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DeckProbe MCP Server

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by deckflow

DeckProbe MCP Server

에이전트가 PDF, Office 또는 iWork 파일을 열지 않고도 그 안에 무엇이 있는지 물어볼 수 있게 해 줍니다.

CI npm License: MIT

설치 · 도구 · 구성 · 보안 · 작동 방식 · DeckProbe

DeckProbe — 문서용 ffprobe — 를 네 개의 타입화된 도구로 노출하는 MCP 서버입니다. 페이지 수, 슬라이드 수, 메타데이터, 암호화 및 매크로 신호, 구조 또는 무결성을 요청하면 신뢰도, 근거, 측정된 I/O 비용과 함께 제한된 결정적 JSON을 반환합니다.

렌더링이 없고, 매크로가 실행되지 않으며, 외부 참조를 따르지 않고, 네트워크 연결이 열리지 않습니다. 신뢰할 수 없는 파일을 대상으로 지정해도 안전합니다.

// probe { "path": "deck.pptx", "targets": ["slide_count"], "view": "values" }
{
  "schema_version": 2,
  "status": "ok",
  "driver": { "id": "powerpoint", "profile": "pptx" },
  "values": { "powerpoint.slide_count": 31 },
  "view": "values"
}

설치

사전에 설치할 것이 없습니다. npx가 서버와 엔진을 함께 가져옵니다.

Claude Code

claude mcp add deckprobe -- npx -y @deckflow/deckprobe-mcp

Claude Desktop, Cursor, VS Code, Zed 및 mcpServers를 읽는 기타 모든 클라이언트

{
  "mcpServers": {
    "deckprobe": {
      "command": "npx",
      "args": ["-y", "@deckflow/deckprobe-mcp"]
    }
  }
}

고정 설치를 원하면 npm install -g @deckflow/deckprobe-mcp를 실행하고 deckprobe-mcp를 명령으로 사용하세요.

Node.js 20 이상이 필요합니다. 엔진 바이너리는 macOS, Linux(glibc 및 musl), Windows의 x86-64 및 ARM64용 플랫폼별 선택 종속성으로 제공됩니다. 그 외의 환경에서는 서버가 동일한 엔진을 WebAssembly로 컴파일한 버전으로 대체하므로 npx는 Node가 실행되는 모든 곳에서 작동합니다.

Related MCP server: flexorch-mcp

도구

도구

용도

probe

단일 문서에 대한 모든 정보

probe_batch

한 번의 호출로 여러 문서를 목록화하거나 분류

list_formats

지원되는 형식과 지원이 중단되는 지점

list_targets

형식이 제공하는 정확한 대상 이름

또한 실행 중인 엔진에 번들된 보고서 JSON Schema를 담은 deckprobe://schema 리소스가 하나 있습니다.

probe

{
  "path": "reports/q3.pptx",
  "targets": ["@summary", "@security"],  // presets, short names, or canonical names
  "level": "metadata",                   // header | metadata | deep
  "min_confidence": "high",              // low | medium | high | exact
  "target_confidence": { "slide_count": "exact" },
  "view": "report",                      // report | values
  "budget": { "max_physical_bytes": 8388608, "timeout_ms": 1000 }
}

targets는 짧은 이름(slide_count), 정식 이름(powerpoint.slide_count) 및 프리셋을 허용합니다:

프리셋

확장 내용

@header

컨테이너 식별 정보만 — 형식, 크기, 확장자 일치, 암호화 플래그

@summary

식별 정보, 공통 메타데이터 및 기본 구조

@security

암호화, 매크로, 서명, 외부 참조, 활성 콘텐츠

@structure

형식 고유의 개수, 이름 및 차원

@assets

이미지, 미디어, 미리보기, 글꼴, 포함된 개체

@quality

무결성, 복구, 확장자 일치, 적합성

@format

활성 레벨의 모든 형식별 대상

@all

활성 레벨에서 사용 가능한 모든 것

@summary는 전체 파일 읽기가 필요한 통계를 의도적으로 생략합니다. PDF의 page_count가 대표적인 경우입니다 — 명시적으로 요청하세요.

