vhdl-rag-mcp
vhdl-rag-mcp
조직의 VHDL 코드, VHDL 관련 문서, 그리고 일반 소스 코드(C/C++, Python, ...)에 대한 고품질 의미론적 검색을 코딩 에이전트에 제공하는 MCP(Model Context Protocol) 서버입니다. 모든 것은 상호 참조되며, 정확한 소스 출처가 함께 제공됩니다.
stdio를 통해 uvx vhdl-rag-mcp로 실행됩니다. 외부 서비스가 필요하지 않습니다. Qdrant는 임베디드로 실행되고, 임베딩 모델은 로컬에서 실행됩니다(ONNX via FastEmbed).
기능
세 가지 인덱스 도메인, 하나의 서버. VHDL 소스, 문서(Markdown/reST/text), 일반 코드(C/C++, Python, ...)는 각각 세 개의 Qdrant 컬렉션에 저장되며, 각 청크에는 밀집(jina v2) 및 희소(BM25) 벡터가 하나씩 포함됩니다.
하이브리드 검색. 모든 쿼리는 Qdrant 고유의 하이브리드(밀집 + 희소, RRF 융합) 쿼리로 실행됩니다. 의미적 유사성 및 정확한 식별자 일치가 한 번의 호출로 이루어집니다.
rst_n을 물으면 그 결과를 얻을 수 있습니다.VHDL 인식 청킹. VHDL 파일은 vhdl_ls 언어 서버(
documentSymbol, 정확한 줄 범위 포함)를 사용하여 구성 단위(entity, architecture, process, package, function, component)별로 청킹됩니다. 구문 오류가 있는 파일은 구조 기반 줄 스캐너로 대체되고, 마지막으로 전체 파일 단위로 처리되므로 어떤 VHDL도 유실되지 않습니다.구조 인식 청킹. 문서는 헤딩 섹션별로 청킹됩니다. 일반 코드는 tree-sitter(문법이 있는 모든 언어)로 최상위 함수/클래스 단위로 청킹되며, 포함되지 않은 최상위 코드는 파일 범위 갭 청크로 채워집니다.
교차 참조. 모든 청크 페이로드에는 해당 청크가 정의하거나 참조하는 식별자(
symbols)가 저장됩니다. 검색 도구는symbols필터를 지원하여 지정된 식별자를 참조하는 청크만 반환하며, 이를 통해 문서 ↔ VHDL ↔ 테스트 코드를 연결합니다(예:fifo_write를 다루는 모든 VHDL 프로세스와 C 함수 찾기).우선순위 인식 랭킹. 저장소는 카테고리(
golden>approved>project>legacy) 또는 명시적priority0–100을 가지며, 융합 점수에 제한된 가중치를 적용합니다. 이를 통해 참조 저장소가 관련성 동점에서 우위를 차지할 수는 있으나, 실제 유사도를 묻어두지는 않습니다.정확한 소스 출처. 모든 결과에는 저장소, 파일, 줄 범위, 커밋이 명시됩니다.
get_source는 동기화된 작업 트리에서 현재 파일(또는 줄 범위)의 정확한 내용을 반환합니다.점진적·자기 유지형 인덱스. 저장소는 Git(clone/fetch/diff)에서 동기화됩니다. 변경된 파일만 다시 청킹 및 임베딩됩니다. 백그라운드 작업은
sync_interval초마다 동기화를 실행하며, 도구를 통해 언제든지 강제 동기화 또는 전체 재구축을 실행할 수 있습니다.명활한 오류 처리(Graceful degradation). 오류는 저장소별로 차단되어 상태에 기록됩니다. 하나의 저장소가 고장나도 다른 저장소나 서버 실행에는 영향이 없습니다.
프로토콜 전용 표준 출력. 모든 로깅은 stderr와 회전 로그 파일로 전송되므로, 어떤 MCP 호스트에서도 안전하게 실행할 수 있습니다.
