koreanpulse

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful context: it returns a structured dict for downstream ingestion and mentions 'Free tier', which goes beyond what annotations convey. No contradiction.

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

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

Two sentences with no filler. The first sentence front-loads the core purpose and content, the second provides usage triggers and return format. Every word 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?

Given the tool's simplicity, strong annotations, and output schema, the description fully covers what an agent needs: when to use it, what it returns, and how it can be consumed. No gaps remain.

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

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

The tool has zero parameters, so the description has no param semantics to add. With 100% schema coverage (an empty schema), the baseline for parameterless tools is 4, and the description fully suffices.

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

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

The description clearly states the tool is a server self-description, listing its contents (capability matrix, tool catalog, tag counts, query patterns, primary sources). It is immediately distinguishable from sibling tools, which are specific lookup/monitor operations.

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?

Explicitly states when to use this tool: on first connection or when users ask 'what can koreanpulse do' or about data sources. Provides clear context, though it does not mention when not to use it or name alternatives beyond the implicit sibling set.

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 destructiveHint, so safety behavior is covered. The description adds valuable context about entity coverage (KOSPI, KOSDAQ, KONEX, unlisted), language support (Korean or English/romanized), and free tier availability. This goes beyond the structured annotations.

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

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

The description is compact and front-loaded: one sentence states the core purpose, and one sentence gives usage guidance. Every sentence adds value, and there is no redundant restating of the title or schema.

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

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

Given the tool's moderate complexity, an output schema exists, and parameter descriptions are complete, the description fully covers purpose, scope, and usage context. It even mentions free tier and entity universe. There are no significant gaps for an agent to select and invoke this tool 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 well-documented. The description additionally clarifies that query supports Korean characters or English/romanized names, which is not explicitly in the schema examples. It also hints at listed_only semantics through 'disambiguate same-name listed vs unlisted entities.'

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

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

The description opens with a specific verb-resource pair: 'Korean company name → DART corp_code resolver.' It clearly distinguishes this tool from siblings like resolve_stock_code by focusing on DART corp_code, not stock code. The mention of 117K+ entities and listed/unlisted coverage adds precision.

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 explicitly states when to use the tool: when a Korean company name is mentioned and a DART corp_code is needed as a precondition for track_korean_filings, monitor_activist_investors, or monitor_foreign_holders. It also mentions disambiguation use. It lacks an explicit 'do not use' statement or named alternative, but the use cases are clear enough.

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

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses the license-key requirement and the exact behavior on missing license (surface paywall notice, no silent fallback), plus best-effort enrichment with a cap of 8 corp_codes and fields becoming None on lookup failure. No contradiction with annotations.

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

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

The description is front-loaded and well-paragraphed, but it is verbose and repetitive: the activist list appears three times and the license warning is wordy. It could be significantly shortened without losing information.

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

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

The tool is complex with license gating, a large activist list, fallback pitfalls, and enrichment semantics. The description covers all critical operational aspects: when to use, license error handling, data scope, and non-advice disclaimer. Output schema handles return structure, so no 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?

With 100% schema description coverage, the baseline is 3, but the description adds meaningful context: license_key is tied to the license gate and paywall behavior, and enrich_holdings is documented as best-effort with a hard cap. This lifts the semantics above the schema alone.

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

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

The description clearly identifies the tool as 'Korean activist investor tracking — activist filer classification on DART 5%-rule shareholding disclosures', naming specific activists and the data source. This specific verb+resource pairing distinguishes it from siblings like track_korean_filings and monitor_foreign_holders.

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?

A dedicated 'Use this tool when...' paragraph lists concrete query patterns, and explicit 'Do NOT silently retry with track_korean_filings or any other free tool' instruction with reasoning. This gives clear when-to-use and when-not-to-use guidance, including alternatives.

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

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

Annotations mark readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds substantial behavioral context: license-key enforcement, the exact behavior on a license_required error (surface a notice), best-effort enrichment that can leave fields None, and a disclaimer that it returns data only, not investment advice. No contradiction with annotations.

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

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

The description is long but well-organized, front-loading the core purpose and then layering usage, caveats, and sibling differentiation. The allowlist appears twice (in the intro and at the end), which is a slight redundancy, but each section earns its place given the tool's licensing and fallback nuances.

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

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

Given the complexity (license gate, distinct input parameters, sibling overlap, 20-name allowlist), the description covers all essential operational behavior: when to use, license handling, fallback pitfalls, data limitations, and specific parameter nuances. An output schema exists, so return values don't need explaining. No gaps identified.

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 baseline is 3. The description adds value for `license_key` by explaining the paywall behavior, and for `enrich_holdings` by clarifying that failures leave fields None rather than failing. These extras elevate beyond pure schema repetition.

