Poly-Glot AI Workspace

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

Annotations only declare readOnlyHint=false and destructiveHint=false. The description adds meaningful behavior: it starts a 3-day free trial on first use and notes that Pro templates require an active trial or subscription. This goes beyond the annotations and warns about side effects, which is valuable for an agent deciding whether to invoke the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences and gets straight to the point, with the primary action front-loaded. It wastes no words, though the trial/subscription detail is somewhat tangential but still relevant. Overall it is efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 params, 1 required, nested objects, output schema present but not visible), the description covers the core action and a side effect but omits details like what the tool returns, what 'entitled' means, and any prerequisites (e.g., does the template already need to exist?). The presence of an output schema may cover return values, but the description itself is incomplete for a full picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden of explaining parameters. It mentions 'Fill' but does not map any of the five parameters (name, values, uiLanguage, inputLanguage, outputLanguage) to their purpose or format. The agent is left guessing what 'values' or the language parameters mean, and the description does not compensate for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: 'Fill and Send an entitled Poly-Glot template.' It identifies a specific verb ('Fill and Send'), a specific resource ('entitled Poly-Glot template'), and distinguishes it from sibling tools like get_template (which retrieves a template) and localize_text/translate_text (which transform content). The purpose is unambiguous.

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 indicates this is a 'Send action' and mentions trial/subscription requirements, but it does not explicitly say when to use this tool versus alternatives, nor does it name siblings or provide conditions for when not to use it. The usage context is implied (send a template) but no exclusions or alternative routing are given.

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, but it only states the detection/mapping behavior. It does not disclose the return format (language code vs. name), confidence, or behavior for unsupported or ambiguous 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 filler. Every word contributes to the core purpose.

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 no output schema and no annotations, the description should explain what the call returns and any constraints. It only says the result is mapped to a supported language, leaving the agent to guess the output shape and edge-case behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only refers to 'supplied text' without adding detail about encoding, length, or expected content. The schema's minLength helps slightly, but the description does not compensate for the missing parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb and object ('Detect the language of supplied text') and adds that the result is mapped to a supported Poly-Glot language. It does not explicitly distinguish this from sibling tools like get_language_options or translate_text, but the core function is unambiguous.

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 detect_language versus translate_text, get_language_options, or localize_text. The description implies a use case but does not state prerequisites, exclusions, or preferred 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?

Annotations already cover the read-only, non-destructive nature, so the description's added value is limited to specifying what capability categories are returned. It does not disclose any behavioral traits such as network dependency, latency, or failure modes beyond the phrase 'from the public remote MCP', though this is a lightweight read operation.

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 sentence that is dense but efficient, with the main action 'Return' front-loaded and the specific capability categories enumerated afterward. No wasted words or redundant restatement of the tool name.

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-parameter, read-only metadata query with annotations covering safety, the description is sufficiently complete. It names all the capability categories returned, and the absence of an output schema is acceptable given the clarity of the listed return content.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and 100% schema description coverage, so no parameter documentation is needed. The description still adds useful context about the content of the returned capabilities, which is the closest analog to parameter semantics here.

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 an exact verb ('Return') and a concrete resource ('supported BYOM adapter modes, credential policy, network restrictions, and notes about localhost access'). This is clearly distinguished from sibling tools like run_custom_model or validate_custom_model, which perform actions rather than retrieve capability metadata.

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 this is a pre-flight informational tool for checking custom model capabilities, but it never explicitly says when to call it or when not to use alternatives. There is no mention of preferring this over sibling tools or calling it before run_custom_model/validate_custom_model.

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 declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful safety context: language selection never changes entitlement and does not start the trial. This goes beyond the basic read-only annotation and reassures an agent about side effects, which is valuable for a tool that might be called before a trial decision.

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 consists of two concise, front-loaded sentences. The first states the core function, and the second adds a useful side-effect guarantee with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple and has annotations plus an output schema, which reduces the need for return-value detail. However, the purpose of the only parameter is left unexplained, which creates an incomplete picture for an agent deciding whether and how to pass uiLanguage. This is a clear gap in an otherwise adequate description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not explain the uiLanguage parameter at all. While the property name suggests it relates to UI language, it is unclear whether it filters the returned options, requests localized language names, or sets the response language. With only one parameter, the description should have compensated for the missing schema information but did not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Return') and resource ('supported Poly-Glot UI, input, and AI output languages'), making the tool's purpose unambiguous. It is clearly distinct from sibling tools like translate_text, detect_language, or localize_text, which perform different actions.

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 clear context on when this tool is appropriate: when an agent needs the list of supported UI, input, and AI output languages. It does not explicitly mention alternatives or exclusions, but the purpose is distinct enough among the siblings that no confusion is likely.