probe_batch

{ "paths": ["a.pdf", "b.pptx", "c.xlsx"], "targets": ["@security"] }

하나의 엔진 프로세스가 전체 배치를 처리합니다. 결과는 입력 순서대로 반환되며, 각각 자체 보고서 또는 자체 오류를 가지므로 잘못된 파일 하나가 실행 전체를 망치지 않습니다. 기본값은 간결한 values 뷰입니다. 리터럴 경로만 허용합니다 — glob은 직접 확장하세요.

list_formatslist_targets

list_targetsformat(pdf, docx, xlsx, pptx, doc, xls, ppt, key, numbers, pages)을 받아 각 대상의 별칭, 설명, 값 유형, 최소 레벨, 비용 클래스 및 선택기 멤버십을 반환합니다. 엔진의 전체 보고서(대상별 JSON Schema 조각 및 확장된 선택기 목록 포함)를 보려면 detail: "full"을 전달하세요.

둘 다 서버 프로세스 수명 동안 캐시됩니다.

보고서 읽기

도구 결과는 엔진 자체의 schema-v2 봉투를 수정하지 않은 것입니다. 소비하기 전에 알아야 할 두 가지가 있습니다:

  • status: "partial"은 실패가 아닙니다. 요청된 대상 중 하나 이상이 요청된 신뢰도로 해석될 수 없음을 의미합니다. 해당 대상은 execution.unresolved_targets에 명시되며, 다른 모든 결과는 여전히 유효합니다.

  • confidence_score는 라벨당 고정 상수(0.4, 0.7, 0.95, 1.0)이며, 보정된 확률이 아닙니다. 0.95가 값이 95%의 확률로 맞다는 의미는 아닙니다.

resolved 또는 estimated 상태의 결과만 value를 갖습니다. unknown은 흔하며, 일반적으로 문서가 해당 사실을 단순히 기록하지 않음을 의미합니다.

실패한 호출은 엔진의 오류 봉투와 함께 무엇을 해야 하는지에 대한 한 줄을 포함한 isError를 반환합니다. 서버가 엔진 실행 전에 발생시키는 실패(경로 누락, 디렉터리, 허용 목록 밖의 경로, 마감 시간 초과)는 MCP_ 접두사 코드와 origin: "mcp-server"를 사용하여 동일한 봉투 형태를 사용합니다.

구성

모든 설정은 환경 변수이며, 클라이언트의 MCP 구성에 설정합니다. 모두 선택 사항입니다.

변수

기본값

의미

DECKPROBE_MCP_BIN

번들된 엔진 대신 사용할 엔진 바이너리

DECKPROBE_MCP_ROOTS

제한 없음

허용된 디렉터리, PATH처럼 구분

DECKPROBE_MCP_TIMEOUT_MS

30000

엔진 프로세스에 대한 호출당 하드 마감 시간

DECKPROBE_MCP_MAX_CONCURRENCY

4

동시 엔진 프로세스 수

DECKPROBE_MCP_MAX_BATCH

64

한 번의 probe_batch 호출이 허용하는 경로 수

{
  "deckprobe": {
    "command": "npx",
    "args": ["-y", "@deckflow/deckprobe-mcp"],
    "env": { "DECKPROBE_MCP_ROOTS": "/Users/me/Documents:/Users/me/Downloads" }
  }
}

보안

DeckProbe는 신뢰할 수 없는 입력을 위해 설계되었습니다: 제한된 파싱, 렌더러 없음, 매크로 인터프리터 없음, 외부 참조 해석 없음, 네트워크 액세스 없음. 이 서버는 그 위에 두 가지를 추가합니다.

  • 프로세스 격리 및 하드 마감 시간. 각 프로브는 자체 수명이 짧은 프로세스에서 실행되며, DECKPROBE_MCP_TIMEOUT_MS를 초과하면 종료됩니다.

