Halal or Not

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety bar is low. The description still adds real behavioural context: it returns a verdict, the main scholarly views, and halal alternatives, and accepts raw ingredients/E-numbers. It stops short of saying answers reflect scholarly opinion rather than a definitive ruling.

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

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

Front-loaded with the core purpose, then scope and return content. The long parenthetical category list is dense but each item expands the discoverable domain, so it largely 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 one-parameter Q&A tool with no output schema, the description covers input scope, expected output shape and domain breadth, while annotations cover the safety profile. A note on how authoritative/definitive the verdict is would complete it.

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

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

Schema coverage is 100% and the single parameter is documented with an example, so the baseline is 3. The description goes further by defining the acceptable input space — free-form questions plus single ingredients and E-numbers — which meaningfully supplements the schema's generic 'plain words' instruction.

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

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

States a clear verb+resource ('Ask any is it halal question') and enumerates the domains it covers, so an agent understands this is the general-purpose free-form question tool. It does not explicitly contrast itself with the specific siblings (check_crypto, check_fund, screen_stock, check_ingredients), which is the only thing keeping it from 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?

The domain list implies when the tool is applicable, but there is no explicit guidance on when to prefer it over the granular siblings (e.g. a specific stock vs a general question). Usage is inferred rather than stated.

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

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so safety is covered. The description adds genuine behavioral value by enumerating the classification buckets (lending, gambling, meme, stablecoin, staking, general-purpose) and stating that scholarly views are returned, which previews the response content absent an 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?

Two compact sentences, front-loaded with the core question and then the classification/answer scope. The opening repeats the title verbatim, which is mildly redundant but still functional as a lead-in.

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

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

With no output schema, the description usefully indicates what comes back (a functional classification plus scholarly views), and the single parameter is fully described in the schema. Annotations cover the safety profile, so nothing critical is missing, though the response format could be described more concretely.

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

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

There is a single parameter with 100% schema description coverage, including format examples (bitcoin, ETH, AAVE), so the schema carries the burden. The description adds no semantics about the coin argument beyond what the schema already documents, matching the baseline for high-coverage schemas.

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

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

The description states a specific action (classifying a coin by function) and an additional output (scholarly views on crypto usage), which is more than a restatement of the title. It is clearly distinct from siblings like screen_stock or check_fund, though it doesn't explicitly name them.

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

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

Usage is implied by the domain — an agent would use it for questions about a specific cryptocurrency's permissibility — but there is no explicit when-to-use, when-not-to-use, or comparison to alternatives such as ask_is_it_halal or screen_stock.

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, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful method disclosure: it reads the latest SEC holdings report and screens every holding by industry and debt, and explains the UK-listing fallback strategy — behavior that goes beyond the 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?

Three sentences with the core question front-loaded and no filler; each sentence carries information (recognition shortcut, US screening method, UK fallback). The final sentence is dense with examples but still earns its place by defining coverage boundaries.

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 only one parameter and no output schema, the description supplies the method, coverage and fallback behavior an agent needs to trust and select the tool. It does not describe the shape of the verdict returned (e.g., pass/fail detail or reasoning), which is the main residual gap.

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

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

Schema description coverage is 100% and the schema already gives ticker examples (VOO, QQQ, SPUS) and notes a Shariah fund name is accepted. The description echoes that input surface and adds UK ETF tickers, but adds little syntactic or format meaning beyond the schema, so the baseline 3 applies.

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

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

States a specific verb+resource: answering whether a fund, ETF or pension fund is halal, and enumerates the accepted input classes (Shariah funds by name/ticker, US-registered funds/ETFs, UK-listed ETFs). This clearly separates it from siblings like screen_stock, check_crypto or check_medicine, which handle different asset/object types.

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 delineates the scope of what the tool will handle and the fallback path for UK-listed ETFs (via a US fund tracking the same index, or the index's holdings). It gives clear context for when this tool applies, but never explicitly names an alternative sibling 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 already declare the safe read-only/idempotent profile, so the bar is lower, and the description adds real value by disclosing the return shape (overall verdict, per-school verdicts, flagged ingredients with reasons) despite there being no output schema. It omits any note on input robustness or limits, so it is not exhaustive.

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 tight sentences, front-loaded with the action, then input contract, then return contract. Every sentence earns its place with no redundancy.

