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

Annotations only supply readOnlyHint=false, but the description adds substantial behavioral context: no arbitrary SQL is executed, no migrations or H2 files are written, menus and dictionaries are submitted separately and may partially succeed, and there is no cross-cutting rollback. These are exactly the operational traits an agent needs for a mutating 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 purpose and the apply gating are front-loaded, and the following sentences each carry a distinct constraint (no raw SQL, no migrations, partial success, no rollback). It is dense but nearly every clause earns its place; slightly clipped phrasing is the only cost.

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-value semantics need not be restated, and the description compensates by noting per-resource results and lack of rollback. However, for a 13-parameter mutation tool the absence of any per-parameter semantics leaves a real gap the sibling reference only partly fills.

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% across 13 parameters, so the description must carry the parameter burden. It offers only a pointer that the same named parameters as generate_codegen_sql are used, giving no meaning, format, or defaults for any individual field, which is a significant shortfall.

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: regenerate the plan and explicitly apply menu/derived-dictionary entries to the configuration database. It names the sibling generate_codegen_sql and distinguishes itself by being the 'apply' side rather than the generator, so an agent can separate the two without opening either 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?

Gives a concrete prerequisite (codegen.apply_to_database=true) and a scope condition (only the mode=auto portion is applied), which tells the agent when the call will actually act. It does not spell out an explicit 'use X instead' decision path against the other siblings, 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?

注释已提供 readOnlyHint=true，描述中进一步强调'只读'、'不写文件、不连接数据库'，增强了安全语义。还披露了扫描行为（只向下扫描）和默认深度限制，但未提及返回结果的格式或具体字段（如告警的细节），考虑到有输出 schema，描述已覆盖关键行为，评 4 分。

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?

描述仅两句话，第一句概括用途和副作用（只读、不写文件），第二句给出参数约束和使用建议。信息密度高，无冗余，关键约束前置，结构紧凑。

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?

工具操作简单（2 参数），有输出 schema，描述已涵盖目的、副作用、参数约束和使用时机。唯一缺失的是返回内容的具体结构，但输出 schema 已承担该职责，整体完整性足够。

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 描述覆盖率为 0%，但描述对两个参数都做了补充说明：search_root 必须是绝对路径且指向项目目录，max_depth 范围 0-6、默认 3。这些信息弥补了 schema 的不足，虽然未给出完整示例，但语义已足够清晰，评 4 分。

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?

描述明确说明这是只读发现工作区的工具，返回已有配置、后端与前端候选、扫描告警，并明确不写文件、不连接数据库。与兄弟工具（如 load_workspace_config、init_workspace_config）区分清晰，动词是'发现'，资源是'工作区'，具有高度特异性。

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?

描述明确说明使用场景：在主目录中确认目标后，再调用其他工具。并给出参数使用条件：search_root 必须是包含项目目录的绝对路径；max_depth 取 0-6，默认 3。还隐含了与其他工具（如 validate_workspace_projects）的衔接关系，提供了清晰的何时使用与后续步骤。

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 only supply readOnlyHint=true, and the description adds meaningful behavioral context beyond that: no file writes, no SQL generation, no DB writes, no preview directory creation, and the important nuance that generated_files is written by the AI while error-code/dict increments are returned as merge operations rather than full files. This is substantive behavioral disclosure.

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?

Information is front-loaded with the read-only constraint and output shape before parameter details, and each sentence conveys a distinct fact. It is dense but not padded, though the mix of behavioral notes and parameter notes could be organized more cleanly.

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, yet the description still usefully characterizes the output (generated_files vs merge_operations). For a 13-parameter mutation-adjacent generator, the coverage of key behaviors and tricky parameters is solid, with only the many self-evident-named parameters left implicit.

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 13 parameters, so the description must compensate. It does so well for the non-obvious ones (field_overrides including the full html_type enum list, backend_module_dir with both 'yudao-module-a/b' and shorthand 'a/b' forms, backend_package_module fallback to module_name), but ~10 parameters (table_name, menu_name, entity_name, module_name, business_name, parent_menu_id, workspace_root, include_*, parent_menu_name) remain undocumented beyond their names.

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 ('只读生成前后端代码内容与目标路径') and immediately differentiates itself from siblings by negation: it does not write files, does not generate SQL (contrast generate_codegen_sql), and does not write to the DB. An agent can tell it apart from generate_codegen_sql/apply_codegen_sql without opening 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?