  • 선택적 읽기 허용 목록. DECKPROBE_MCP_ROOTS는 접근 가능한 트리를 고정합니다. 경로는 검사 전에 심볼릭 링크가 해석되므로 링크가 이를 우회할 수 없습니다. 기본값은 제한 없음으로, 사용자가 직접 실행할 수 있는 CLI와 일치합니다 — 공유 또는 자동화된 배포에는 설정하세요.

보고서는 문서의 내용을 재현하는 대신 문서를 설명합니다(메타데이터, 개수, 신호). 문서 제목과 같은 보고서 값은 여전히 공격자가 제어하는 문자열입니다: 서버는 이를 JSON 데이터로 전달하고 지침에 삽입하지 않으므로, 소비자는 동일한 방식으로 취급해야 합니다.

취약점은 SECURITY.md에 설명된 대로 비공개로 신고하세요.

작동 방식

MCP client
    │  JSON-RPC over stdio
    ▼
deckprobe-mcp ── validates arguments, resolves the path, maps the result
    │  argv + stdout (one process per probe, or one --jsonl process per batch)
    ▼
DeckProbe engine ── plans the cheapest paths that answer the request

서버는 WebAssembly 빌드를 호출하는 대신 네이티브 DeckProbe CLI를 생성합니다. CLI는 프로브 계획에 필요한 바이트 범위만 읽는 반면, WebAssembly 경로는 전체 파일을 메모리에 보관하며, 별도의 OS 프로세스는 신뢰할 수 없는 파싱을 격리하고 완전히 종료할 수 있습니다. 엔진은 다음 순서로 선택됩니다:

  1. DECKPROBE_MCP_BIN

  2. 이 패키지의 @deckflow/deckprobe 종속성과 함께 제공되는 바이너리

  3. PATHdeckprobe

  4. 번들된 WebAssembly 엔진

해석된 엔진은 시작 시 stderr에 기록됩니다. stdout은 MCP 전송에 속하며 다른 것은 전달하지 않습니다.

MCP 서버 또는 에이전트 스킬?

DeckProbe는 셸을 사용할 수 있는 에이전트에게 CLI를 직접 사용하도록 가르치는 Agent Skill도 제공합니다. 둘 다 동일한 어휘를 가르칩니다. 에이전트에 셸이 있고 CLI의 전체 표면을 원하면 스킬을 사용하고, 셸이 없거나 엔진이 실행되기 전에 타입화된 인수의 유효성 검사를 원하면 이 서버를 사용하세요.

개발

npm install
npm test          # typecheck, lint, build, and the full suite
npm run test:watch

기여를 환영합니다 — CONTRIBUTING.md를 참조하세요. 거부된 대안을 포함한 설계 근거는 docs/rfc.md에 있습니다.

라이선스

MIT. LICENSE를 참조하세요.

Available Tools

4 tools
list_formatsList supported formatsA
Read-onlyIdempotent

List the document formats DeckProbe can inspect: drivers, the extensions each one handles, and where support stops. Call this when you are unsure whether a file type is supported at all.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable context about the exact output content (drivers, extensions, support limitations), which goes beyond the annotations. No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, with the primary purpose and scope front-loaded. It avoids redundancy and every sentence earns its place—the first states what it lists, the second when to call it. No unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, parameterless list tool with no output schema, the description fully explains what the tool returns (drivers, extensions, and support limits). There is nothing missing for an agent to correctly invoke it and interpret the result. Complexity is low, so this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema is fully covered (100% by default). There is nothing to add about parameters; the description doesn't need to explain any. The baseline for zero parameters is 4, and no additional info is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('List'), a clear resource ('document formats DeckProbe can inspect'), and specifies the content (drivers, extensions, and where support stops). It effectively distinguishes itself from sibling tools like probe and list_targets by focusing on format capabilities rather than probing or target listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: 'Call this when you are unsure whether a file type is supported at all.' It doesn't mention alternatives, but the trigger condition is clear and implies that if you have a specific file, you would use probe instead. It could be improved by naming the sibling tools explicitly, but the guidance is sufficient.