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

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

With no output schema, the description correctly carries the return-value burden, and the fully-covered input schema plus safety annotations round out the picture. The only gap is the absence of when-to-use routing against the many sibling checkers.

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

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

Schema description coverage is 100%, so the schema already documents both 'ingredients' and the 'context' enum in detail. The description reinforces the input format for 'ingredients' but adds no syntax or semantic detail beyond what the schema provides, making the baseline 3 correct.

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

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

States a specific verb+resource ('Check an ingredients list') and specifies the input modality ('exactly as printed on the pack'), which distinguishes it from barcode-based siblings like check_product_by_barcode. It does not, however, name or contrast itself with the closest alternatives (check_medicine, explain_ingredient).

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 instruction to pass ingredients 'exactly as printed on the pack' implies the use case (you have the raw label text, not a barcode), but the description never states when to prefer this over check_product_by_barcode or check_medicine, nor any exclusions. Usage is only implied.

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 the safe-read profile (readOnly, idempotent, non-destructive), so the bar is lower, and the description still adds real value: the US-label/openFDA provenance, the specific substances flagged, and the deliberate design of leading with a necessity ruling so prescribed medicine is not stopped. Missing rate-limit or label-availability caveats.

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

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

Front-loaded with the user-facing question, then the mechanism and the output shape in two tight sentences. Every clause carries information, though the 'so nobody stops a prescribed medicine' rationale is slightly discursive rather than operational.

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?

No output schema exists, so the description must carry the return-shape burden, and it names the returned fields (ingredient_headline, verdict) plus the source and its US-scope limitation. For a single-parameter read tool, an agent has everything needed to call and interpret it.

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

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

Only one parameter (name) with 100% schema description coverage ('Brand or generic name'), so the schema already carries the semantics. The description does not add format guidance beyond implying a medicine name; baseline 3 applies when structured data does the heavy lifting.

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

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

States a concrete verb and resource — reads a medicine's active and inactive ingredients from its US label via openFDA and flags specific haram substances (gelatine, alcohol, pig-derived enzymes, heparin). The medicine-specific, US-label, openFDA framing clearly separates it from generic siblings like check_ingredients and check_product_by_barcode.

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 domain (medicine) and data source (openFDA label) imply when to reach for this tool, but no alternative is named. An agent must infer whether check_ingredients, check_product_by_barcode, or ask_is_it_halal is the better fit for a non-medicine or non-US-label query.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description usefully adds the sequential lookup chain (Open Food Facts, then Open Beauty Facts) and that ingredients are checked, but omits what happens on a miss, whether results are cached, or any rate/latency expectations.

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, front-loaded with the action, and every clause carries information (barcode, lookup sources, ingredient check). The first sentence restates the title verbatim, which is minor redundancy but not wasteful.

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

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

No output schema exists, so the description bears some burden for explaining what is returned. It hints that ingredient findings come back, but does not describe the shape of the response (product metadata, ingredient verdicts, not-found handling), leaving a gap for a read tool that returns data.

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

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

Schema description coverage is 100% and the single barcode parameter is already documented as 'EAN-13, EAN-8 or UPC barcode digits.' The description adds no format, normalization, or validation detail beyond what the schema provides, so the baseline 3 applies.

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

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

States a specific verb and resource (check + product by barcode) and names the two lookup sources it consults (Open Food Facts, then Open Beauty Facts) plus the ingredient check. It does not explicitly distinguish itself from siblings like check_ingredients or search_products, so the boundary is inferable but not stated.

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: the agent must have a barcode (EAN-13/EAN-8/UPC) to use this tool. There is no explicit when-to-use versus check_ingredients or search_products, and no exclusion or fallback guidance if the barcode is not found in either database.

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 readOnly, idempotent, non-destructive and openWorld behavior, so the bar is lower. The description adds genuinely useful context beyond them by disclosing the response shape: a verdict per school plus reasoning. It does not mention rate limits or how ambiguous names are resolved.

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

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

Two sentences, zero filler, and the core purpose is front-loaded before the illustrative examples and the return-value note.

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

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

With no output schema, the description usefully summarizes what comes back (per-school verdicts and reasoning), which is the main gap a caller would have. The cosmetic/food context option is left to the schema, which is acceptable given its full coverage.

