Poly-Glot AI Workspace

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

The description reveals non-obvious side effects and prerequisites: the first use of a free template triggers a 3-day trial, and Pro templates require an active subscription. This is valuable context beyond the annotations, which only state readOnlyHint=false, openWorldHint=false, and destructiveHint=false. It does not describe what persistent state changes occur, but the licensing behavior is a meaningful disclosure.

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, and the core action is front-loaded. The wording 'entitled' is slightly awkward and could be clearer, but the overall length and structure are appropriate.

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-parameter tool with 0% schema documentation, the description omits parameter semantics and usage guidance. It covers the core action and licensing side effects, and an output schema exists, but an agent would still struggle to know what values or languages to provide and when to choose this tool over siblings.

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 loosely implies that a named template is filled with content. It does not clarify the meanings of `values`, `uiLanguage`, `inputLanguage`, or `outputLanguage`. Most parameters remain underspecified for an agent.

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 ('Fill') and resource ('entitled Poly-Glot template'), making clear the tool builds a prompt from a named template. The title reinforces this. However, it does not explicitly differentiate the tool from siblings like get_template or localize_text, and 'entitled' is somewhat 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 description provides no guidance on when to use this tool versus alternatives such as get_template or search_templates. It only mentions trial and subscription constraints, which are licensing conditions rather than usage criteria. No exclusion or alternative routing is 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 indicate read-only and non-destructive; the description adds meaningful context: the exact count (38), the three language categories, and the guarantee that language selection never changes entitlement. This goes beyond the structured 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?

Two short sentences; the first states the exact output, the second adds a behavior guarantee. No filler 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?

For a simple read-only options tool with an output schema, the description covers what the tool returns and an important side-effect guarantee. It lacks usage context and uiLanguage semantics, but these are minor for this low-complexity tool.

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

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

Schema description coverage is 0% and the description never mentions the uiLanguage parameter, its default, or how it affects output. With no enum or description in the schema, the agent is left without any semantic guidance for the only parameter.

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 names a specific verb ('Return'), a precise resource (the 38 supported Poly-Glot UI, input, and AI output languages), and even a cardinality. This is clearly differentiated from sibling language tools like detect_language/translate_text by its 'options list' 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?

No when-to-use, prerequisites, or alternatives are given. The description implies this is for fetching supported language options, but it never says when to choose it over detect_language, translate_text, or localize_text.

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 already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is covered. The description adds that the result is 'current' and covers entitlement plus pricing, but does not disclose additional behavioral details such as authentication requirements or whether the status is fetched live or cached. No contradiction exists.

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 that front-loads the action and the exact resource. Every word contributes meaning, with no redundant or filler content.

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 status tool with an output schema present and safety annotations provided, the description is fully adequate. Nothing needed to invoke the tool correctly 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 baseline of 4 applies. The description needs to convey no parameter meaning, and the input schema is vacuously 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 verb ('Return') and a clear resource ('current Poly-Glot trial or Pro entitlement and current pricing'). This is unambiguous and distinct from all listed sibling tools, none of which cover subscription status.

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 clearly implies when to use this tool: whenever an agent needs the user's current subscription entitlement or pricing. No explicit exclusions or alternatives are needed because no sibling tool serves a similar purpose.

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 read-only and non-destructive. The description adds a meaningful behavioral trait: the source prompt body is returned only when the account is entitled to the template, which signals an authorization-dependent response.

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 main action is front-loaded, and the entitlement condition earns its place as important behavioral 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?

The presence of an output schema covers return-value details, and annotations cover safety. However, the description still lacks sibling differentiation and parameter semantics, so an agent receives enough to invoke it but not enough to reliably select it over search_templates in every context.

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 needed to explain the parameters and compensate for the schema's lack of descriptions. It does not mention name, uiLanguage, or how they affect the result, leaving parameter semantics entirely to the schema's bare names.

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 and resource: 'Get a template's fields.' This is clear and mostly distinguishes the tool from siblings such as search_templates, which searches templates, and build_prompt, which builds prompts. However, it does not explicitly name a sibling alternative, so it stops short of full differentiation.

