Digital in Asia

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds useful context the annotations cannot: the underlying data source (Digital in Asia's tracker) and that results come as comparison tables with source links, which tells the agent what it will get back.

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 the core action and scope front-loaded, the topic filter next, and the return format last. No filler or repetition.

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

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

For a simple two-parameter read tool with rich annotations, the description covers what it does, the optional filter, and the shape of the output. With no output schema present, it could say slightly more about table contents, but nothing needed to invoke it 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?

Schema description coverage is 100% and the schema already enumerates the topic values and the 2-6 market bounds, so the description adds little beyond restating the topic list. Baseline 3 is appropriate when the schema carries parameter semantics.

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 (compare markets side by side), the data source (Digital in Asia's tracker data), and the domain restriction (Asian markets). The comparative framing clearly separates it from single-entity siblings like get_country_profile and get_tracker.

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 'two or more' and the enumerated topics imply the use case, and 'side by side' signals it is for multi-market comparison rather than a single lookup. However, the description never explicitly names an alternative such as get_country_profile for single-market questions, leaving the routing 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, idempotentHint, non-destructive and closed-world, so safety is covered. The description adds meaningful behavior the annotations don't: that this is an aggregation across multiple trackers plus articles, which tells the agent to expect a large composite payload rather than a narrow record. It omits how an unsupported market is handled and gives no sense of payload size or limits.

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

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

A single front-loaded sentence leads with the resource and scope, then the payload contents, then valid inputs. The trailing market enumeration is long but functional; it could have been shortened to a pointer if the schema carried an enum.

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 return-value burden and discharges it by naming the three component parts of the response. For a single-parameter, read-only aggregator, that plus the market list is enough for an agent to invoke it confidently.

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 there is only one parameter, so the baseline would be 4. The description still adds value by enumerating the 16 supported markets, which constrains the free-text string beyond the schema's example-only hint ("Vietnam") and reduces invalid-input retries.

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 ("overview of one Asian digital market") and enumerates exactly what the payload aggregates: hub summary, rows from every DIA tracker, and recent articles. It is distinguishable from siblings in substance, but never names a sibling such as compare_markets or get_tracker, leaving the boundary implicit.

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

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

The phrase "Overview of one Asian digital market" implies the single-market, all-in-one use case, and the market list makes clear the scope of acceptable inputs. There is no explicit when-to-use versus compare_markets (multi-market) or get_tracker (single tracker) guidance, so the agent must infer routing from sibling names.

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

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

Annotations already cover read-only, idempotent and closed-world safety. The description adds genuinely new behavior: it returns a definition plus link and will suggest close matches when the exact term misses, which tells the agent how to handle a miss. It does not state what the miss response looks like beyond 'close matches'.

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 action and scope, followed by return behavior. 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?

With one parameter, no output schema and strong annotations, the description covers purpose, expected input vocabulary and return shape. The only gap is the fuzzy-match behavior being described but not specified.

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 carry the load. The term examples clarify the expected vocabulary (acronyms, concepts like 'super app'), but nothing is said about casing, exact-match requirements, or multi-word handling.

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 ('look up a term in the Digital in Asia glossary'), quantifies scope (103 terms) and gives representative examples (UPI, QRIS, super app). It is unmistakably different from the sibling market/report tools.

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 examples imply the domain where this tool applies, so usage is inferable, but there is no explicit statement of when to prefer it over e.g. search_dia or get_country_profile, and no 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?

Annotations already declare the safe read-only, idempotent, closed-world profile, so the description only needs to add ordering/scope context. It does contribute that results are ordered by recency, but omits result count behavior, pagination limits, and what an article record contains.

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

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

A single front-loaded sentence with no wasted wording. It could carry slightly more routing information without becoming bloated, but nothing here is redundant.

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

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

For a simple two-parameter read tool with full annotation coverage and no output schema, this is nearly enough, but the agent still cannot tell how many articles to expect, how to widen the search, or what fields come back. A brief note on ordering scope and the search_dia alternative would close the 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 phrase filter is reflected as 'query', matching the one documented parameter, but the description says nothing about 'limit' — its purpose, default of 10, or ceiling of 20 — so with 50% schema coverage half the parameters rely purely on the schema. Baseline 3 is appropriate since the description adds meaning for one parameter and nothing for the other.

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 resource (Digital in Asia articles) and a specific selection criterion (most recent, optionally phrase-filtered), so an agent knows exactly what it returns. It does not distinguish itself from the sibling search_dia, which likely overlaps heavily on the phrase-filter path.

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 guidance and no mention of search_dia as the alternative for broader or relevance-ranked searching. 'Optionally filtered by a phrase' hints at usage but leaves the agent to infer that this is the recency-oriented variant.

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 closed-world behavior, so the safety profile is covered. The description adds genuinely useful behavioral context that annotations cannot: the response contains summary findings, headings, and a free-PDF link rather than full text. It does not say what happens if the slug is ambiguous or unmatched.