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 establish readOnlyHint=true and destructiveHint=false, but the description adds a valuable non-obvious behavioral detail: 'Does not start the trial.' This prevents an agent from assuming that checking status might trigger a side effect like activating a trial. That goes beyond what the annotations alone convey.

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, each earning its place. The first sentence front-loads the exact data returned; the second adds a critical disclaimer. No redundant wording or fluff.

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-parameter read-only tool with an output schema present, the description covers everything an agent needs: what values are returned, the authoritative nature of the data, and the lack of trial side effects. Nothing important 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?

The tool has zero parameters, so the schema covers all needed parameter meaning trivially. The description appropriately doesn't waste space discussing parameters. The baseline of 4 for no-parameter tools 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 names a specific verb ('Return') and an exact resource ('current Poly-Glot entitlement state, trial status, daily free send allowance, feature locks, and pricing'). It also frames the tool as the 'authoritative MCP-facing explanation', which clearly sets it apart from sibling tools, none of which concern subscriptions. The explicit caveat 'Does not start the trial' further sharpens its 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?

The description does not name alternatives, but the phrase 'authoritative MCP-facing explanation of the user's access' gives a clear context for when to call it: whenever entitlement or access state is needed. Since no sibling tool overlaps in domain, explicit exclusions aren't necessary. Clear context without exclusions warrants a 4.

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 readOnlyHint and destructiveHint annotations, the description adds meaningful behavioral detail: the operation does not start a trial or consume a send, and the prompt body is entitlement-gated. This is especially valuable because the title says 'Open', which could otherwise imply a side effect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loaded with the primary purpose, and each sentence adds distinct useful information. There is no filler or redundant restatement of the tool name.

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 behavior and side effects well, and the presence of an output schema reduces the need to describe return values. However, it lacks any sibling comparison or parameter-level detail, leaving an agent to guess when this tool is preferred over closely related template 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 0%, so the description carries the burden of explaining parameters, but it never mentions 'name' or 'uiLanguage' or their meaning. The required 'name' is somewhat inferable from the tool purpose, but 'uiLanguage' is left completely unexplained.

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 action and resource: 'Get a template's fields and prompt body.' It adds important qualifiers about not starting a trial, but it does not explicitly differentiate this tool from siblings like search_templates or build_prompt.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides useful context that opening a template has no side effects)Skipi.e., it does not start a trial or consume a send—but it gives no guidance on when to use this tool versus alternatives such as search_templates, open_workspace, or build_prompt. The when-to-use guidance is only implicit in 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 burden. It explains the semantic intent of localization but does not disclose what the tool returns, how it handles unsupported languages or invalid inputs, or any other behavioral characteristics. This is minimal for a tool with no output schema.

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 redundant wording. It conveys the core purpose and key parameters efficiently, earning every word it uses.

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 five parameters, no schema descriptions, no annotations, and no output schema, the one-sentence description is not enough for an agent to confidently invoke the tool. The agent is left without input format conventions, parameter semantics details, or knowledge of the return value.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds some meaning by naming the target language, locale, audience, and tone as localization dimensions, which is more than the bare schema provides. However, schema description coverage is 0%, and the description gives no format, allowed values, or examples for these string parameters, so it only partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource ('Localize text') and names the key dimensions: target language, locale, audience, and tone. It also distinguishes itself from literal translation, which helps differentiate it from the sibling translate_text, though it does not name that sibling explicitly.

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 'for a target language, locale, audience, and tone' provides clear context for when this tool should be used. The 'not merely literal translation' clause implies an alternative use case, but it does not explicitly instruct the agent to use translate_text for literal translation or state when not to use this tool.

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

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

Annotations already declare readOnlyHint=true, but the description adds value by disclosing that browsing does not start a trial or consume a send—important trial-related behavior not captured by the annotations. It also mentions subscription-aware locked states, giving the agent insight into conditional UI behavior beyond the simple read-only hint.

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 concise sentences with no filler. The primary purpose is front-loaded, and the second sentence earns its place by adding a meaningful behavioral guarantee. Every word contributes.

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 that all parameters are optional, an output schema exists, and annotations cover safety, the description is largely complete. The main gap is the undocumented parameter semantics, but the tool can be safely invoked with defaults based on the information provided.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description says nothing about how 'query' or 'uiLanguage' affect the workspace. The parameter names and defaults are somewhat self-explanatory, but the description does not compensate for the missing schema documentation, leaving the agent to guess whether 'query' is a search filter, initial editor text, or something else.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Open') and a specific resource ('interactive Poly-Glot template browser and prompt editor'), immediately distinguishing this from sibling tools like get_template, search_templates, and build_prompt. The 'subscription-aware locked states' adds precision about what the workspace contains.