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 is given on when to choose this tool over siblings like search_templates or get_language_options. The entitlement clause explains a condition on output but not a usage context or exclusions.

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 provide readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds one useful behavioral nuance ('subscription-aware locked states') but does not explain what happens when the user is not subscribed or what 'open' returns. With annotations carrying the main safety burden, this is acceptable but not richly transparent.

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, well-structured sentence that places the core action and resource first. There is no redundant phrasing or filler; every phrase adds meaning. This is an efficient and appropriately sized description.

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

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

For a simple tool with no required parameters and read-only annotations, the description covers the main purpose. However, it omits any explanation of the two parameters and does not clarify what the tool returns or how the subscription-aware states manifest. Given the low complexity, this is passable but has clear gaps.

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 explain what 'query' and 'uiLanguage' mean, but it does not mention either parameter at all. The agent is left with only default values and max lengths, which are insufficient to know how to populate these fields correctly.

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 action ('Open') and the resource ('interactive Poly-Glot template browser and prompt editor'), making the basic purpose evident. It also adds a distinguishing detail ('subscription-aware locked states') not present in the title. However, it does not explicitly differentiate this tool from siblings like get_template or build_prompt, so it falls just short of a 5.

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

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

There is no guidance on when to use open_workspace versus alternatives such as get_template, search_templates, or build_prompt. The description implies a UI-opening use case, but it never states conditions, exclusions, or which sibling tools to prefer instead. This leaves the agent to infer the appropriate context.

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 useful behavioral context: it performs server-side template entitlement checks and never calls third-party models. However, readOnlyHint is false, implying possible state changes, and the description does not disclose what 'prepare' actually creates, saves, or modifies. The no-external-call guarantee is valuable, but the side-effect profile remains under-specified.

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, front-loaded with the core purpose and followed by the most important behavioral caveat. No filler and no redundant repetition of schema 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?

The tool has 7 parameters, no parameter descriptions in the schema, and a similarly named sibling. The description does not explain how to construct a valid call, what the language or value fields do, or what the output represents. The existence of an output schema softens the return-value gap, but for this parameterized tool the description leaves too much unspecified.

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 undocumented parameters, but it only maps 'canonical prompt' to prompt and 'two or more AI providers' to providers. The meaning of values, uiLanguage, inputLanguage, outputLanguage, and name is left entirely to inference from property names, which is insufficient at this coverage level.

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 ('prepare'), a clear resource ('one canonical prompt for two or more AI providers'), and the intended outcome ('compare answers'). It also distinguishes itself from build_prompt by referencing the same entitlement checks and explicitly stating it never calls third-party models. However, it does not explicitly differentiate from the near-identical sibling prepare_custom_compare, so it stops short of full sibling separation.

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

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

The description implies the tool is for multi-provider answer comparison and clarifies it does not call third-party models, which gives some usage context. But it does not state when to choose this tool over build_prompt or prepare_custom_compare, nor does it give exclusions or prerequisites beyond the entitlement-check mention.

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?

Annotations already declare the operation read-only, non-destructive, and not open-world, so safety is covered. The description adds one useful behavioral detail: results indicate whether a template is locked for the current account. However, it does not disclose pagination, ordering, or other search 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?

The description is two short sentences with no wasted words. The first sentence states the core purpose, and the second adds a relevant result detail, making it appropriately concise and easy to parse.

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 five parameters and no schema-level descriptions, this short description leaves key invocation details unspecified. The output schema may cover result structure, but parameter semantics and search behavior are not sufficiently documented for the agent to call the tool with confidence.

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 information about the five parameters. The agent is left to guess the meaning of goal, query, plan, limit, and uiLanguage from names alone, and the distinction between goal and query is not addressed.

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 ('Find') and resource ('Poly-Glot prompt templates'), and adds a concrete output detail (lock status). It is clear, though it does not explicitly contrast with the sibling get_template, so differentiation is mostly inferred from the name and verb.

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 tool is for discovering templates, but it does not state when to use it versus alternatives like get_template. There is no explicit when-to-use or when-not-to-use guidance, so the agent must infer the appropriate scenario from the tool name and 'Find' verb.

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.