The description implies usage through its read-only framing and mentions that generated_files are meant to be written by the AI and that merge_operations points to dedicated merge tools (siblings merge_backend_error_codes/merge_frontend_dict_types). However, it never states an explicit when-to-use rule, sequence (e.g. before generate_codegen_sql), or exclusion criteria, so guidance is only implied.

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, but the description adds substantial context: even if codegen.apply_to_database=true the tool will not apply to the database, and SQL files are written by the AI rather than by this tool. This clarifies destructive boundaries beyond the annotation bar.

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?

Purpose and safety constraints are front-loaded and the prose is dense with no filler. The trailing parameter notes read as appended patches rather than a structured parameter section, slightly hurting flow.

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 present, return values need no explanation, and the read-only safety model is well covered. However, for a 13-parameter tool the undocumented majority of inputs leaves an agent without enough guidance to set most arguments 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 description coverage is 0% across 13 parameters, so the description carries the full burden. Only backend_module_dir and backend_package_module are explained (with accepted format variants), leaving the other 11 parameters undocumented anywhere.

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 (generate) and precise resources (menu, derived dictionary, H2 SQL content and paths) scoped to a named business table. It also distinguishes itself from apply_codegen_sql, which actually applies menu/dictionary changes, so an agent can tell the two apart without opening either 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?

Explicitly defines when and when-not: it does not write files or the DB, and real menu/dictionary application requires explicitly calling apply_codegen_sql. It also specifies mode behavior (auto/migration_only generate, disabled does not), leaving nothing to inference.

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=false`, so the tool is known to mutate state. The description adds transparency by stating it creates a config file, that `overwrite=true` rebuilds existing config, and that in ambiguous cases it returns `selection_required` and writes no file. This goes beyond the annotation and covers important behavioral traits.

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 dense paragraph that front-loads the main action and then explains parameter constraints and fallback behavior. Every sentence adds value, and it is not overly long. It could be broken into bullet points for clarity, but it is appropriately concise.

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 an output schema (indicated by context), the description does not need to detail return values. It covers the core behavior, parameter relationships, and the edge case of ambiguity. It could mention prerequisites like existing workspace structure, but for a config-init tool it is fairly 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?

Schema description coverage is 0%, so the description must compensate. It explains `backend_path` and `frontend_projects` relationship and the meaning of an empty array, and partially explains `overwrite`. However, it does not describe `workspace_root` or the internal structure of `frontend_projects` items (type/path), leaving some parameters under-explained.

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 identifies workspace projects and creates `.yudao-pilot/config.yaml`, with a specific verb (identify/create) and resource. It also explains the `overwrite` behavior. However, it does not explicitly differentiate from sibling tools like `load_workspace_config`, though the name 'init' vs 'load' makes it clear. It is specific and unambiguous.

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 concrete usage rules: `backend_path` and `frontend_projects` must be provided together or omitted, and `frontend_projects=[]` means backend-only. It also explains the auto-detection fallback and the `selection_required` case when ambiguity exists. This gives clear guidance on how to invoke the tool, though it does not explicitly state when to use this tool versus 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 declare readOnlyHint=true, so the agent knows it's a safe read operation. The description adds that it combines multiple data sources, which is useful, but doesn't disclose potential rate limits, auth needs, or what the output context contains (though output schema exists). With annotations covering safety, this is adequate but not rich.

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, but the first sentence is in Chinese while the rest is in English, creating linguistic inconsistency. It's front-loaded with the main purpose, but the parameter explanations could be more structured. It's adequately concise but not optimally organized.

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 an output schema and readOnly annotation, the description doesn't need to explain return values or safety. However, with 10 parameters and 0% schema coverage, the description should document more parameters to be complete. It covers two parameters but leaves eight undocumented, which is a significant gap for a tool with many inputs.

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 should compensate. It explains two parameters (backend_module_dir and backend_package_module) with format examples and fallback behavior, which is helpful. However, 8 other parameters (menu_name, table_name, entity_name, module_name, business_name, parent_menu_id, workspace_root, parent_menu_name) are undocumented in both schema and description. This partial coverage means the description adds value but doesn't fully address the gap.

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 states the tool builds codegen context from config rules, backend defaults, and SQL menu data, which is a specific verb+resource. However, it doesn't differentiate itself from sibling tools like generate_codegen_scaffold or inspect_table_schema, and the purpose is somewhat vague about what 'context' entails. It's clear enough for a technical user but lacks sibling routing.

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 explicit guidance on when to use this tool versus alternatives. It doesn't mention prerequisites like requiring workspace configuration or table schema inspection first, nor does it exclude any scenarios. The agent must infer usage from the context of sibling tools.

