mcp-nexus
MCP Nexus
MCP Nexus는 Model Context Protocol을 위한 로컬 우선 지능형 라우터입니다. AI 하네스는 Nexus라는 하나의 MCP 엔드포인트에 연결되고, Nexus가 실제 MCP 서버들을 백그라운드에서 모두 관리합니다. 즉, 도구를 인덱싱하고, 필요 시 기능을 발견하며, 서버를 지연 시작(lazy start)하고, 라우팅된 호출을 실행하며, 로컬 사용 기록을 학습하여 시간이 지날수록 결과 순위를 더 잘 매깁니다.
Before With MCP Nexus
AI Harness AI Harness
├── GitHub MCP (30 tools) └── mcp-nexus (4 control tools)
├── Jira MCP (25 tools) ├── search_capabilities
├── Slack MCP (20 tools) ├──── describe_capabilities
├── Figma MCP (18 tools) ├──── execute_capability
... └──── search_servers
~90+ tool schemas in context │
(everything else stays indexed
on disk until actually needed)왜 필요한가
연결된 각 MCP 서버는 모델 컨텍스트에 도구 스키마를 기여합니다. 서버가 10개가 되면 모델이 거의 사용하지 않는 정의에 수만 개의 토큰을 소모하게 되고, 도구 선택 품질도 저하됩니다.
Nexus는 모델을 뒤집습니다. 모든 다운스트림 스키마를 컨텍스트에 밀어 넣는 대신, 가벼운 기능 인덱스를 디스크에 유지하고 작은 제어 평면을 제공합니다. 에이전트는 필요할 때 기능을 발견하고(search_capabilities), 선택한 기능에 대해서만 정확한 스키마를 검사하며(describe_capabilities), Nexus를 통해 실행합니다(execute_capability). 모든 상태(설정, 인덱스, 분석, 학습된 시퀀스)는 .mcp-nexus/에 로컬로 저장됩니다.
Related MCP server: MetaMCP
빠른 시작
# 1. Scaffold a project config
npx @fyrlabs/mcp-nexus init
# 2. Add downstream MCP servers (anything runnable over stdio)
npx @fyrlabs/mcp-nexus add github -- npx -y @modelcontextprotocol/server-github
# or import an existing config:
npx @fyrlabs/mcp-nexus import --from claude
# 3. Point your harness at Nexus (see docs/harness-setup.md)하네스 설정(Claude Code, Cursor, Codex 및 기타 MCP 클라이언트):
{
"mcpServers": {
"mcp-nexus": {
"command": "npx",
"args": ["-y", "@fyrlabs/mcp-nexus"]
}
}
}Nexus는 작업 디렉터리에서 위로 올라가며 project-mcp.json을 자동으로 찾거나, --config ./path/to/nexus.json을 전달할 수 있습니다.
그런 다음 에이전트 관점에서:
search_capabilities { "query": "find comments people left on my PR" }
→ github.review_comments.list score=0.94 ...
describe_capabilities { "capabilityIds": ["github.review_comments.list"] }
→ exact input schema
execute_capability { "capabilityId": "github.review_comments.list",
"arguments": { ... } }
→ forwarded verbatim to the right server, started on demand노출되는 것 vs. 숨겨지는 것
모델에 노출됨 | 로컬에 유지됨 | |
제어 평면 도구 | 4개의 고정 도구 | — |
기능 메타데이터 | 검색 시에만 (작은 레코드: id, title, description, risk, score) | SQLite의 전체 인덱스 |
도구 입력 스키마 | 설명된 기능에 대해서만 | 인덱스 시점에 영구 저장 |
사용 분석 | — | 로컬 이벤트 + 집계 |
비밀 정보 | 절대 안 됨 (env 참조는 시작 시 해석되며 로그에서 삭제됨) | 셸/env에 저장 |
주요 특징
로컬 우선. 클라우드 서비스 없음, 계정 없음, 텔레메트리 없음.
.mcp-nexus/를 삭제하면 학습된 모든 상태가 사라집니다.지연 수명 주기. 다운스트림 서버는 작업이 필요할 때만 시작되고 계층형 유휴 시간 초과(핫 / 웜 / 콜드) 후 중지됩니다.
하이브리드 검색. 가중 필드에 대한 BM25 어휘 순위, 정확한 id/도구 일치, 별칭 확장(
pr → pull request, 구성 가능), 선택적 의미 계층을 위한 플러그형EmbeddingProvider인터페이스.설명이 포함된 적응형 순위. 모든 결과는 신호 분석을 제공합니다. 고정된 기능은 학습된 인기도보다 우선하며, 차단된 기능은 절대 제안되지 않습니다.
