opendata-radar-pro

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

No annotations are supplied, so the description carries the full behavioral burden. It does disclose two valuable facts beyond the schema — a Bearer sp_live_... API key requirement and a $0.02/call cost — but says nothing about data freshness cadence despite billing itself 'Real-Time', nor about pagination, rate limits, or whether the operation is purely read-only.

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

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

A single compact line with the domain and audience front-loaded, followed by the auth and pricing constraints an agent needs before calling. No filler sentences, though the length is on the thin side for a paid external API.

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 three optional parameters, no output schema, and no annotations, the description covers what data exists and the cost/auth model but never indicates the shape or structure of the response. For a priced third-party API with zero structured safety metadata, more detail on return contents and real-time update behavior would be warranted.

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 each of the three optional parameters (limit range, apiKey fallback, keyword filter examples) fully documented in the schema itself. The description adds no additional meaning about limit defaults, filter semantics, or how keyword matching interacts with Korean-language terms, 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?

The description names a specific resource and scope: surging South Korean search demand keywords, traffic volume tiers, and breaking news triggers. It is clearly distinguishable from every sibling (github bounties, auctions, b2b leads, procurement bids, saas pain points, grants) by domain. It loses a point only because the operation is expressed as a noun-phrase tagline rather than an explicit verb, leaving 'retrieve vs. monitor' slightly 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 only through the stated audience ('for e-commerce sellers and marketers'), with no explicit when-to-use, when-not-to-use, or named alternative among the seven siblings. An agent can infer this is the Korea e-commerce trend source, but nothing tells it how to choose between this and, say, query_saas_pain_points for adjacent market research.

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

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

With no annotations, the description carries the full behavioral burden, and it does disclose two important operational facts: a Bearer sp_live_... key is required and each call costs $0.03. It also reveals that results are pre-scored/ranked, which affects how the agent should treat ordering. It does not cover pagination, rate limits, result freshness, or what happens when the keyword matches nothing.

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 segments: a resource/scope statement followed by a parenthetical covering auth and pricing. Front-loaded and free of filler. The em-dash product framing is mildly promotional but does not cost readability.

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 three-optional-parameter read tool this is close to sufficient, and the scoring dimensions hint at what fields come back. However, with no output schema and no annotations, the description never states the shape of a result (issue title, repo, bounty amount, score) or whether ordering is guaranteed. That leaves a real gap for an agent deciding whether to call 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 description coverage is 100%, so limit, apiKey, and keyword are already fully documented, including the 1-15 range and the auth-header fallback. The description adds no parameter-level meaning beyond that, which is acceptable but not additive. 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?

The description names a specific resource (open GitHub software engineering issues/bounties) and adds that results are normalized and scored by repository activity, language ecosystem, and difficulty. That is enough to distinguish it from the sibling tools, which all cover unrelated domains (auctions, procurement, grants). The word 'Tracker' is slightly product-marketing rather than a verb, so it is not a clean 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 statement of when to call this versus the sibling query_* tools, no prerequisites beyond the API key, and no exclusions. An agent must infer usage purely from the domain label. Only the implicit domain match gives any routing signal.

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 does carry real behavioral context: it discloses the required Bearer sp_live_ API key and the $0.04/call cost, which an agent needs before invoking. It does not disclose whether results are paginated, how they are ordered, or what a returned notice looks like, so the read-only/return behavior is left to inference.

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 dense sentence, front-loaded with the resource and source, followed by the auth/cost parenthetical. Efficient, though the triple-filter list and em-dash construction make it slightly packed rather than crisply structured.

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

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

For a three-optional-parameter read query with full schema coverage, the description covers source, authentication, and pricing. With no output schema, the shape of returned notices is undocumented, which leaves a meaningful gap for result handling.

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 limit, apiKey, and keyword are already documented in the schema; the description adds no syntax or format detail about them. The mention of 'regional filters' and 'asset categories' loosely gestures at filtering but does not map to any concrete parameter. 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: official South Korean public asset sale notices aggregated from data.go.kr (KAMCO Onbid), with notice schedules, asset categories, and regional filters. An agent can distinguish it from query_public_procurement_bids by the 'asset sale notices' framing, though no sibling is named 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 description never says when to prefer this tool over query_public_procurement_bids or simulate_public_auction_margin, nor are there any exclusions or prerequisites beyond the API key. Usage is only implied by the subject matter.

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 and does meaningful work: it discloses the Bearer sp_live_... API key requirement, a $0.05/call cost, and that only strictly public entity names and road addresses are returned (a privacy/data-provenance guarantee). It does not cover result ordering, refresh cadence, or pagination behavior, so it stops short of full transparency for a paid endpoint.

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

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

It is a single dense sentence-block that front-loads the dataset identity, then layers audience, provenance, privacy, and cost in a sensible order. Nearly every clause earns its place, though the title-cased header and redundant '(Public Open-Data)' parenthetical add some clutter.

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 three-parameter read-only query with full schema coverage and no output schema, the description covers the essentials: what the data is, where it comes from, who it is for, that it costs money, and that it requires auth. The main remaining gap is what a returned record actually contains beyond name and road address, which an agent might want to judge relevance.

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 limit, apiKey, and keyword are already fully documented with types and examples. The description adds only indirect context, notably that an API key is required and billable, but says nothing about how keyword matches against entity name vs. address, or how limit interacts with cost. 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 names a concrete resource and scope: newly licensed and newly opened South Korean commercial establishments (restaurants, clinics, academies, retail) drawn from official public business registries. It is clearly distinguishable from siblings like query_kamco_public_auctions or query_public_procurement_bids by domain and geography, though it reads more as a dataset blurb than a statement of the action performed.

