법마디(Lawmadi) OS — Korean Legal AI
Server Details
Korean Legal AI — 60 agents, real-time statute verification via law.go.kr. Free 2/day.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 6 of 6 tools scored.
Each tool has a clear, distinct purpose. Ask and ask_expert differ in depth, chat_leader targets a specific leader, get_leaders lists them, search handles statutes, and suggest_questions generates follow-ups. No overlap.
All tool names follow a consistent verb_noun snake_case pattern (e.g., chat_leader, get_leaders, suggest_questions). The naming is predictable and uniform.
With 6 tools, the server is well-scoped for a Korean legal AI assistant. It covers asking, expert analysis, specific chat, leader listing, search, and suggestion generation—neither too few nor too many.
The toolset provides a complete workflow: identifying experts (get_leaders), asking questions (ask, ask_expert), chatting with specific experts (chat_leader), searching legal info (search), and generating follow-ups (suggest_questions). No obvious gaps for an end-user legal assistant.
Available Tools
6 toolsaskAInspect
Ask a Korean legal question. Routes to 1 of 60 specialist AI legal leaders and returns a statute-verified analysis (real-time law.go.kr check). Supports Korean and English.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Response language. Auto-detected if omitted. | |
| query | Yes | Legal question in Korean or English (max 2000 chars). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden. It discloses routing to a specialist, real-time law.go.kr verification, and support for Korean and English. It does not mention side effects, but the tool appears read-only; transparency is higher than minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. Front-loaded with core purpose, followed by key detail. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 2 simple parameters, the description is sufficient. It explains the input (legal question) and output (statute-verified analysis), including routing and verification. Could mention response format or error handling, but not essential for basic use.
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% and both parameters are described in the schema. The description adds no new information beyond what the schema provides (e.g., 'max 2000 chars' is in schema, language support is mirrored). Baseline of 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 the tool's purpose: 'Ask a Korean legal question' and explains it routes to 1 of 60 specialist AI legal leaders for a statute-verified analysis. It differentiates from siblings like 'ask_expert' and 'search' by specifying the legal domain and real-time law check.
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 for Korean legal questions and mentions language support. It does not explicitly state when not to use it or suggest alternatives among siblings, but the context is clear enough for an agent to distinguish.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ask_expertAInspect
Expert mode legal analysis — runs the full verification pipeline for a more detailed, deeply-cited answer. Slower than ask.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Response language. Auto-detected if omitted. | |
| query | Yes | Legal question in Korean or English (max 2000 chars). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses slower speed, full verification pipeline, and deep citations. For a query tool, this adequately covers behavioral traits beyond structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with essential information front-loaded. No wasted words.
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?
No output schema, and description does not specify return format or pagination. However, it mentions 'deeply-cited answer' which hints at structure. Adequate but not fully complete for a non-trivial 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?
Input schema has 100% coverage for both parameters. Description does not add additional meaning beyond what is already in the schema descriptions. 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?
Description clearly states 'Expert mode legal analysis — runs the full verification pipeline for a more detailed, deeply-cited answer.' and contrasts with sibling 'ask' by noting it is slower. Verb+resource is specific.
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?
States it is 'Slower than `ask`,' which implies use when detail is needed and speed is less critical. Provides context but does not explicitly list when not to use or other alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
chat_leaderAInspect
Chat 1:1 with a specific legal leader. Provide the leader_id (e.g. 'L01', 'L32', 'CCO') from get_leaders.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Question to ask the leader (max 2000 chars). | |
| leader_id | Yes | Leader identifier (e.g. 'L01'). See get_leaders. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the chat is stateless, read-only, or has side effects. It only implies a conversational interaction, missing important safety and behavior cues.
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, concise sentence that immediately states the tool's purpose and essential usage instruction. No filler or redundant 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?
For a simple chat tool with two parameters and no output schema, the description provides the core usage but omits details about response format, session handling, or constraints. The presence of sibling tools like ask_expert suggests more context could help differentiate, but the description is minimally adequate.
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 covers both parameters (100% coverage), so baseline is 3. The description adds extra examples for leader_id (e.g., 'CCO'), which provides more guidance than the schema's single example 'L01'. Query is not further elaborated.
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's for 1:1 chat with a specific legal leader, distinguishes from siblings by referencing get_leaders and using leader_id. It provides a specific verb and resource, which differentiates it from general ask tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs to provide leader_id from get_leaders, indicating a prerequisite. However, it does not specify when not to use this tool compared to alternatives like ask or ask_expert, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_leadersAInspect
List all 60+ specialist legal leaders with their names and specialties.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It accurately describes the read operation and output fields, but does not mention authentication, rate limits, or other behavioral traits beyond the implied read.
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 efficient sentence that front-loads the key information without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema), the description is mostly complete by specifying the content (names and specialties) and scope (all 60+ leaders). However, it does not explicitly state the return format, though this is implied.
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 input schema has 0 parameters with 100% coverage, so the baseline is 4. The description adds no additional parameter meaning, but none is needed.
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 lists all 60+ specialist legal leaders with their names and specialties, using a specific verb and resource. It is distinguishable from sibling tools like 'search' or 'chat_leader'.
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 use for getting a complete list, but provides no explicit guidance on when to use it versus alternatives such as 'search' for specific leaders or 'chat_leader' for discussions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspect
Search Korean statutes, precedents and legal terms via law.go.kr. Returns matching law topics.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query (min 2 chars). Example: '근로기준법'. | |
| limit | No | Max results (1-100, default 10). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It states it returns matching law topics but lacks details on authentication, pagination, or search behavior (e.g., full-text vs exact match). Adequate but not thorough.
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 concise sentences: first describes action and source, second describes return type. No redundant or extraneous 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?
Simple tool with two parameters and no output schema; description adequately covers what it does and returns. Lacks details like language support or result format, but sufficient for basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in schema. The description adds no further context beyond 'returns matching law topics', offering no additional meaning beyond what schema already provides.
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 verb 'search', the resources (Korean statutes, precedents, legal terms), and the source (law.go.kr), distinguishing it from sibling tools like 'ask' or 'ask_expert' which are likely Q&A or expert consultation tools.
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?
No explicit guidance on when to use this tool versus alternatives. Sibling tools exist but no comparison or when-not-to-use advice is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_questionsAInspect
Generate relevant follow-up legal questions for a given query.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Current user question (max 500 chars). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the basic action without disclosing any behavioral traits such as return format, authentication needs, or limitations. This is insufficient for a tool with no 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 a single sentence with no redundancy. It is concise and front-loaded, effectively conveying the tool's purpose without unnecessary words.
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 tool with one required parameter and no output schema, the description is minimally complete. However, it does not explain the output format (e.g., list of questions) or any error handling, leaving some 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 description coverage is 100% (parameter 'query' is described). The description adds general context but does not provide additional meaning beyond the schema, achieving baseline score.
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 'generate relevant follow-up legal questions' which precisely describes the tool's action and resource. It distinguishes from sibling tools like 'ask' or 'search' by focusing on generating questions rather than answering or retrieving.
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 for generating follow-up questions based on a query, but it does not explicitly state when to use this tool versus alternatives like 'ask' or 'search'. No exclusions or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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