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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about the content returned (structured dict with capability matrix) and its purpose for initial connection, enhancing transparency beyond 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 only two sentences, front-loaded with the key purpose and usage guidance. No unnecessary words, every sentence adds 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?

With no parameters and an output schema (not shown but referenced), the description is complete. It states what the tool returns (capability matrix, tool catalog, etc.) and when to use it, which is sufficient for an agent.

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 no parameters, and schema description coverage is 100% (since there are none). According to guidelines, 0 parameters baseline is 4. The description does not need to add parameter info.

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 explicitly states it is a 'Server self-description' providing 'capability matrix, tool catalog, classifier counts, supported query patterns, primary sources.' This clearly differentiates it from sibling tools which are specific data retrieval 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?

The description explicitly provides guidance: 'Use this tool when an agent first connects and needs the capability matrix... or when the user asks what can koreanpulse do'. This gives clear when-to-use instructions and distinguishes from alternatives.

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

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

Annotations already 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?

Adds key behavioral context beyond annotations: requires license key, returns paywall message without valid license, and instructs to surface activation URL. 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?

Description is verbose with extensive instructions on license handling and fallback behavior. While important, it could be more streamlined.

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 complexity (license gate, fallback behavior, specific filer list), the description is complete. Output schema exists, so return values need not be explained.

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% with all parameters well-described. Description adds no extra parameter information, meeting baseline for high coverage.

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

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

Description clearly states the tool monitors Korean activist investor disclosures on DART and tags specific named filers, distinguishing it from siblings like track_korean_filings which lack activist tagging.

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 tells when to use (e.g., user asks about Korean shareholder activism) and when not to (e.g., do not silently retry with track_korean_filings because activist match is not derivable from raw feed).

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 declare readOnlyHint true, destructiveHint false, idempotentHint true. The description adds critical behavioral context: requires a license key, returns a paywall message without a valid license, and does not generate trading advice. It also explains that the allowlist is not derivable from raw DART filings.

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 lengthy but well-structured with clear paragraphs. It front-loads the core purpose, then details usage, error handling, and distinctions. Some repetition exists (allowlist listed twice), but overall it is organized and informative.

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, the description covers all necessary aspects: purpose, use cases, parameter implications, error handling, and relationship to siblings. The presence of an output schema and clear annotations complement the description well.

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 the baseline is 3. The description does not add significant detail about parameters beyond what the schema provides, except emphasizing the `license_key` requirement. It confirms the purpose of each parameter indirectly.

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 monitors foreign 5%-rule holders on KOSPI/KOSDAQ and lists 20 specific entities. It distinguishes itself from the sibling `monitor_activist_investors` by explaining the difference in intent (allocation vs governance pressure).

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 explicit when-to-use scenarios (e.g., foreign capital flow, specific entity holdings) and when-not-to-use (e.g., not for activist). It also gives clear instructions on handling license errors and warns against using a fallback tool.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false. The description adds 'Free tier', which hints at rate limits or accessibility, providing extra context beyond annotations for a simple lookup 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 description is two sentences: the first concisely defines the tool, the second gives usage guidance with examples. It is front-loaded, every sentence adds value, and no words are 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 simple resolver with an output schema, the description covers the main purpose, usage context, and downstream applications. It does not detail error handling or rate limits, but overall it is sufficient for the tool's simplicity.

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

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

Schema description coverage is 0%, and the description does not explain the two parameters (stock_code and license_key). The example values for stock_code are helpful, but license_key is not mentioned, and no parameter details are added 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 clearly states the tool resolves a KRX 6-digit ticker to a DART corp entry, provides examples, and explains the output (company name + corp_code). It distinguishes itself from siblings like lookup_corp_code and search_korean_industry_news by specifying the purpose and downstream use.

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 says 'Use this tool when the user provides a 6-digit Korean stock code... and you need the company name + corp_code'. This gives clear context for when to use it, though it does not mention when not to use or list alternatives.

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

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

Annotations already indicate read-only, non-destructive, idempotent behavior. Description adds valuable context: free tier, aggressive caching for translations, and limit cap of 50.

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?

Concise paragraph front-loads core purpose and usage. Every sentence adds value—no redundancy or 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?

Given rich annotations and output schema, description covers all necessary aspects: when to use, parameters, sources, translation behavior, and industry list. No gaps.

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

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

Schema coverage is 100%, so baseline 3. Description adds meaning by clarifying default nulls, industry list (though schema already has it), source keys, limit max, and the condition for license_key.

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

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

Description clearly states it searches Korean industry news across 16 sectors with English translation. It names specific sources and explicitly lists query scenarios, distinguishing it from unrelated siblings.

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 guidance on when to use, listing example queries and industry tags. Does not mention when not to use, but siblings are distinct enough that exclusions are not critical.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds significant behavioral context: it returns raw filings without classification, does not require a license for free tier, and explicitly warns about paywall activation for paid tools. 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 lengthy (multiple paragraphs) and includes repetitive warnings. While front-loaded with purpose, it lacks conciseness. It is structured but could be more efficient.

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 (multiple parameters, sibling tools, and an output schema), the description is thorough. It covers purpose, usage, limitations, and links to other tools. Output schema exists, so return values are handled separately.

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% with all parameters described. The description does not add substantial parameter details beyond the schema, but it does provide high-level context about the license_key and free tier. 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 retrieves Korean DART filings for KOSPI/KOSDAQ/KONEX/KRX companies, listing specific filing types. It distinguishes from siblings like monitor_activist_investors and monitor_foreign_holders by explicitly stating when to use each.

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 provides usage scenarios ('Use this tool when...') and warns against using it for activist or foreign holder queries, directing users to alternative tools. It also explains that the free tier provides raw data without classification.

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