MyFSS

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

Annotations already mark it read-only and idempotent, and the description adds substantial behavior: truncated semantics, offset continuation, fallback from parsed HWP attachments to HTML body, exclusion of parsed=false attachments, unknown-id error format, and '(본문 없음)' placeholder. 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 organized into Purpose, Usage, Response, and Rules, with the most important scoping statement first. Every section earns its place; the labeled format and short example mappings make it easy to scan.

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, so the description must carry return-value information; it enumerates the response fields, explains truncated and error cases, and documents body fallback behavior. Given the tool's moderate complexity, nothing needed to call 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?

Even though schema coverage is 100%, the description enriches parameter meaning: offset_chars=60000 for continuing, max_chars=8000 for a short excerpt, and the rule that raising max_chars grows the whole response while offset_chars moves the window. This is actionable guidance 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?

States a specific action and resource: returns the full document body, with HWP attachments converted to markdown. The Purpose line explicitly frames it as the quoting/reading follow-up to a found document, distinguishing it from search_documents/list_recent.

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?

Contains clear context and usage examples ('그 자료 본문 보여줘' -> ntt_id), and says summary/snippet are never quoting grounds, implying get_document is for exact reading. It does not explicitly name alternative tools or state when not to use it, so it falls 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds meaningful behavioral context beyond them: no pagination, no total/offset, no snippet, ordering by publish date, and the subtle point that retroactively registered old documents never surface as new. 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 exceptionally well structured with [Purpose], [Usage], [Response], and [Rules] sections. Every section earns its place and important caveats are front-loaded before deeper behaviors.

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 has no output schema, the description supplies the response shape, the absence of pagination and snippet, sort behavior, and routing to search_documents for larger/narrower queries. This is complete enough for an agent to select and invoke the 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?

Although schema coverage is 100%, the description adds crucial semantics not present in the schema: board uses English keys while responses return Korean labels, and includes a warning not to feed the response board back as a parameter. The usage examples also clarify valid value mappings and the limit default.

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: returns the recent document list sorted by publish date descending. The [Purpose] section explicitly frames it as a query-less skim, and contrasts it with search_documents, making differentiation clear.

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 concrete natural-language usage examples that map directly to parameter values, and explicitly says to use search_documents for period/topic narrowing or larger volumes. This gives the agent both when-to-use and when-not-to-use guidance.

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

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint), so the description carries a lighter burden and far exceeds it. It adds genuinely non-obvious behaviors: exact-match vs partial-match semantics for different fields, '0 rows, not an error' for invented values, boards[].key vs label distinction, and the top-25 truncation of depts_top25.

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 organized into [Purpose], [Usage], [Response], and [Rules] sections, with the core purpose front-loaded in the first sentence. Every sentence earns its place — matching rules, response shape, and caveats are each stated once with no repetition or filler.

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

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

For a zero-arg, read-only tool with no output schema, the description carries full responsibility for return-value documentation and handles it completely: full response shape, meaning of n, key/label distinction, matching behavior, and edge cases (missing dept, empty results). No information an agent needs 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?

With zero parameters, the baseline is 4, and the description exceeds it substantially. The [Response] and [Rules] sections document the semantic meaning of every returned value field — the exact n count, the key/label contract for the board parameter, the partial-match rule for dept, and the open-world caveat that absent depts still exist. This effectively documents the vocabulary contract that search_documents' parameters depend on.

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

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

The opening line names a specific verb (반환합니다/returns) and a specific resource (search-filter vocabulary: 주제 태그·문서 성격·게시판·주요 담당부서). The [Purpose] section cements differentiation by tying the tool to search_documents' exact fields (topics·nature·board·dept), making it clearly distinguishable from siblings like get_document and list_recent.

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 gives explicit when-to-use guidance: 'check here before a futile search' because invented values return 0 rows rather than an error. It also names the consuming sibling (search_documents). It stops short of a 5 because it never states when not to use the tool (no explicit exclusions of 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?

The description goes far beyond annotations: it discloses the board key/label asymmetry, that topics/depts are joined strings requiring split, the FTS-vs-LIKE token length rule, no-query descending date ordering, corpus coverage from 2010, and snippet-only-on-match behavior. This is rich, non-obvious behavioral detail.

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 into Purpose, Usage, Response, and Rules sections. Every line carries necessary information for a 10-parameter search tool, and front-loading the purpose makes it scannable despite the density.

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 fully documents the response shape, pagination via total/offset, source labels, snippet conditions, and the get_document fallback for body content. It also addresses search semantics, board asymmetry, and corpus boundaries, leaving no significant calling 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?

Although schema coverage is 100%, the description adds substantial meaning: query-to-parameter examples, the board English-key/한글-label mismatch, filter-only sweeping, and the no-query behavior. It clarifies how to combine parameters effectively, which the schema alone does not convey.

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 performs full-text search over FSS/FSC accounting and disclosure materials, and it enumerates the board categories and example queries. However, it never explicitly distinguishes itself from sibling tools like list_recent or list_tags; the differentiation is only implied by the term '전문검색' and a later rule mentioning get_document.

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 [Usage] section provides three concrete natural-language-to-parameter mappings and a rule directing users to get_document for quote bodies. It lacks explicit when-not-to-use guidance for the sibling list tools, though the examples give clear context for typical search scenarios.

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