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

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

Schema description coverage is 100%, so both parameters are fully documented in the schema, including the food/cosmetic enum and its default. The description's name examples duplicate the schema's, adding no meaning beyond it; baseline 3 is appropriate.

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

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

States a specific verb (Explain) and resource (one ingredient or E-number), and the scope word 'one' implicitly separates it from the bulk sibling check_ingredients. The concrete examples ('E471', 'carmine', 'prawns') remove any ambiguity about what counts as an ingredient.

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

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

Usage is only implied: the tool is clearly for a single lookup, which contrasts with check_ingredients, but the description never states when to pick this over that sibling or what prerequisites exist. No when-not guidance 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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is fully covered. The description adds substantive behavioral content beyond that — it discloses the comparison output shape (agreement vs. difference, with reasoning and halal alternatives) — but says nothing about sourcing, authority ranking of scholarly bodies, or response format.

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

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

Front-loaded with the core purpose in the first sentence, followed by the domain coverage. The second sentence is one long run-on enumeration, and the text appears truncated ("halal alt..."), but there is no filler or repetition.

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

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

With no parameters, no output schema and read-only annotations, the description carries the remaining burden: it states the scope, the covered domains, and the nature of the returned comparison. It is adequate, though adding a pointer to the sibling verdict tools would close the last gap.

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 declares zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; the description correctly implies a purely knowledge-driven lookup with no input to supply.

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 concrete resource (how the four Sunni schools differ) and enumerates the ruling domains it covers (food, cosmetics, medicines, stocks, funds, crypto, money products, everyday life). It is distinguishable from siblings like ask_is_it_halal in spirit, since this explains scholarly disagreement with reasoning rather than issuing a verdict, but that distinction is never stated 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?

There is no when-to-use guidance, no exclusions, and no named alternative. Worse, the phrase "Answers 'is it halal?'" overlaps directly with the sibling ask_is_it_halal, leaving the agent to infer which tool to pick for a straightforward halal question versus a comparative-schools question.

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, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context beyond that: it says nothing about timezone handling, data source, or output shape, and the halal-rulings sentence is behaviorally irrelevant to a prayer-times 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 text is truncated mid-sentence ('halal alt...') and devotes most of its length to an unrelated block about halal rulings for food, cosmetics, medicines, stocks and crypto. That content does not earn its place in a prayer-times definition and buries the one useful clause about Asr/suhoor/iftar.

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 schema is rich and the annotations are complete, and no output schema exists, so the description need not explain return values. But for a zero-required-parameter, location-sensitive tool it should clarify at minimum what happens when no place is given and how the method affects results. Instead the space is spent on off-topic halal content, leaving the agent with an unclear mental model of the tool.

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

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

Schema description coverage is 100%, with lat/lon, city, date and the method enum all fully documented in the schema, so the baseline is 3. The description adds no syntax, precedence (city vs coordinates), or default-method guidance beyond what the schema already says.

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

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

The opening clause names a concrete verb and resource ('Today's prayer times for a place, with both Asr times and Ramadan suhoor and iftar'), which is clear and specific. However, the description then pivots into halal food/cosmetics/stocks screening content that belongs to the sibling tool ask_is_it_halal, muddying what this tool actually returns. An agent could still pick it out, but the conflation of two distinct purposes is a real defect.

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 city vs lat/lon, which calculation method to choose or when it matters, or how this differs from the many check_*/explain_* siblings. The halal-screening sentence even implies this tool covers rulings, but never states when to use it versus ask_is_it_halal. Usage is left entirely to inference.

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, idempotentHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered. The description adds content-scope context (cross-school agreement/difference, reasoning, alternatives) but says nothing about breadth, structure, or pagination of the returned rules.

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 lead sentence is crisp and front-loaded, but the second sentence is a long run-on covering many domains and trails off mid-word ('halal alt...'), which suggests it is truncated and not fully readable. Structure is acceptable but the sentence sprawls.

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

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

With no parameters and no output schema, the description carries most of the burden. It conveys content coverage but omits how the rulebook is organized, how much it returns, and how it relates to the many sibling lookup tools, leaving gaps for an agent deciding whether to invoke it.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a 0-param tool is 4. No additional parameter meaning is required or missing.

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 title and first sentence frame it as a browsable 'full rulebook,' but the bulk of the description describes answering 'is it halal?' questions, which is exactly what the sibling ask_is_it_halal appears to do. It never clarifies the distinction between listing rules and asking a specific halal question, so the agent can't confidently choose between them.

