kothar
kothar
문맥 인식 MCP 서버 어드바이저. 특정 프로젝트에 무엇을 설치해야 하는지, 그리고 그 이유를 알려줍니다.
문제점
Glama에는 19,000개 이상의 MCP 서버가 있습니다. 당신에게는 프로젝트가 있습니다. 하지만 이 둘을 연결해 주는 것이 없습니다.
LLM에게 직접 물어보면 존재하지 않는 서버를 환각으로 만들어내거나 오래된 학습 데이터를 기반으로 추천합니다. 디렉토리는 검색 기능만 제공할 뿐 조언을 해주지는 않습니다.
kothar는 문맥 기반 선택의 공백을 메워줍니다. "여기 19,000개의 옵션이 있습니다"가 아니라 "지금 당신의 특정 프로젝트에는 이것이 필요하며, 그 이유는 다음과 같습니다"라고 알려줍니다.
아무도 해결해주지 않는 두 가지 순간
프로젝트 시작: "DuckDB와 FastAPI를 사용하여 Python 데이터 파이프라인을 구축 중이야" → 지금 당장 무엇을 설치해야 할까
프로젝트 진행 중: "방금 인증 계층을 추가했어 / PDF 수집 기능을 처리해야 해" → 이 시점에 무엇을 추가해야 할까
두 번째 순간이 더 가치 있습니다. 프로젝트 시작 시에는 사람들이 구글링을 할 수 있지만, 진행 중일 때는 작업 흐름(flow) 속에 있기 때문입니다.
세 가지 도구
recommend_for_project(description)
→ top MCP servers for your stack with rationale
recommend_next(current_stack, new_context)
→ what to add as your project evolves
explain_why(server_name, project_description)
→ why a specific server fits your project설치
전제 조건: uv
git clone https://github.com/yahiaklk/kothar
cd kothar
uv sync인덱스 빌드 (첫 실행 시, 약 30초 소요):
uv run python -m kothar.indexerClaude Code에 추가
claude mcp add --scope user kothar -- uv run --directory /path/to/kothar python -m kothar.serverClaude Desktop에 추가
claude_desktop_config.json에 추가하세요:
{
"mcpServers": {
"kothar": {
"command": "uv",
"args": ["run", "--directory", "/path/to/kothar", "python", "-m", "kothar.server"]
}
}
}사용법
연결이 완료되면 AI 어시스턴트에게 다음과 같이 물어보세요:
recommend_for_project("Python FastAPI backend with PostgreSQL and JWT auth")
recommend_next("github,filesystem", "adding Stripe payments and PDF invoices")
explain_why("postgres", "multi-tenant SaaS with row-level security")작동 원리
awesome-mcp-servers (2,000개 이상의 큐레이팅된 서버) 파싱
all-MiniLM-L6-v2를 사용하여 설명 임베딩 (로컬 실행, API 비용 없음)DuckDB에 저장하고 코사인 유사도로 쿼리
템플릿 기반 근거 제시 — 환각이 아닌 레지스트리에 기반함
인덱스 재빌드
uv run python -m kothar.indexer --forceDocker
임베딩 모델과 DuckDB 인덱스가 포함된 멀티 스테이지 이미지 — 런타임 네트워크 의존성 없음.
docker build -t kothar:0.3.0 .
docker run --rm -i kothar:0.3.0 # stdio transport, for local MCP clientsClaude Desktop에 연결:
{
"mcpServers": {
"kothar": {
"command": "docker",
"args": ["run", "--rm", "-i", "kothar:0.3.0"]
}
}
}루트가 아닌 사용자 (uid=10001), 고정된 Python 3.12, uv.lock에서 해결된 종속성, 런타임 시 HF_HUB_OFFLINE=1로 /app/.hf_cache에 캐시된 모델 사용.
스택
Python · FastMCP · DuckDB · sentence-transformers · uv
라이선스
Available Tools
4 toolsexplain_whyB
Explain why a specific MCP server is a good fit for a given project. Example: server_name="github", project_description="open source Python library with CI/CD"
| Name | Required | Description | Default |
|---|---|---|---|
| server_name | Yes | ||
| project_description | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations and description only states purpose. Does not disclose behavior like error handling, required permissions, or output format beyond what output schema might 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?
Description is a single sentence plus example, which is concise. However, the structure could be improved by adding a brief usage context.
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 low complexity and presence of output schema, description is minimally adequate but lacks usage guidelines and behavioral details that would make it fully self-contained.
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%, meaning parameters have no descriptions. The description provides an example usage but does not explain parameter semantics beyond the example.
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?
Describes a clear verb+resource combination: 'Explain why a specific MCP server is a good fit for a given project.' This distinguishes it from sibling recommendation 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?