Related MCP server: PAMPA
설치
요구 사항:
uv (
uvx용), Python ≥ 3.12Git (개인 저장소용 인증/SSH 설정 포함)
vhdl_ls바이너리(VHDL 저장소에서만 필요): https://vhdl-lang.org/에서 릴리스를 설치하여vhdl_ls가PATH에 있거나vhdl_ls_path가 사용하는 바이너리를 지정하세요. 바이너리와 함께 배포되는vhdl_libraries디렉터리는 자동 감지됩니다.
$ uvx vhdl-rag-mcp --help
# (the server speaks MCP over stdio; --help is not a flag — see "Usage")첫 시작 시 서버는 데이터 디렉토리를 생성하고, 임베딩 모델을 다운로드하며(jina v2 base-code + base-en, 각각 수십 MB, 최초 1회), 설정된 모든 저장소를 초기 동기화합니다.
설정
구성 파일: ~/.config/vhdl-rag/config.toml (파일이 없으면 처음 실행 때 주석 템플릿이 생성됩니다).
data_dir = "~/.local/share/vhdl-rag" # all state lives here
sync_interval = 300 # seconds between periodic syncs
vhdl_ls_path = "vhdl_ls" # binary on PATH or full path
log_level = "INFO"
[embeddings]
vhdl_model = "jinaai/jina-embeddings-v2-base-code" # per-collection dense models
docs_model = "jinaai/jina-embeddings-v2-base-en"
code_model = "jinaai/jina-embeddings-v2-base-code"
sparse_model = "Qdrant/bm25" # one shared sparse model
[qdrant]
mode = "local" # embedded (default) — or "server" with url
# url = "http://qdrant:6333"
[[repositories]]
name = "company-standards" # unique, [A-Za-z0-9._-]
url = "git@github.com:company/vhdl-standards.git"
ref = "main" # branch (tracked on every sync),
# tag, or commit SHA (pinned)
category = "golden" # golden | approved | project | legacy
priority = 100 # optional 0-100 (defaults by category:
# golden=100, approved=90, project=70, legacy=20)
# domains = ["vhdl", "docs", "code"] # which domains to index (default: all)
# exclude = ["sim", "build/*", "*.log"]# glob path excludes ('*' crosses '/');
# wildcard-free patterns exclude the subtree참고:
ref: 동기화 때마다 브랜칠 이름을 fetch하고 저장합니다. 태그 혹 commit SHA는 사용자는 저장소를 고정합니다(정확히 40자리 16진수 SHA면 네트워크 fetch를 완전히 건너니다).저장보별 도메인/제외 (정도 설정: 각 저장소가 어떤 도메인에 속하는 제어합니다. 예:
domains = ["vhdl"]은 순수 IP 저장소를,exclude = ["sim"]은 시뮬레이션 전용 파일을 무시합니다.임베딩 모델 변경은 밀집 벡터의 차원을 바꿉니다. 서버는 인덱스를 주지 않고 뚜렷한 경고 메시지와 함께 명확히 실패합니다(단, 컬렉션 또는
data_dir을 삭제하고 재인덱싱하면 해제됩니다).
사용법
서버 실행
$ uvx vhdl-rag-mcp호스트가 연결을 닫기 전까지 stdio를 통해 MCP를 제공합니다. 백그라운드 작업이 sync_interval초마다 모든 저장소를 동기화합니다. 단일 인스턴스 잠금(data_dir/서버.lock)으로 두 서버가 같은 데이터 디렉토리를 동시에 사용하지 못하게 합니다.
