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 the single most important trait: this is a non-mutating preview. It says nothing about permissions/auth, file size limits, or how malformed rows are handled, and the only structured signal is an 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 core behavior and the dry-run qualifier are front-loaded in a single compact sentence. The trailing keyword list is largely SEO filler that adds little, but it is short enough not to obscure the main statement.

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 output schema covers what a preview returns, and the description covers the non-mutating nature of the call. What is missing is any explanation of how the five default_* parameters are applied to file rows lacking those fields, which is essential context for interpreting a preview result on a 6-parameter tool.

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 none of the six parameters carry schema descriptions. The description only implies file_path semantics via the listed file extensions; the five optional defaults (default_floor, participation, default_purpose, default_visit_date, default_visit_time) — which materially change preview output — are completely unexplained in both schema and 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?

States a specific verb (preview), resource (visitor list), and input source (file), plus the supported formats (.xlsx/.csv/.tsv). It also negates the sibling action ('does not actually register'), so an agent can separate it from register_visitors_from_file and preview_visitors_from_text without opening a schema.

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 parenthetical '(실제 등록하지 않음)' gives clear context for when to reach for this tool: a dry run before committing a registration. It does not, however, explicitly name the alternative siblings (preview_visitors_from_text, register_visitors_from_file) or state exclusions, so it stops short of a 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?

No annotations are provided, so the description must carry the full behavioral burden. It does disclose the key trait of not registering ('등록 없이') and only returning parse/validation results, but it omits other behavioral details such as authentication, rate limits, or side effects beyond registration.

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 core description is concise and front-loaded, with the main action and usage in two sentences. The appended keyword list adds noise without helping an agent invoke the tool, keeping it from a perfect score.

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 6 parameters, no annotations, and 0% schema description coverage, the description is incomplete. It covers purpose and pre-registration usage, but leaves optional parameter semantics unexplained and does not clarify text format or relation to file-based preview tools.

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% for 6 parameters, so the description should compensate heavily. It only indicates that 'text' is the visitor list input; the five optional parameters (default_floor, participation, default_purpose, default_visit_date, default_visit_time) are not described at all.

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 (parse/validate) and resource (visitor list from text), and explicitly scopes it as a preview without registration. This clearly distinguishes it from sibling registration tools like register_visitors_from_text.

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 says to call this before registration ('등록 전에 먼저 호출하세요'), giving clear context. It does not name when-not-to-use or explicitly contrast with preview_visitors_from_file, so it falls short of a 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?

No annotations are provided, so the description carries the full burden. It does disclose one important behavioral trait — dry_run performs validation only and does not submit — but says nothing about permissions/auth, duplicate handling, whether the booking is idempotent, or what happens on failure. Partial coverage for a mutation 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?

Core behavior is front-loaded in the first sentence, with the dry_run caveat second. The trailing keyword block is redundant noise for an agent but may aid retrieval; overall still compact.

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?

An output schema exists so return values needn't be documented, but with 9 parameters (6 required) at 0% schema coverage and only dry_run explained, an agent lacks enough information to populate the remaining required fields correctly. Incomplete for a multi-param mutation tool with no annotations.

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% — all 9 parameters have only titles, no descriptions. The description explains only dry_run, leaving visitor_mobile format, visit_date/visit_time format, floor, and the visit_purpose default ('meeting') completely unspecified in both schema and 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?

States a specific verb+resource+scope: registering exactly one visitor reservation at the Centerfield building. The '1명' (one person) implicitly distinguishes it from the bulk siblings (register_visitors_from_text/file), though it never names them. Clear, but sibling differentiation is left to inference.

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 explains the dry_run=true branch (validate only, do not submit), which is genuine usage guidance. However, it gives no guidance on when to use this single-visitor tool versus register_visitors_from_text/file, nor any prerequisites such as who may file the reservation.

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. It does disclose the important mutation-control trait (dry_run=true means nothing is submitted) and the column-fallback rule, which is genuinely beyond the schema. It is silent on permissions, row-level validation/error behavior, duplicate handling, and whether partial failures roll 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?