시퀀스 예측. 반복되는 도구 전환을 로컬에서 학습하고 이를 사용하여 다음에 올 가능성이 높은 기능을 부스트합니다. 예측은 절대 자동 실행되지 않습니다.
네이티브 종속성 없음. 저장소는 Node의 내장
node:sqlite를 사용합니다. 이 패키지를 설치해도 컴파일되는 것은 없습니다.하네스에 구애받지 않음. MCP stdio를 지원하는 모든 것은 Nexus 앞에 놓을 수 있습니다.
요구 사항
Node.js >= 22.5 (24 LTS 권장)
문서
설정 참조 — 모든 필드, 해석 순서, env 대체
CLI 참조 — 모든 명령 및 플래그
아키텍처 — 모듈, 점수 모델, 저장소 스키마
하네스 설정 — Claude Code, Cursor, Codex, 일반 MCP 클라이언트
examples/project-mcp.json— 주석이 달린 시작 설정
개발
git clone https://github.com/fyrlabs/mcp-nexus && cd mcp-nexus
npm install
npm run build # tsc -> dist/
npm run test # vitest (unit + integration, mirrors src/ structure under src/tests/)
npm run typecheck # strict tsc, no emit
npm run lint # eslint통합 테스트는 실제 @modelcontextprotocol/server-everything 패키지를 다운스트림 stdio 서버로 사용하고 전체 런타임을 통해 실행을 라우팅합니다. 패키지를 해석할 수 없으면 자동으로 건너뜁니다.
기여 규칙(커밋, 버전 관리, 구조)은 AGENTS.md를 참조하세요.
개인정보 보호
Nexus는 설정 캐시, 인덱스, 분석을 .mcp-nexus/(또는 XDG 데이터 디렉터리)에 저장합니다. 라우터 자체는 아무 곳에도 아무것도 보내지 않습니다. 외부 임베딩 제공자를 구성하는 경우, 기능 텍스트(제목/설명/키워드)만 해당 제공자에게 전송됩니다. 인수, 비밀 정보, 분석은 절대 전송되지 않습니다. 원시 도구 인수는 절대 영구 저장되지 않습니다.
라이선스
Available Tools
4 toolsdescribe_capabilitiesDescribe capabilitiesB
Return the full metadata and input schemas for the given capability IDs.
| Name | Required | Description | Default |
|---|---|---|---|
| capabilityIds | Yes | Capability IDs returned by search_capabilities |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It clearly states the tool returns metadata and schemas, which is a read-like operation, but it doesn't disclose any limitations, such as whether invalid IDs cause errors or whether the returned data is verbose. The description is accurate but sparse on behavioral nuances.
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 clear sentence that is entirely on-topic. It front-loads the action and object, with no filler. Could earn a 5 if it referenced a sibling or workflow, but it remains 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?
Given there is no output schema and no annotations, the description is the sole source of behavioral information. It tells the agent what the tool returns, but for a metadata-inspection tool, an agent might need to know whether it can be used before executing a capability or how errors are surfaced. Still, for a simple lookup tool, this is 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 coverage is 100%, so the schema already documents the 'capabilityIds' parameter. The description adds the purpose context ('full metadata and input schemas') but doesn't add format or constraint details beyond the schema. Baseline 3 applies.
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 ('Return') and resource ('metadata and input schemas') for given capability IDs. It distinguishes itself from sibling tools like search_capabilities (which likely finds capabilities) and execute_capability (which runs them), though it doesn't explicitly name them.
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: given capability IDs, retrieve metadata. The parameter description notes IDs come from search_capabilities, implicitly showing a workflow step. However, it doesn't state when to use this vs execute_capability or search_servers, leaving some inference needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_capabilityExecute capabilityA
Execute a downstream MCP capability by ID. The underlying server is started on demand and the arguments are forwarded verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | No | Arguments matching the tool's input schema (see describe_capabilities) | |
| capabilityId | Yes | Capability ID to execute |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses two genuine behaviors: the backing server is 'started on demand' (affecting latency/resource use) and arguments are 'forwarded verbatim' (no transformation). It does not mention output shape, error behavior, or potential side effects of the downstream capability, so coverage is partial.
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, each adding distinct information: the core action and two behavioral caveats. No redundant or filler wording, and the most important information is front-loaded.
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 definition is largely sufficient for a simple pass-through invocation, and the arguments schema points to describe_capabilities for the input contract. However, there is no output schema or description of the return value/error behavior, so an agent does not know what to expect after invocation. This is a moderate gap given 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?