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 readOnlyHint annotation already establishes that this is a non-mutating inspection, so the safety profile is covered. The description does add real behavioral context beyond the annotation: detection is fingerprint-based on pom.xml/package.json rather than directory naming. It still says nothing about failure modes (e.g., unknown project type) or how confident/partial results are presented.

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 with no filler; the analytical method leads and the non-reliance-on-name caveat follows. Nothing is redundant, though it is terse to the point of leaving the parameter format unstated.

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 the read-only annotation covers safety. What remains missing is the expected form of project_path and any note about behavior on unrecognized projects, which for a one-parameter inspection tool is a modest but real 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?

Schema description coverage is 0% for the single required parameter project_path, so the description carries the burden. It implicitly frames the input as a project location rather than a name ('does not rely on the directory name'), but it never specifies whether the value is a directory path, a file path, or whether it must be absolute.

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 gives a concrete verb (分析/analyze) and a concrete target (项目类型/project type), plus the mechanism used (dependency fingerprints in pom.xml or package.json). That is enough to distinguish it from siblings like validate_workspace_projects or inspect_table_schema, though it never names which sibling it complements or replaces.

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 tool versus alternatives such as validate_workspace_projects or load_workspace_config. The only guidance-like content is the methodological note that analysis does not depend on directory names, which is a caveat rather than a when-to-use rule.

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?

readOnlyHint=true already covers safety, and '只读' merely repeats it. The description does add genuinely new behavioral context: the real database must recognize the target table, and local SQL is diagnostic-only. It still omits what happens on failure or whether results are cached, so a 3 is appropriate.

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 sentence that front-loads the core action (read-only single-table inspection) before the constraints. It is efficient, though the dense punctuation makes the two clauses blur together slightly.

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 described, but with 0% parameter coverage the description should compensate for undocumented inputs — it does not explain workspace_root at all. For a two-parameter tool, this leaves a material 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?

Schema description coverage is 0% and neither of the two parameters is documented. The description only hints at 'single table' (implying table_name) but says nothing about workspace_root, its default null, or path resolution — leaving the uncovered schema to carry meaning the description never supplies.

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 verb+resource in '只读检查单表字段' (read-only inspection of a single table's fields), which clearly separates it from project/workspace-level siblings like inspect_project_path or validate_workspace_projects. It is clear but does not explicitly route to any alternative tool.

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 implies usage context via '真实数据库必须识别到目标表，本地 SQL 仅作诊断参考' — i.e. the tool expects a real, resolvable database and treats local SQL as diagnostic only. However, it never states when to choose this over resolve_database_config or inspect_codegen_context, so the guidance is only implied.

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?

readOnlyHint=true already tells the agent this is a non-mutating read, and the description partly restates that (只读, 不创建文件). Its real added value is disclosing the failure mode (returns config_missing rather than creating anything), which annotations do not convey; beyond that it says nothing about permissions, resolution order, or partial configs.

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 clause, front-loaded with the read-only character and the missing-file behavior; no filler. Every phrase carries 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?

An output schema exists, so return-value details are covered elsewhere, and the missing-config behavior is stated. What is missing is any treatment of workspace_root semantics and the relationship to init_workspace_config, which matters even for a one-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 the single parameter workspace_root has no description, default documented only as null. With low coverage the description is expected to compensate, but it never mentions workspace_root, what null/default means, or how the root is resolved — so the parameter remains ambiguous.

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 states a specific verb (只读加载/read-only load) and resource (工作区配置/workspace config), and implicitly distinguishes itself from the sibling init_workspace_config by stressing it does not create files. It stops short of naming that sibling, so the differentiation is inferable 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?

It gives one concrete usage-relevant condition — when the config is missing the tool returns config_missing instead of provisioning — which implies init_workspace_config is the alternative for that case. However, it never explicitly says 'use this to read, use init_workspace_config to create', leaving the routing to inference.

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 only readOnlyHint=false in annotations, the description fills the gap substantially: it states it modifies only the target file and ServiceErrorCodeRange.java, does not generate ordinary code or delete placeholders, returns changed_files, and produces an empty list for duplicate input. This level of detail on scope, side effects, and idempotency goes well beyond what annotations provide.

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 appropriately sized and front-loaded, opening with the core purpose before parameter details and behavioral constraints. Every sentence adds information and there is no wasted text.

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 write tool with four required parameters, nested constants, and an output schema, the description covers purpose, behavior, and most parameters. It misses the meaning of workspace_root and any prerequisite such as ensuring the backend workspace is configured, which leaves a small completeness 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?