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 through the stated audience ('B2B sales teams (signage, interior, POS, tax accounting)'), which gives an agent a reasonable sense of fit. There is no explicit when-to-use vs. when-not guidance and no routing to or away from the seven sibling query tools, which matters given how many similar lead/trend datasets exist.

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

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

With no annotations, the description carries the burden. It discloses the required Bearer API key and cost per call ($0.04), which is valuable behavioral context. However, it doesn't state whether the operation is read-only, idempotent, or any rate limits, though the query name implies safety.

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

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

The description is a single, front-loaded sentence that efficiently conveys the tool's domain and key details. The parenthetical cost/auth info is compact and doesn't distract.

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 query tool with no output schema, the description lists the returned fields and mentions auth/cost. It omits pagination details beyond the limit parameter, but the schema covers that. It is largely complete for an agent to invoke correctly.

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

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

Schema coverage is 100%, so parameters are fully described in the schema. The description adds no parameter-specific meaning beyond the schema, only describing returned fields. 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 clearly states it queries high-value Korean public procurement bids from Korea PPS NaraJangteo, listing the normalized fields. It distinguishes from most siblings by domain, but doesn't explicitly contrast with query_kamco_public_auctions (auctions vs bids) or simulate_public_auction_margin.

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 guidance or exclusions are provided. It doesn't indicate when to prefer this tool over query_kamco_public_auctions or others, leaving the agent to infer from the 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 supplied, the description carries the full burden, and it does disclose two operationally important facts: a Bearer sp_live_... API key is required and each call costs $0.02. It stops short of describing result ordering, recency window ('real-time' is asserted but undefined), or how many items typically come back, all of which matter for a paid data-feed call.

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 resource description and followed by the auth/cost prerequisite. Every clause carries information; the only soft spot is the branded 'Developer & B2B SaaS Pain-Point Radar' lead-in, which spends words on positioning rather than semantics.

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, three-optional-parameter query tool with no output schema, the description covers what the data is, the auth requirement, and the cost. The remaining gap is return shape and volume expectations, which is modest but noticeable given there is no output schema to fall back on.

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 defines limit (1-15, default 8), apiKey, and keyword with examples. The description adds only a mild hint that an Authorization Bearer header is the preferred auth path, which slightly clarifies the apiKey parameter's role as a fallback, but otherwise adds nothing the schema doesn't already say. Baseline for full coverage is 3; the auth clarification lifts it marginally.

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 set — structured pain points, unmet tooling demands, and micro-SaaS wedges extracted from developer discussions — so an agent knows exactly what data comes back. It lacks an explicit verb (query/list/search) and the 'Pain-Point Radar' branding is marketing framing, but the resource is unambiguous and clearly distinct from domain-specific siblings like query_ecommerce_trends or query_public_procurement_bids.

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, when-not-to-use, or alternative-tool guidance. The agent must infer from the topical content alone that this is the tool for developer/SaaS pain-point research, and the description never explains how the optional keyword filter should be applied or when to omit 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?

With no annotations supplied, the description carries the full burden and does disclose the two highest-stakes traits: Bearer sp_live_... authentication and a $0.03/call charge, plus that data is 'live ongoing'. It stops short of return shape, pagination, or rate-limit behavior, and the '1 to 15' item cap lives only in the 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?

A single dense sentence plus a parenthetical cost/auth note, front-loaded with the resource and geography. Mild marketing framing ('Radar', em-dash list) but no sentence is wasted.

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 three-optional-parameter query tool with no output schema, the definition covers what the tool returns at a category level (grants, D-day, agencies, application URLs) plus auth and cost. Only secondary details such as result ordering or default limit are unaddressed, and the limit default is in the schema.

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

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

Schema coverage is 100%, so limit, apiKey, and keyword are fully self-documenting, and the description adds nothing parameter-specific beyond implying keyword relevance through examples in the schema. Baseline 3 applies since the schema already does all the work.

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?

Names a specific resource and scope: live ongoing South Korean government startup/SME grants and R&D subsidies, plus the fields returned (D-day countdowns, supervising agencies, application URLs). The domain is clearly distinct from siblings like query_public_procurement_bids or query_kamco_public_auctions, though the description never explicitly contrasts itself with 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 guidance, no exclusions, and no mention of the sibling tools an agent should pick instead for non-Korean or non-grant data. The only conditioning is a pricing/auth note, which is behavioral rather than usage routing; the agent must infer that 'Korean startup grants' is the trigger condition.

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

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

No annotations exist, so the description carries the full burden. 'Free utility tool' signals no auth or cost barrier and the wording implies a pure simulation with no external side effects, but it never confirms that no live auction data is fetched or whether results are deterministic. For a no-annotation tool this leaves meaningful gaps.

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

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

A single front-loaded sentence with no filler, and the output list is the most useful part placed early. The 'Free utility tool:' prefix is slightly marketing-flavored but does convey cost/auth information.

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

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

There is no output schema, and the description compensates by enumerating the five computed values, which is exactly what an agent needs to judge relevance. The residual gaps are the undocumented propertyTitle parameter and the missing units/format of returned figures.

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 80%, so the baseline is 3. The description names the computed outputs but adds nothing about input semantics such as units, whether category defaults to RESIDENTIAL, or what propertyTitle is used for; that text stays entirely in 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?

Uses a specific verb ('Calculates') and enumerates the exact outputs: acquisition tax, loan interest, all-in cost, equity ROE, and max bid cap, scoped to South Korean public asset sales. An agent can tell it is a computation tool rather than the data-retrieval siblings like query_kamco_public_auctions, though no sibling is named 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?

Usage is only implied: an agent must infer this is for evaluating a planned bid on a public auction. There is no statement of when to use this versus query_kamco_public_auctions or query_public_procurement_bids, and no prerequisites or exclusions are given.

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