MCP 클라이언트 등록
Claude Code:
$ claude mcp add vhdl-rag-mcp -- uvx vhdl-rag-mcpMaki(TOML 설정 — 정확한 테이블 이름은 사용 중인 Maki 버전의 문서를 확인하세요):
[mcp_servers.vhdl_rag_mcp]
command = "uvx"
args = ["vhdl-rag-mcp"]도구
도구 | 기능 | |
| VHDL 소스(엿티티, 아키텍처, 프로세스, 패키지, 함수)에 대한 하이브리드 검색. | |
| 문서 섹션에 대한 동일 검색. | |
| 일반 코드 유닛(함수/클래스)에 대한 동일 검색. | |
| 세 도메인을 한 번에 검색하고 RRF로 융합. | |
| 커밋 출처가 포함된 현재 파일(또는 일부 구간)의 정확한 내용 반환. | |
| 저장소별: category, ref, domains, 마지막 인덱스 커밋, 마지막 동기화, 마지막 오류. | |
| 점진적 동기화(기본값: 전체). 오류는 저장소별로 격리됩니다. | |
| 지정한 저장소의 인덱스를 삭하하 고 재구축하습니다. |
모든 검색 도구는 선택적 repo(repository)(이름) 및 category(golden/approved/project/legacy) 필터, 그리고 symbols: list[str]를 지원합니다. 하나라도 주어진 식별자를 참조하는 청크로 제합됩니다. 결과는 소스 출처와 함께 마크다운 점수, 참조 식별자를 담은 마크다운으로 렌더링되고, 도메인별로 코드 블록이 구분됩니다.
예시 어시트 흐름:
search_knowledge("asynchronous reset conventions")→ 리셋에 대한 문서 섹션과 실제 리셋을 구현한 VHDL 프로세스.search_vhdl("reset", symbols=["rst_n"])→rst_n을 참조한 모든 VHDL 청크.get_source("company-standards", "rtl/reset_ctrl.vhd", 12, 40)→ 복사하기에 딱 좋은 정확한 로우 범위 내용.
운영
데이터 디렉터리(
data_dir):. Qdrant 컬렉션, 저장소별 Git 워킹 트루트(<name>/) 상태(state/repositories.json), 로그 파일(logs/vvdh-rag-mcp.log) 및 잠금 파일을 포함합니다. 이 텍터를 삭제하면 인덱스가 초기화됩니다.상태 및 재시도: 저장소의
indexed_commit은 해당 저장소의 인덱스 업데이트가 완전히 성공한 경우에만 진행됩니다. 동기화 실패 시 이전 커밋이 유지되어 다음 동기화에서 동일한 diff를 다시 시도합니다.last_sync_error는repository_status에서 확인할 수 있습니다.설정에서 저장소 제거: 다음 시작 시 서버가 상태 파일 사용해서 해당 저장소의 모든 청크와 저장소 상태를 자動으로 삭제합니다.
로그:
stderr및 프로에서logs/vhdl-rag-mcp.log(롤링, 3×5 MB)로 기록됩니다.log_level = "DEBUG"설정하면 LSP/git/임베딩 상세 로그를 볼 수 있습니다.
개발
$ uv sync
$ uv run ruff format -q . && uv run ruff check . # format + lint
$ uv run mypy src # strict types
$ uv run pytest -q # offline test suite테스트 스윗트는 완전히 오프라인으로 동작합니다. 로컬 file:// git 리모트, 가짜 LSP 서버, 임베딩 프로바이더를 사용합니다. 실제 배이너리 테스트 하나는 VHDL_LS_TEST_BIN 환결 변수에 게이트되어 있습니...
Layout:
src/vhdl_rag_mcp/
config.py typed config (pydantic) + default template
state.py atomic repository sync state
git_manager.py async clone/fetch/checkout + incremental SyncPlan
routing.py extension -> domain classification (+domains/excludes)
lsp/client.py vhdl_ls LSP client (handshake, quiet-wait, symbols)
embeddings/ FastEmbed dense/sparse providers (per-collection + shared)
vector_store.py Qdrant wrapper: hybrid RRF query, payload filters
indexing/ vhdl (LSP-primary), docs (sections), code (tree-sitter),
pipeline (incremental sync driver)
retrieval.py search service: fusion, priority bonus, source access
server.py FastMCP tools + startup + periodic sync + lockAvailable Tools
8 toolsget_sourceARead-only
Read the exact current content of an indexed file (or a line
range) from the synced repository, with commit attribution.
file is the repository-relative path from any search result's
source line.
| Name | Required | Description | Default |
|---|---|---|---|
| file | Yes | ||
| end_line | No | ||
| repository | Yes | ||
| start_line | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation `readOnlyHint=true` already signals a safe read operation. The description goes beyond this by noting that it returns exact current content and includes commit attribution, which tells the agent more about what to expect without repeating the annotation.