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 clear context: this is the tool to use when the user wants to browse or edit in an interactive UI rather than perform a single discrete operation. The sentence 'Browsing does not start the trial or consume a send' gives a safety rationale for choosing this tool, but it does not explicitly name alternatives or states when not to use it.

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 only indicate it is a non-read, non-destructive operation. The description adds significant behavioral context: it discloses the side effect of starting a 3-day free trial on first use, and clarifies that it never calls third-party models on the user's behalf. This goes beyond the annotation information and helps the agent understand the tool's side effects and boundaries. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each serving a purpose: the first states the core function, the second discloses the trial side effect, and the third clarifies a limitation. It is appropriately concise and front-loaded with the main purpose. Slight redundancy with 'Prepare one canonical prompt' and 'for two or more AI providers' but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 parameters with zero schema description coverage, and the description does not explain any of them, the description is incomplete for correct invocation. It also does not describe the output or return behavior, though an output schema exists (which partially mitigates this). The subscription requirement is mentioned but not how to check it. Overall, the description leaves too much for the agent to infer, making it incomplete for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for documenting parameters. However, the description does not explain any of the 7 parameters (name, prompt, values, providers, uiLanguage, inputLanguage, outputLanguage). It only describes the overall action. With zero coverage and no parameter details, agents would have to infer parameter meaning entirely from the schema, which is inadequate. The description adds no semantic value for the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Prepare one canonical prompt for two or more AI providers so the user can compare answers.' It uses a specific verb (prepare), a concrete resource (prompt for multiple AI providers), and a clear goal (compare answers). It implicitly distinguishes from the sibling prepare_custom_compare by focusing on standard AI providers rather than custom models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context by noting it is a 'Send action' that starts a trial and requires a subscription, but it does not explicitly state when to use this tool versus alternatives like prepare_custom_compare or build_prompt. It mentions prerequisites but lacks explicit exclusion criteria or comparative guidance. The guidance is inferred rather than explicit.

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?

The description adds a meaningful behavioral guarantee: credentials are never embedded in the comparison plan. This goes beyond the sparse annotations. It does not, however, disclose other behaviors such as whether the plan is persisted, validated, or immediately executed.

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 efficient sentences fully front-load the purpose and add a relevant security constraint. There is no fluff or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema and minimal annotations, so the description should clarify expected return behavior and parameter semantics. It does neither in sufficient detail. The agent is left to infer what a 'plan' actually is and how custom model descriptors are interpreted.

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 schema has 0% description coverage, so the description must compensate. It only alludes to two parameter groups ('custom model descriptors' and 'built-in providers') but never explains prompt, uiLanguage, inputLanguage, outputLanguage, or the nested customModels fields. This is insufficient for a 6-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 specific action ('Build a Compare Mode plan') and identifies the exact ingredients: built-in Poly-Glot providers and developer-supplied custom model descriptors. The 'custom' qualifier clearly distinguishes this from the sibling prepare_compare tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: this is for building a compare plan involving custom model descriptors, as opposed to a standard compare. It also clarifies a timing aspect (credentials supplied only at execution time). However, it does not explicitly name alternatives or state when not to use this tool.

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

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

The annotations are all false and provide little safety context, so the description carries most of the burden. It adds useful behavioral detail: API keys are transient and never persisted, entitlement checks are respected, and language instructions are applied. However, it omits that this makes an external network call and says nothing about response behavior or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences with no filler. The core action is front-loaded, followed by security-relevant behavior and constraints. Every sentence contributes information without 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?

For a tool with 18 parameters, no output schema, and sparse annotations, the description is far too brief. It covers API key handling and entitlement checks, but it omits how to construct a request, what the response looks like, what adapterMode options mean, and how failures are surfaced. An agent would struggle to call this tool correctly without external knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no meaningful parameter-level guidance. With 18 parameters including adapterMode, baseUrl, endpoint, authMode, and responseTextPath, the description fails to compensate for the schema's lack of explanations.

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 action ('Run') and a specific resource ('a Poly-Glot prompt against a developer-supplied model endpoint'). This distinguishes it enough from sibling tools like get_custom_model_capabilities or validate_custom_model, though it does not explicitly name alternatives.

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 prefer this tool over siblings such as validate_custom_model or get_custom_model_capabilities. The description implies it is for executing prompts, but it does not state exclusions, prerequisites, or alternative 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?