Schema description coverage is 0% at the top level, so the description must carry the burden. It explains relative_path (path to ErrorCodeConstants.java relative to the configured backend repo), constants (non-empty {name, message} list, auto-numbering, same-name update behavior), and section_title (business group title), but leaves workspace_root completely undocumented.

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 states a specific verb+resource: '合并后端错误码' (merge backend error codes), and clarifies the target file via relative_path. It distinguishes itself from the frontend sibling implicitly by the 'backend' domain, but never names an alternative tool to make the contrast 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?

The description explains what the tool does but gives no guidance on when to use it versus alternatives such as merge_frontend_dict_types or the codegen tools. Constraints like 'not generating ordinary code' describe behavior rather than helping an agent select this 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 only declare readOnlyHint=false; the description carries the real behavioral burden. It discloses exactly what changes (only the DICT_TYPE block plus member commas), what is preserved (other content), idempotency (same name+same value is skipped), conflict handling (name or value mismatch errors), and that it will not create files or enums.

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?

Front-loaded purpose sentence followed by tightly packed parameter and behavior notes; nearly every clause earns its place. Slightly dense but no 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?

An output schema exists so return values need not be described. For a mutation tool the description covers what is written, preserved, skipped, and errored, plus the no-file-creation constraint. Only the workspace_root parameter and any permission/auth requirements are unaddressed.

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?

Top-level schema coverage is 0%, so the description must compensate, and it does: target_type uniquely matches a configured frontend, relative_path is project-relative and may include subdirs like apps/web-antd, and constants is a non-empty {name, value, comment?} list with skip/conflict behavior. It does not disambiguate workspace_root, leaving one param unexplained.

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: incrementally merge constants into the DICT_TYPE of src/utils/dict.ts, with the explicit negative scope 'does not create a database dictionary.' An agent can distinguish this from merge_backend_error_codes (frontend dict vs backend error codes) without opening the 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?

Usage is implied by the purpose statement, but there is no explicit when-to-use/when-not against the sibling tools (merge_backend_error_codes, apply_codegen_sql, generate_codegen_scaffold). It notes the target file must already exist ('does not create missing files/enums'), which is a useful precondition, but alternative routing is left to inference.

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 readOnlyHint=true already declaring safety, the description still adds genuine behavioral context beyond annotations: the tool resolves config from two sources with an explicit fallback order (workspace config, then backend local config). It does not describe failure behavior when neither source exists, but the dual-source precedence is real value.

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 sentence that front-loads the action and then the priority order. Nothing wasted, though there is no additional clarifying detail that could have been added cheaply.

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 no explanation, and annotations cover safety. But for a tool whose entire purpose is resolution logic, the description leaves key questions open: what happens when workspace_root is null, how conflicts between the two sources are merged, and what 'backend local config' covers.

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?

There is one parameter (workspace_root) at 0% schema description coverage, and the description never mentions it. The agent must guess what passing null/default versus a path does, and whether that path drives the 'workspace config' lookup. With low coverage the description should compensate but does not.

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 verb (解析/resolve) and resource (数据库配置), and adds the resolution precedence (workspace config first, backend local config second). This is clear, but it does not differentiate the tool from siblings like load_workspace_config or init_workspace_config that also deal with config.

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 explains the internal source precedence, which implies it is a resolution/lookup step, but it never states when an agent should call this instead of load_workspace_config, init_workspace_config, or the inspect_* tools. Usage is only inferable.

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?

readOnlyHint=true already tells the agent this is a safe read operation, so the description carries a lighter burden. It adds the useful 'strict match' semantic, but leaves open the key behavioral question for a validator: whether a mismatch raises an error or is returned as a report, and whether validation is recursive over all projects.

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 — the verb and scope come first. It is efficient, though its brevity shades into under-specification rather than disciplined conciseness.

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 structure need not be explained. What is missing is what exactly counts as a 'strict match,' what happens on failure, and how workspace_root defaults — gaps that matter for a validation tool an agent must decide to invoke.

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 single workspace_root parameter has 0% schema description coverage and is undocumented in the description beyond the vague phrase 'in the workspace.' The default of null and any path syntax or resolution behavior are never explained, so the description does not compensate for the schema gap.

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 gives a specific verb plus resource pair — validate backend and frontend projects in the workspace against the configuration — and adds the qualifier 'strictly matches.' That is enough for an agent to know what the tool does. It does not, however, distinguish itself from close siblings such as load_workspace_config or inspect_project_path.

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 guidance, no stated prerequisites, and no mention of alternatives among the many sibling config/inspect tools. An agent can only infer that this is a preflight check, not when it should be preferred over loading the config directly.

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