Core behavior is front-loaded in the first sentence, with the date/time fallback and dry_run clause following in tight order. The trailing keyword block is long but functional for retrieval; little is wasted otherwise.

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?

An output schema exists, so return values need not be explained. For a 7-parameter bulk mutation tool with zero annotation coverage, though, the description should say more about expected file columns and what happens to invalid rows; three parameters and the failure model remain undocumented.

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 compensate. It explains default_visit_date/default_visit_time (and dry_run), roughly half the parameters, but leaves default_floor, default_purpose, and participation entirely undefined in both description and schema, so the agent cannot tell what values those accept or what they default to semantically.

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 (bulk-register visitors read from a file) and enumerates the accepted formats (Excel/CSV/TSV), which cleanly separates it from the single-visitor sibling register_visitor and from the text-based register_visitors_from_text. It never names the preview counterpart preview_visitors_from_file, so sibling routing is implied rather than explicit.

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?

Gives two concrete usage conditions: supply default_visit_date/default_visit_time when the file lacks date/time columns, and dry_run=true to avoid submission. However it never states when to prefer this over preview_visitors_from_file or register_visitors_from_text, and no prerequisites are mentioned.

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. It usefully discloses that dry_run=true suppresses submission and that this is a bulk mutation, but says nothing about auth requirements, duplicate handling, parse-failure behavior, or reversibility.

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 operative content is front-loaded and clear in the first three sentences, but the trailing 'Keywords:' block is search-optimization padding that adds little selection value for an agent and inflates the length.

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?

Output schema exists so return values need not be described, but for a 7-parameter, no-annotation mutation tool the description omits the critical pasted-text format (expected columns/delimiters) and any pointer to the preview sibling. Adequate but with clear 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 0%, so the description must compensate and it only partially does: it explains text, dry_run, default_visit_date and default_visit_time, leaving default_floor, participation, default_purpose and the expected text column format undocumented. The date/time-column constraint is a genuinely useful semantic addition.

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: bulk-registering visitors from pasted text ('텍스트... 방문자 목록을 입력받아 일괄 등록합니다'). This is distinguishable from the singular register_visitor and the file-based register_visitors_from_file, though it never names those siblings 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?

Gives one conditional: when the attendee list lacks date/time columns, set default_visit_date/default_visit_time. It also explains dry_run, but there is no guidance on when to prefer preview_visitors_from_text first or register_visitors_from_file instead. Usage is implied rather than framed against 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?

With no annotations, the description carries the full behavioral burden. It usefully discloses that the tool queries the live site and does not mutate state ('실제 사이트에 조회', '예약을 생성하지 않습니다'), effectively a read-only dry run. However it omits auth/permission requirements, whether it can fail on misconfiguration, and any side effects — gaps that matter for a validation tool with zero annotation coverage.

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 core sentences are front-loaded with the action and the non-mutation guarantee, with zero waste. The trailing 'Keywords:' line is somewhat redundant with the body text, mildly diluting the otherwise tight structure.

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?

An output schema exists, so return values need not be explained, and a zero-parameter validation tool with a clear no-side-effect statement is largely self-contained. The remaining omission is what failure looks like when the queried site settings do not match local config, which would help an agent interpret results.

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 takes no parameters, so per the scoring baseline this is a 4. The description correctly does not pad with parameter talk, though the enumerated settings fields are informational rather than inputs.

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 in Korean (조회해 확인합니다 = query and verify the Centerfield MCP settings), enumerates exactly what is validated (tenant company name, approval manager, floor list), and explicitly contrasts with siblings via '예약을 생성하지 않습니다' (does not create a reservation). An agent can distinguish this read/validation tool from the register_* siblings without consulting any schema.

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?

'dry run' and '연결 테스트' (connection test) signal the intended context clearly, and the explicit note that no reservation is created implicitly routes write intent to the sibling tools. It stops short of stating a prerequisite relationship (e.g. run this before register_visitor), so it is clear but not fully prescriptive.

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