The schema already documents both parameters completely (100% coverage), so the baseline is 3. The description's 'arguments are forwarded verbatim' reinforces the arguments parameter's semantics, but adds no new parameter-specific detail beyond the schema, which already tells the agent to see describe_capabilities for the expected argument shape.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action – 'Execute a downstream MCP capability by ID' – with a clear verb and resource. The 'by ID' scoping and the 'downstream MCP' qualifier distinguish it from siblings like describe_capabilities and search_capabilities, which are informational rather than invocational.
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: you invoke this when you have a capability ID and want to run it, and the 'forwarded verbatim' phrasing indicates arguments should already conform to the target schema. It does not explicitly list exclusions or alternatives, but the sibling names make the distinction clear. This is adequate context without explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_capabilitiesSearch capabilitiesA
Search every indexed downstream MCP capability by natural language, keywords, tags, server id, or capability id. Returns lightweight metadata only.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return | |
| query | Yes | Natural language or keyword query describing the capability you need |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full behavioral burden. It usefully discloses that the tool is a search operation returning 'lightweight metadata only', which implies read-only behavior and no full payload or execution. However, it does not mention result shape, pagination behavior, indexing caveats, or what happens when nothing matches.
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?
One dense, front-loaded sentence carries the purpose, scope, search dimensions, and return nature with no filler. Every phrase earns its place, and the most important information appears first.
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 two-parameter search tool, the description is largely complete: the query semantics are enriched, the limit is documented in the schema, and the return type is characterized. The main remaining gap is the absence of an output schema or a more concrete list of returned metadata fields, but this does not prevent a capable agent from selecting and invoking the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is already 100%, so the baseline is 3. The description adds genuine meaning beyond the schema by expanding query to include tags, server id, and capability id as acceptable forms, which the schema's 'natural language or keyword query' line does not convey. The limit parameter remains fully covered by its schema 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?
The description identifies the exact action ('Search'), the resource ('every indexed downstream MCP capability'), and the query dimensions (natural language, keywords, tags, server id, capability id), which clearly differentiates it from search_servers and the other sibling tools. It also specifies the return nature ('lightweight metadata only'), leaving no ambiguity about what the tool is for.
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 scope phrasing implies when to use it—whenever you need to discover a capability across indexed downstream MCPs—but it never explicitly states when not to use it or points to describe_capabilities/execute_capability/search_servers. Sibling names hint at those alternatives, but the description itself provides no routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_serversSearch serversA
Rank configured downstream MCP servers against a domain-level query. Useful before drilling into capability search.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Domain-level query such as 'project management' |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the core behavior (ranking servers, not modifying them) and the scoping to configured downstream MCP servers. It doesn't cover details like ranking criteria, read-only guarantees, or response shape, but the disclosed behavior is accurate and non-contradictory.
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 short sentences with no filler. The core action is front-loaded, and the usage context is a single additional clause that 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?
For a single-parameter search tool with no output schema, the description is largely complete: it states what is ranked, the query type, and when to use it. The main gap is the absence of any mention of the return value, which the absence of an output schema makes slightly more relevant.
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%, and the query parameter is already documented in the schema. The description reinforces 'domain-level query' but adds no parameter format or constraints beyond the schema, so the baseline 3 applies.
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 uses a specific verb ('Rank'), a specific resource ('configured downstream MCP servers'), and a clear scope ('domain-level query'). It also positions itself against the capability-search siblings, so an agent can distinguish it from search_capabilities 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?
Description gives explicit temporal guidance: use this before drilling into capability search. It doesn't explicitly name the alternative or list when-not-to-use cases, but the context is clear enough given sibling names.
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.
4 tool updates
v0.10.0- First observed
describe_capabilities - First observed
execute_capability - First observed
search_capabilities - First observed
search_servers
TDQS
Scored across 4 tools
Each tool has a clear operational role: searching servers, searching capabilities, describing schemas, and executing. search_servers and search_capabilities are adjacent but their descriptions clearly separate domain-level server ranking from capability-level search.
All tool names follow a consistent verb_noun snake_case pattern. The only minor inconsistency is 'capabilities' being plural in search/describe but singular in execute_capability.
Four tools is well-scoped for a gateway or meta-server: discover servers, discover capabilities, inspect schemas, and execute. There is no redundancy or unnecessary bloat.
The discover → describe → execute workflow is covered end-to-end. Minor gaps such as explicit list-all-server or dedicated server metadata retrieval are missing, but search_servers can largely cover discovery needs.
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