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 focused sentences with no fluff. The primary action and subject are front-loaded, and the value add about `file` is kept brief.
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 `readOnlyHint` set, an existing output schema, and a fairly simple parameter shape, the description gives most of what an agent needs to use the tool after a search. The remaining gaps are the exact form of `repository` and line-range boundary behavior, which are useful but not crippling.
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 partially compensates: it clearly explains `file` as the repository-relative path from a search result and introduces the concept of line ranges. However, `repository` is left only with its name, and start/end line semantics are not fully specified.
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 ('Read') and resource ('indexed file' with optional line range), and places it cleanly in the repository/search context. It is clearly distinguishable from sibling search and repository-maintenance 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?
The description gives concrete usage context by explaining that `file` comes from a search result's source line, and that the tool reads from a synced repository. It does not explicitly list alternative tools or when not to use it, but the intended workflow is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reindex_repositoryB
Fully reindex one repository (drops and rebuilds all of its chunks). Use after config changes or to repair a drifted index.
| Name | Required | Description | Default |
|---|---|---|---|
| repository | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says the tool 'drops and rebuilds all of its chunks', which is destructive behavior, but the annotations set destructiveHint=false. This contradicts the annotation and leaves the agent with conflicting signals about whether the tool is safe or destructive.
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 front-loaded sentence: it starts with 'Fully reindex one repository', adds an essential parenthetical about destructiveness, and then a concrete use case. Every phrase contributes value with no waste.
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 the single repository parameter is simple. However, the clash between the description's 'drops' language and the destructiveHint false annotation undermines the tool's overall safety context, and the parameter format remains underspecified.
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 description does not explicitly explain how the 'repository' parameter should be identified, whether it is a name, ID, or path. It only repeats that 'one repository' is reindexed, so it does not compensate for the lack of parameter documentation.
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, 'reindex', a resource, 'repository', and the precise scope: 'fully', 'drops and rebuilds all of its chunks'. It also names concrete use cases, making it clearly distinguishable from the sibling search/status/sync 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 says when to use the tool: 'after config changes or to repair a drifted index'. However, it does not explicitly mention when not to use it or direct the user to a sibling alternative, but the use case is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
repository_statusARead-only
Show every configured repository: category, ref, enabled domains, last indexed commit, last sync time, and any sync error.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already provides the key safety trait. The description adds that the tool lists all configured repositories and includes sync error state, which is useful context, but does not address more specific behaviors like pagination or result size limits. No contradiction with 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?
One clear, front-loaded sentence that states the action first, then lists the output fields. Every word earns its place and there is 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?
For a zero-parameter, read-only listing tool with a readOnlyHint annotation and an output schema, this description fully communicates what the tool does and what the agent should expect. No critical gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so there is no parameter burden on the description. Baseline of 4 is appropriate for a no-parameter tool; nothing further 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?
Description starts with a clear verb ('Show') and identifies the resource ('every configured repository'), then enumerates the exact fields returned. It is easy to distinguish from sibling tools that search, sync, or reindex.
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 that this tool is for inspecting repository state, but it does not explicitly say when to use it instead of sync_repositories or reindex_repository. The read-only status context is clear but no alternatives or exclusion conditions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_codeARead-only
Search general source code (C/C++, Python, ...): one result per
function/class. symbols matches identifiers referenced in the
unit (cross-reference to VHDL signal/port names, etc.).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| symbols | No | ||
| category | No | ||
| repository | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already covers the read-only nature. The description adds meaning beyond that by clary specifying the result granularity ('one result per function/class') and the special behavior of 'symbols' (matches identifiers referenced in the unit). This gives useful behavioral details not present in the annotations concurrently.