Provides an example but no explicit guidance on when to use this tool versus the sibling recommendation tools. The example helps but does not fully clarify context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_for_goalA
Decompose a multi-part goal into sub-queries and recommend MCP servers for each part. Splits on hard boundaries: '. ', '; ', ' then ', ', then ', ' and then ' (not bare ' and '). project: optional project context prepended to each sub-query for richer semantic matching. Example: goal="integrate GitHub. add Stripe payments", project="Python FastAPI backend"
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| project | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries the burden. It explains splitting behavior and project prepending but does not disclose whether the tool is read-only or destructive, side effects, or error handling. Adequate but lacks safety profile.
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?
Three sentences plus example, no wasted words. Front-loaded main purpose, then splitting details, then optional parameter. Could be more structured but efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return values not required. Explains input and behavior well, but lacks usage guidelines for sibling differentiation and does not mention prerequisites or error conditions. Fairly complete for a tool with output schema.
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%, yet the description adds meaning: 'goal' is a multi-part goal with splitting rules, 'project' is optional context prepended. This goes beyond the schema's minimal type info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool decomposes multi-part goals into sub-queries and recommends MCP servers. Distinguishes from siblings like recommend_next and recommend_for_project by focusing on multi-part goals and splitting logic.
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 splitting rules and optional parameter description but does not explicitly state when to use this tool versus alternatives like recommend_for_project or recommend_next. Usage is implied for multi-part goals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_for_projectA
Given a project description, recommend the top MCP servers to install and explain why each one fits. Example: "Python FastAPI backend with PostgreSQL and JWT auth"
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 implies a read-like recommendation behavior but does not disclose details like whether it modifies state, requires authentication, or has limitations on input length.
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 extremely concise with two sentences and an example, front-loading the core purpose. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema, the description does not need to detail return values. It covers what the tool does and gives an example, but could mention that it returns a list of servers with explanations. The hint 'top' could be clarified.
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% coverage, so the description must add meaning. It specifies that the parameter is a 'project description' and provides an example, which clarifies the expected format beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: given a project description, it recommends MCP servers and explains why. It distinguishes from siblings like 'recommend_for_goal' by specifying the input is a project description, not a goal.
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 clear context for when to use (when you have a project description) with an example, but does not mention when not to use or explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_nextA
Mid-project advisor: given your current MCP stack (list of server names) and a new development context, recommend what to add next and why. Example: current_stack=["github", "filesystem"], new_context="adding Stripe payments and PDF invoices" session_file: optional path to a session notes file whose content is appended to new_context.
| Name | Required | Description | Default |
|---|---|---|---|
| current_stack | Yes | ||
| new_context | Yes | ||
| session_file | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full transparency burden. It explains the tool recommends with reasoning and mentions an optional session_file. The output schema exists but is not shown; the description does not cover output format, but the example hints at a recommendation with reasons. Overall, it is sufficiently transparent for a recommendation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus an example, front-loaded with purpose, and contains no unnecessary words. It is efficiently structured and easy to parse.
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 recommendation nature and the presence of an output schema (which handles return value documentation), the description covers all essential input semantics and usage context with an example. It is fully complete for agent decision-making and invocation.
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 current_stack as 'list of server names', new_context as 'development context', and session_file as 'optional path to session notes file whose content is appended to new_context'. This adds meaningful context beyond the schema. A slight improvement could clarify the format of current_stack entries.
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 recommends what to add next given current stack and new context, with an example. It distinguishes from sibling tools (explain_why, recommend_for_goal, recommend_for_project) by focusing on 'next additions' in a mid-project advisor role.
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 as a 'mid-project advisor' and provides an example, giving clear context. However, it does not explicitly state when not to use this tool or contrast with alternatives, slightly limiting guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.3.0- Added
recommend_for_goal - Changed
recommend_next3 fields changed- added
Input schema / properties / current_stack / itemsAdded value: +{ + "type": "string" +} - changed
Input schema / properties / current_stack / typePrevious value: -"string"New value: +"array" - added
Input schema / properties / session_fileAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null +}
3 tool updates
v0.1.2- First observed
explain_why - First observed
recommend_for_project - First observed
recommend_next
TDQS
Scored across 4 tools
Each tool has a distinct purpose: explaining a specific server, recommending for a multi-part goal, for a full project, or for next steps. No overlap.
All names follow verb_noun pattern (explain_why, recommend_for_goal, etc.), but 'explain_why' uses 'why' as a noun, slightly deviating from standard action-object convention.
Four tools cover the essential recommendation scenarios without redundancy. The count is well-scoped for the server's purpose.
The set covers main use cases: explaining, recommending for goals, projects, and next steps. Missing features like comparing or updating recommendations, but not critical.
Maintenance
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