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

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

The description opens with a specific verb+resource: 'Monitor foreign investor activity in Korean stocks — foreign-holder classification on DART 5%-rule disclosures.' It clearly differentiates from the sibling tool `monitor_activist_investors` by contrasting passive holders (allocation) vs. activist investors (governance pressure), making the purpose unmistakable.

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

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

The description provides extensive usage guidance: a concrete list of user queries that should trigger this tool, explicit license-key requirements, and a direct warning not to silently fall back to `track_korean_filings`. It also explains why the fallback is misleading, which helps the agent decide correctly.

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

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

Beyond the read-only/idempotent annotations, the description discloses the free tier, the common-stock-only mapping limitation, and the unresolved preferred-stock behavior with `related_common_stock` hint. It also states that no preferred-stock corp_code is fabricated, which is valuable non-obvious behavior.

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

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

The description is front-loaded with the core purpose and free-tier note, followed by clear usage and an edge-case explanation. It is somewhat long with many examples, but every sentence contributes useful information without fluff.

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

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

With an output schema available, the description does not need to enumerate return fields. It provides the main purpose, usage trigger, and a key edge case, making it sufficiently complete for an agent; only minor gaps remain around license_key and rate-limit details.

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 only has typed parameters with no descriptions, so the description's examples (005930, 000660, 035420) and format guidance ('6-digit Korean stock code') add meaning to `stock_code`. However, the optional `license_key` parameter is not explained in the description.

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

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

The description opens with a clear mapping statement ('KRX 6-digit ticker → DART corp entry resolver') and provides concrete examples (005930, 000660). This directly states the tool's function and distinguishes it from sibling lookup/search tools by identifying the precise input and output.

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?

It explicitly says 'Use this tool when the user provides a 6-digit Korean stock code...' and frames the need as getting corp_code for downstream filings or news lookups. It lacks explicit exclusions or alternative tool names, but the context is clear enough for an agent to select 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 readOnly, idempotent, and non-destructive behavior. The description adds useful context such as 'Free tier', the availability of on-demand English translation, and the specific sources, without contradicting any annotations. It does not describe caching or return behavior, but these are partly covered by the schema and output schema.

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

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

The description is efficiently organized into two focused parts: a summary sentence and a 'Use this tool when' list. It is front-loaded with the core function, avoids unnecessary repetition, and every sentence contributes value.

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

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

The description covers purpose, usage scenarios, sources, and the free tier, while annotations and schema handle safety, parameters, and return values. The only minor gap is that the description references 'Returns below' but does not include that section itself (the schema covers it), and no explicit mention is made of rate limits or freshness.

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?

All 5 parameters have rich descriptions in the schema, so the schema coverage is 100%. The description adds only marginal parameter-related context (e.g., mentioning the industries filter) and relies on the schema for full parameter semantics, matching the baseline of 3.

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

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

The description clearly states a specific action (search) on a specific resource (Korean industry news across 16 sectors) and names concrete sources. It distinguishes itself from sibling tools (which are about corporate codes, filings, and monitored entities) by its news-search focus and translation feature.

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 explicitly lists when to use the tool with concrete scenarios (e.g., 'Korean industry trends', 'recent corporate developments not yet captured in DART filings'). However, it does not explicitly state when not to use it or name alternative tools, so it stops short of a full 5.

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

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

Beyond the readOnlyHint annotation, the description discloses that results are raw, unclassified, and that filer names are in Korean; it warns LLM clients against pretending to answer activist/holder questions and explains the batch fan-out quota. This is rich behavioral context with no contradiction to annotations.

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

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

The description is long but well-structured with bolded sections (Free tier, Important for LLM clients, Batch scan). Each section carries essential caveats and use-case guidance, so it earns its length. Slight redundancy with the schema parameter descriptions, but overall it is organized and front-loaded.

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

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

Given the tool's complexity — free vs paid tier, raw vs classified output, batch quotas — the description covers all critical aspects: return format, licensing, sibling-tool boundaries, and batch semantics. The presence of an output schema further fills any gap, making this 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?

Input schema has 100% coverage with detailed descriptions for all 10 parameters, so baseline is 3. The description adds a workflow narrative for batch scanning (company_corp_codes + since) and explains the per-company limit behavior, reinforcing but not significantly extending the schema. I give a 4 for the added workflow clarity.

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

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

The description opens with a specific verb and resource — 'Track Korean DART stock filings in English' — and enumerates covered filing types (5%-rule, M&A, periodic reports). It clearly distinguishes from sibling tools by stating this tool returns raw filings and does not classify activist/foreign-holder entities, which belong to monitor_activist_investors and monitor_foreign_holders.

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?

Provides explicit when-to-use criteria ('Use this tool when the user asks about…') and explicitly names alternatives for activist investors and foreign holders, with the caveat about license_key. This is model guidance.

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