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

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

No when-to-use or when-not-to-use guidance is given. With siblings like ask_is_it_halal, list_topics, and explain_schools, the description should say when the full rulebook is preferable over a targeted lookup, and it does not.

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, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds domain context (four Sunni schools, scholarly bodies agree/differ, reasoning, halal alternatives), but says nothing about result shape, volume of topics returned, or whether the listing is exhaustive vs paginated.

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 first sentence is a compact frame but simply restates the title verbatim. The second sentence is a long, truncated run-on that spends its length cataloguing subject areas instead of clarifying what the tool does or returns, so the structure is not front-loaded with the actual 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?

This is a low-complexity, read-only, zero-required-parameter tool, so it does not need much. However, with no output schema present, the description should describe the return values (a list of topics/questions) and it does not, leaving the caller unsure what a successful response contains.

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

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

Schema description coverage is 100% and the single enum parameter is fully documented in the schema, so the baseline is 3. The description's domain enumeration ('cosmetics', 'stocks', 'funds') does not map cleanly onto the enum values (money, crypto, medicine, food, life), adding no usable semantics for the filter.

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 frames the tool as 'every question the rulebook covers' and enumerates the domains (food, cosmetics, medicines, stocks, funds, crypto), which hints at a catalogue of topics. But it never states the verb 'list' or 'returns topics', and the second sentence ('Answers is it halal?') describes answering behavior that belongs to the ask_is_it_halal sibling, so the tool's actual identity is muddled rather than distinguished.

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

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

There is no explicit when-to-use statement and no routing to alternatives, even though ask_is_it_halal, list_rules, and the check_* siblings clearly compete in the same space. An agent must infer whether this tool lists question topics or answers them.

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

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds real process detail beyond that: it runs a business screen against each standard's excluded industries, then the AAOIFI, Dow Jones, S&P and MSCI debt screens, and returns halal alternatives — useful multi-step and output context.

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

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

Front-loaded with the user-facing question, then three tight sentences covering process, accepted input, and output. Every sentence adds something; no filler.

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 one-parameter read-only tool with no output schema, the description covers the screening steps, alternate input form, and a hint at the return (links to halal alternatives). Only the exact result shape and any rate/coverage caveats are left 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 coverage is 100% for the single ticker parameter, giving a baseline of 3. The description goes further by stating a company name is also accepted ('nvidia, philip morris'), which is meaningfully broader than the schema's 'US stock ticker' wording.

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

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

States a specific verb and resource ('Screens a US-listed stock') and clarifies scope (US-listed, via Mizan). The stock-specific scoping plus asset-class framing lets an agent distinguish it from check_crypto and check_fund without opening schemas.

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

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

Clearly scoped to US-listed equities and notes that a company name works as input, which covers the main invocation context. No explicit when-not or routing to check_fund/check_crypto, though sibling names make the boundary largely inferable.

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 safety profile (readOnly, idempotent, openWorld, non-destructive), but the description adds real behavioral substance: it caps results at five matches, returns a verdict per match plus a 'say' line, and explains that brand-stated ingredient sources override unnamed labels and are quoted in brand_statement. That is meaningful beyond the structured fields.

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

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

Three sentences, front-loaded with purpose and source before the return-shape detail and the brand_statement nuance. The third sentence is dense and fairly niche but does explain a real behavior, so it earns its space without bloat.

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

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

With no output schema, the description carries the burden of describing returns, and it does so reasonably: up to five matches, verdicts, a 'say' line, and brand_statement. It is essentially complete for an agent to call and interpret the tool, with only minor gaps like ordering or empty-result 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 100%, so q (name/brand/barcode) and country (slug, default, Amazon-store effect) are already fully documented. The description adds no syntax or format detail beyond the schema, so the baseline 3 is appropriate.

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

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

The description states a specific verb and resource ('Find products by name and check them') plus the data source (Open Food Facts), so the agent knows exactly what it does. It does not, however, differentiate itself from the close siblings check_product_by_barcode and ask_is_it_halal, which appear to overlap heavily.

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 'by name or brand' and the example query, suggesting it is used when the agent has a name/brand rather than a barcode. But there is no explicit when-to-use, when-not, or routing to the obvious alternative (check_product_by_barcode) for barcode lookups, so 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.