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 concise two-sentence block. The first sentence says what the tool does and states its granularity, while the second explains the non-obvious 'symbols' parameter behavior. Every sentence carries functional value and the core purpose is frontal-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 tool has five parameters but only one is required and their semantics are mostly absent; the description explains only one optional parameter. The tool has an output schema, so return exploitation is not needed, but the optional `category', 'repository', and 'limit' parameters remain unclear, leaving a evaluable operational 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%, so the description carries the full burden of explaining parameters. It only clarifies 'symbols'; the meaning of 'query', 'limit', 'category', and 'repository' remains undocumented. Given five parameters and zero schema descriptions, this is insufficient for an agent to use all capabilities with confidence.
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 action ('Search general source code') and specifies the resource (C/C++, Python) and a distinctive granularity ('one result per function/class'). It is also differentiates from siblings like search_docs, search_vhdl, and search_knowledge by being the general code search.
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 when to use this tool: for general source-code search rather than docs, VHDL, or knowledge searches. It does not explicit name alternatives or provide exclusion rules, but the resource scope and the symbol matching note give adequate context for most agent decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_docsBRead-only
Search VHDL-related documentation: coding standards, design
guides, conventions (one result per section). symbols matches
identifiers referenced in the section's code snippets.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| symbols | No | ||
| category | No | ||
| repository | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, so the risk profile is covered. The description adds some behavioral context, notably one-result-per-section behavior and how the symbols parameter matches code snippet identifiers, but it does not describe pagination, output structure, or any special matching behavior for the regular query.
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 succinct sentences convey the tool's domain, content scope, result granularity, and a special parameter behavior. Every clause earns its place and there is no fluff or resuppLI.
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 description is usable for a read-only documentation search, but context is incomplete: the category and repository parameters are undefined, there is no guidance about how the tool relates to the sibling search tools, and the meaning of 'one result per section' is not expanded enough to set why that limitation matters.
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 only explains the symbols parameter; query, limit, category, and repository receive no semantic explanation beyond their raw names and defaults. This leaves a significant gap for an agent choosing how to populate the parameters.
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 uses a specific verb 'Search' and identifies a domain/resource, 'VHDL-related documentation', while enumerating content types: coding standards, design guides, conventions. It is clear, though it does not explicitly differentiate itself from the sibling tool search_vhdl.
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 communicates a search scope but does not indicate when to use this tool versus alternatives like search_code, search_knowledge, or the similarly named search_vhdl. There are no explicit conditions or exclusion criteria provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_knowledgeARead-only
Search ALL domains (VHDL, documentation, code) at once, fused with RRF so the domains interleave fairly. Use when the question may span domains (e.g. a design requirement in the docs implemented in VHDL and tested in C).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| symbols | No | ||
| category | No | ||
| repository | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description supplements this with useful behavioral detail: all domains are searched at once and results are fused via RRF for fair interleaving. This goes beyond the structured annotation without contradicting it.
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 tight sentences: the first states the core behavior, and the second gives a usage criterion and concrete example. There is no filler, fluff, or repetition of schema/annotation details.
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?
It is adequately complete for tool selection: it says what the tool searches, how it combines results, and when to use it. However, the shape has 5 undocumented parameters and an output schema, so an agent still lacks detail on what symbols/category/repository constrain and what an RRF fusion result looks like
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 parameter description coverage is 0%, so the description carries the burden of documenting parameters, but it never mentions limit, symbols, category, or repository. Only 'query' behavior is implied through 'Search ALL domains...', leaving agents to guess what the optional filters do.
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 opens with a specific verb and clear scope: 'Search ALL domains (VHDL, documentation, code) at once'. It also names a concrete behavior, RRF fusion, which distinguishes this tool from domain-specific siblings like search_docs and search_code.
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 gives explicit guidance: 'Use when the question may span domains' and provides a realistic cross-domain example. It does not explicitly name the domain-specific alternatives or say when not to use this tool, but the intended use case is conveyed clearly enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_vhdlARead-only
Search VHDL source: entities, architectures, processes, packages,
functions — semantic + exact-identifier hybrid search.
symbols restricts to chunks referencing the given identifiers
(e.g. ["fifo_write", "rst_n"]). category: golden/approved/
project/legacy. repository restricts to one repository name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| symbols | No | ||
| category | No | ||
| repository | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, and the description adds valuable behavioral details: the search is hybrid semantic/exact, symbols restrict to chunks referencing identifiers, and category/repository narrow results. It does not describe index-freshness limitations, but output schema and read-only promise reduce that burden.