Despite readOnlyHint and destructiveHint annotations, the description adds value by clarifying that searching does not start the trial or consume a send, and that results include lock status. This goes beyond the annotations and provides useful side-effect information without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with three short sentences that each add distinct information: purpose, result contents, and side-effect safety. It is front-loaded with the primary action and contains no filler or redundant phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the output schema covers return structure and annotations cover safety, the description leaves parameter semantics unaddressed and offers no guidance on selecting this tool over get_template. This is a meaningful gap for a search tool with 5 optional parameters, though the tool is simple enough to remain minimally viable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description provides no explanation of the parameters (goal, plan, limit, query, uiLanguage). The description fails to compensate for the lack of schema descriptions, leaving agents to guess parameter meanings from names 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?

Description states a specific action ('Find Poly-Glot prompt templates') and resource. It distinguishes from sibling get_template by indicating search behavior and including lock status in results. This provides enough differentiation for an agent to select this tool over alternatives.

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 tool name and description, but there is no explicit guidance on when to use this tool versus alternatives like get_template. The note about not starting a trial or consuming a send adds some usage context, but no exclusions or alternative comparisons are provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals the HTTPS-only constraint and the one-of requirement for audio inputs, but it does not mention what the tool returns, whether transcription is synchronous or asynchronous, file size limits, language detection behavior, or any other side effects. This is a substantial gap for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences with no filler. The most critical usage constraint is front-loaded, and every sentence earns its place. It is efficient and easy to parse, though the brevity comes at the cost of completeness.

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 7 undocumented parameters, no annotations, and no output schema, the description is not complete enough for confident invocation. It captures the core input requirement but omits the meaning of most optional parameters, expected return format, and any behavioral caveats. An agent would likely need external knowledge or trial and error.

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 input schema has 0% description coverage and 7 parameters, so the description must compensate. It adds meaning for audioUrl and audioBase64 by specifying 'exactly one' and 'HTTPS only', which is useful. But it says nothing about prompt, filename, mimeType, languageHint, or detectLanguage, leaving most parameters semantically undefined.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Transcribe' and the resource 'supplied audio', so an agent can immediately tell what the tool does. It does not explicitly differentiate from sibling tools like detect_language or translate_text, but the transcription function is distinct enough from those names.

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 invocation constraint—provide exactly one of audioUrl (HTTPS only) or audioBase64—and places the tool in a 'cross-platform Poly-Glot workflows' context. However, it does not explicitly state when to choose this tool over alternatives or when not to use it, leaving the selection guidance mostly implied by 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?

With no annotations provided, the description carries the burden of behavioral disclosure. It usefully states that meaning, formatting, names, code, and URLs are preserved, which is non-obvious and valuable for an agent. It does not explain side effects or failure modes, but for a translation tool this is a strong level of transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. The core operation is front-loaded, and the preservation caveat is a meaningful, concise addition.

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 main purpose and a key behavioral guarantee, but it leaves gaps around language value conventions, source language detection, and return format. For a simple tool this is adequate but not fully complete, especially since no output schema or parameter descriptions exist.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it only vaguely maps targetLanguage to a 'supported Poly-Glot language.' It does not clarify language code formats, the sourceLanguage default of 'auto', or how to specify the source language.

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 names a specific verb and resource: translate text into a supported Poly-Glot language. It also adds a distinguishing detail by noting what is preserved, which helps separate it from related tools like detect_language or localize_text.

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 states what the tool does but gives no guidance on when to prefer it over siblings such as localize_text or how to combine it with get_language_options. There are no exclusions, prerequisites, or alternative routing hints.

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 and non-destructive, and the description adds meaningful behavioral detail beyond that: HTTPS and SSRF checks, an optional minimal probe, and the fact that API keys are transient and never persisted or echoed. This is strong safety-relevant disclosure, though it stops short of explaining failure behavior or probe 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?

Three sentences with no filler. The first sentence gives the core purpose, and the following two add high-value behavioral and security details. The description 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 tool with 11 configurable parameters and no output schema, this description is too thin to support correct invocation. It provides a good orientation but not enough detail about required configuration or expected 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?

The schema has 11 parameters and 0% description coverage, so the description needed to clarify at least the key parameters. It mentions probe behavior and API keys conceptually, but it never names or explains critical fields like adapterMode, baseUrl, endpoint, authMode, promptField, or responseTextPath, leaving the agent to guess how to construct a valid call.

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 names a specific verb and resource: 'Validate a developer-supplied model endpoint.' It goes on to specify the concrete checks (HTTPS, SSRF, optional probe), which clearly differentiates this from sibling tools like run_custom_model or get_custom_model_capabilities.

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 intended use is implied: validate an endpoint before using it with a custom model. However, the description never explicitly states when to choose this over alternatives like run_custom_model or get_custom_model_capabilities, so the routing guidance is left to inference.

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