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

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

One sentence, front-loaded with the deliverable ('Key findings and section headings') and ending with the PDF link. The parenthetical topic list is long but earns its place by hinting at valid inputs. No filler or repetition.

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

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

For a one-parameter read tool with full annotation coverage and no output schema, the description supplies the essential return-value picture an agent needs. The only gap is failure/ambiguity behavior for an unrecognized report slug, which is minor at this complexity.

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 a type and an example ("fintech"), so the schema does the work. The description's topic list ('fintech and digital payments, AI in Asia, Southeast Asia ecommerce, Vietnam gaming') indirectly hints at acceptable slug values, which is a modest addition beyond the schema.

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

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

The description concretely states what comes back: key findings, section headings, and a link to the free PDF for a Digital in Asia research report, and it names the covered topic areas. It is clear what the tool does, though it never explicitly contrasts itself with list_reports or search_dia, so sibling differentiation is left implicit.

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

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

Usage is implied by the output description (reach for this when you need the substance of one specific report), but there is no explicit when-to-use or when-not-to-use guidance and no mention of list_reports as the way to discover valid report slugs first. An agent can infer the situation but is not routed.

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, closed-world, non-destructive, so the safety profile is covered. The description still adds real behavioral value by disclosing the no-argument enumeration mode and that the argument accepts partial title words rather than exact slugs, which is not derivable from the annotations. It stops short of describing error behavior for unmatched trackers or the shape of returned tables.

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: the capability and domain list come first, the discovery shortcut second. Front-loaded and appropriately sized for a single-parameter read tool.

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 low-complexity, single-optional-parameter read tool with full annotation coverage and no output schema, the description plus schema gives an agent everything needed to call it correctly, including the parameterless listing path.

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

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

Schema coverage is 100% and the schema already states the parameter is a slug or a few words of the title with an example. The description only implicitly reinforces that the parameter is optional via the no-argument listing mode, adding little beyond structured data. Baseline 3 applies when the schema carries the load.

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 ('Get') and a specific resource ('structured data tables from a Digital in Asia market tracker'), and enumerates the covered domains (payments, LLM benchmarks, gaming, ecommerce, AI policy, market size). That is far more informative than the title alone, though it never contrasts itself with siblings such as get_report_summary or list_reports, so an agent must infer the boundary.

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?

'Call with no arguments to list trackers' is a genuine usage rule that tells the agent how to discover valid inputs before calling with a slug. However, there is no guidance on when to choose this over get_report_summary, list_reports, or search_dia, and no exclusions are 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=true, idempotentHint=true, destructiveHint=false and a closed-world scope, so the safety profile is covered. The description adds value beyond that by specifying the payload fields returned and that the catalog is limited to free reports, though it says nothing about result volume, ordering, or truncation.

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

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

A single front-loaded sentence with no filler; the resource and the returned fields are both packed into it 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 zero-parameter, read-only listing tool with no output schema, the description supplies enough: the resource, the scope ('free'), and the shape of each result. A brief note on whether the full catalog is returned or paginated would make it complete.

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

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

There are zero parameters, so the baseline is 4; the description correctly implies an unfiltered full listing and therefore adds no misleading parameter expectations. No further parameter meaning is needed or possible.

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 gives a specific verb and resource ('List Digital in Asia's free research reports') and enumerates the returned fields (titles, descriptions, dates, links), so the agent knows exactly what it retrieves. It does not, however, distinguish this tool from sibling retrieval tools like get_report_summary or search_dia, which a reader must infer covers a broader, summary-oriented or query-driven 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?

Usage is only implied: the tool takes no parameters, so it is evidently the 'list everything' entry point, and the word 'free' hints at scope. There is no explicit statement of when to use this versus search_dia or get_report_summary, and no note about whether all reports are returned or a subset.

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 read-only, idempotent and closed-world behavior, so the safety profile is covered. The description adds that matching is semantic and that the response carries titles, URLs, dates and relevant passages, which is genuinely useful given no output schema exists. It says nothing about result ranking, coverage limits, or the effect of the 15-result cap.

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 the corpus and purpose front-loaded and return contents last. Each sentence carries distinct information; only the parenthetical topic list is slightly expendable.

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

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

For a two-parameter search tool with no output schema, the description covers corpus, query mode and return shape, which is what an agent needs to invoke it correctly. The omission is the undocumented limit parameter and any note on ranking or result count.

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

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

Schema coverage is 50%: the query parameter is documented in the schema with an example, and the description reinforces that natural-language questions are accepted. The limit parameter is undocumented in both places, so its meaning and the default/max bounds are left to the raw schema. 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 (semantic search) and enumerates the corpus precisely: articles, trackers, reports and glossary on Asia's digital economy, with topic examples. This clearly separates it from siblings like get_latest_articles, get_glossary_term and get_tracker, which target single content 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 signals the intended mode of use (natural-language questions, semantic matching) but never states when to choose this over get_latest_articles for recent news or get_glossary_term for a definition. Usage is implied rather than routed.

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