the call or how the update is surfaced.

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 six parameters are already documented in the input schema. The description adds no parameter-level meaning beyond the schema, which is the baseline expectation when schema coverage is complete.

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 — tracking a user message and updating the user's risk profile — and adds the purpose of enabling pattern detection across messages. It is clearer than a tautology, but it does not explicitly distinguish itself from siblings such as check_profanity, analyze_context, or get_user_profile.

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 phrase 'enables pattern detection across messages' implies usage for ongoing user-level risk tracking rather than one-off content checks. However, it gives no explicit when-to-use/when-not guidance and names no alternatives among the many sibling 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return shape (safety score 0-100 and action recommendation), but does not state whether validation is read-only, how strictness or context affect behavior, what the action recommendation values mean, or any rate or permission constraints.

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 front-loaded sentence that communicates the core purpose and output without filler. It is appropriately concise, though it sacrifices necessary detail for brevity.

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-parameter validation tool with no annotations and no output schema, the description is incomplete. It gives a partial view of return values but omits parameter semantics, usage guidance, and behavioral details needed to invoke it correctly against similar sibling tools.

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 only 33%, with only 'text' described. The description does not explain the 'context' or 'strictness' parameters, and while strictness has an enum, its behavioral impact is undocumented. The description adds almost no parameter meaning beyond the schema.

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 verb and resource: 'Validate content', and adds that it returns a safety score and action recommendation. However, it does not distinguish this tool from nearby siblings such as check_profanity, check_prompt_injection, scan_pii, or analyze_context.

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?

There is no guidance on when to use this tool versus alternatives like check_profanity or scan_pii, nor any stated preconditions or exclusions. The agent must infer usage entirely from the tool name and sibling list.

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