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 front-loaded with purpose, then dives into parameter semantics with backtick highlighting and a concrete `symbols` example. Every sentence contributes value, though the combination of hybrid-search jargon and parameter explanations makes it dense rather than simple.
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 read-only search tool, the description covers the core behavior and all non-obvious filters. The presence of an output schema means the return-value structure is already handled externally. A brief note on indexed-repository freshness or when to prefer search_code would improve completeness, but this is enough for correct 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?
Given the input schema has 0% description coverage, the description compensates for `symbols`, `category`, and `repository` with concrete semantics and a useful example. `query` is naturally explained by the search purpose, and `limit` has an obvious default and title, leaving no major ambiguous parameters.
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 opens with a clear action and resource: 'Search VHDL source: entities, architectures, processes, packages, functions'. It also distinguishes the tool from generic search siblings by confining it to VHDL and promising a hybrid semantic/exact-identifier behavior.
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 VHDL-specific phrase immediately signals when to use the tool—when searching VHDL source constructs—but no alternative tools or exclusions are named. This is clear context, yet lacks the explicit sibling routing that would earn a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sync_repositoriesA
Incrementally sync repositories (default: all): fetch the ref, chunk changed files, update the index. Safe to call any time; failures are contained per repository and reported.
| Name | Required | Description | Default |
|---|---|---|---|
| repositories | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=false, and the description does not contradict them. It adds useful behavioral detail beyond the annotations: the sync updates the index, explicitly signals reusability ('safe to call any time'), and discloses containment of failures per repository.
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 short, immediately front-loads the core action, and each clause adds a distinct piece of information: scope, mechanism, safety, and failure containment. There is no fluff or repeated schema content.
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 one-parameter operation with an output schema present, the description covers the core behavior, the optional input semantics, and failure behavior. Nothing critical is missing for an agent to decide whether and how to invoke it.
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 no property descriptions are present. The description clarifies the important default behavior ('default: all') and implies that the optional 'repositories' list filters which ones are synced, but it does not add detail about the expected string format or how omitted values behave beyond the default. It partially compensates 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 clearly states the verb ('sync'), the resource ('repositories'), and the mechanism ('fetch the ref, chunk changed files, update the index'). The word 'incrementally' meaningfully distinguishes it from a full rebuild and brings out the tool's intended scope.
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 clear usage context: callable any time, defaults to all repositories, and supports an optional subset list. It does not explicitly name alternatives or state when not to use it, but 'incremental' and 'safe to call any time' provide enough orientation for an agent.
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. Dates show when Glama detected each change.
8 tool updates
v0.1.0- First observed
get_source - First observed
reindex_repository - First observed
repository_status - First observed
search_code - First observed
search_docs - First observed
search_knowledge - First observed
search_vhdl - First observed
sync_repositories
TDQS
Each tool has a clearly distinct role: domain-scoped searches (docs, VHDL, code) are separated from a fused all-domain search, and source retrieval plus sync/reindex operations are unambiguous. The descriptions reinforce the boundaries, so an agent should rarely misselect.
Most tools follow a clear verb_noun pattern: search_docs, search_vhdl, search_code, search_knowledge, get_source, sync_repositories, and reindex_repository. repository_status breaks the pattern by using a noun phrase, but the overall naming is still predictable and readable.
Eight tools is well-scoped for a RAG/search MCP server: domain-specific searches, a combined search, source retrieval, status, and index maintenance each earn their place. There is no obvious bloat or redundancy.
The server covers the full expected surface for VHDL RAG: searching documentation, VHDL source, general code, and all domains together, plus retrieving exact source content and managing repository indexing state. The sync and reindex tools close the otherwise common operational gap.
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