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

list_targetsList targets for a formatA
Read-onlyIdempotent

List every target a format supports, with its aliases, value type, minimum probe level, cost class, and which @selectors include it. Call this before naming a target you have not already seen — probe rejects an unknown one rather than guessing.

ParametersJSON Schema
NameRequiredDescriptionDefault
detailNocompact (default): target names, aliases, descriptions, levels, and selector membership. full: the complete report, including each target's JSON Schema fragment and every selector's expanded member list.
formatYesA format profile from list_formats, such as "pdf", "docx", "xlsx", "pptx", "doc", "xls", "ppt", "key", "numbers", or "pages".

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, covering safety. The description adds behavioral context by spelling out returned fields and the probe rejection behavior, which matters for planning calls. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences front-load the core purpose and output contents, then give one practical usage rule. No filler or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a rich annotation set, fully documented parameters, and a description that names the output fields and the prerequisite call, an agent has enough to invoke the tool correctly even with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already explains format and detail fully, including enumerations and examples. The description adds no new parameter-level semantics beyond naming the report contents, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (list) and resource (targets for a given format), enumerates exactly what is included (aliases, value type, minimum probe level, cost class, selector membership), and contrasts with probe by stating probe rejects unknown targets. This distinguishes it from sibling tools list_formats and probe.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs when to call: before naming a target not already seen, because probe rejects unknowns rather than guessing. It also ties the format parameter to list_formats, implying the prerequisite and differentiating from list_formats.

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

probeProbe a documentA
Read-onlyIdempotent

Read facts about one local PDF, Microsoft Office, or Apple iWork document without opening or rendering it: page, slide, and sheet counts; title, author, and dates; encryption, macro, signature, and active-content signals; structure; embedded assets; and integrity. Handles .pdf, .docx/.xlsx/.pptx, legacy .doc/.xls/.ppt, and modern .key/.numbers/.pages.

Start with targets ["@summary"], or ["@security"] to triage an untrusted file. Prefer this over unzipping the document or parsing its bytes by hand.

Reports facts ABOUT the document, never its text: it does not extract, render, run macros, follow external references, or open a network connection.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the document. The filename extension selects the format driver, and the container is then verified against it.
viewNoreport: full evidence — value, confidence, path, and cost per target. values: a compact target-to-value map.
levelNoProbe budget and eligible paths. header: identity only. metadata (default). deep: higher-cost paths, needed only when a target's min_level says so.
budgetNoOverride the level's resource limits. Raise after a BUDGET_EXCEEDED error.
targetsNoShort names (slide_count), canonical names (powerpoint.slide_count), or presets: @header, @summary, @security, @structure, @assets, @quality, @format, @all. Defaults to the driver's own set. Call list_targets rather than guessing a name. @summary omits statistics needing a full-file read, so ask for a PDF's page_count explicitly.
min_confidenceNoWeakest evidence a path may offer. Default high; lower it to accept an approximation.
target_confidenceNoPer-target override, e.g. {"slide_count": "exact"} to force the authoritative path.

Output Schema

ParametersJSON Schema
NameRequiredDescription
statusNo
schema_versionNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds critical behavioral context beyond those hints: it does not extract, render, run macros, follow external references, or open a network connection; it handles legacy formats; it can hit resource limits (referenced by 'Raise after a BUDGET_EXCEEDED error'). This is exactly the kind of non-obvious behavior an agent needs to trust the tool with untrusted files.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: opening scope sentence, format list, usage hint, and a final 'does not' sentence. It is front-loaded with the core purpose and scoping. It earns its sentences; only a minor redundancy exists (the 'Prefer this over...' sentence partially repeats the 'does not' list). Not a 5, but far above the typical terse definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 7 parameters, nested budget object, output schema present, and complex behavior (format drivers, confidence levels, presets, resource limits), the description provides substantial orientation: preset suggestions, the @summary caveat, the budget-error hint, and a clear non-extraction guarantee. The description does not explain what each target means, but it correctly defers to list_targets. So it's complete enough without being exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 7 parameters well. The description adds important cross-references that the schema alone does not capture: the distinction between @summary and full-read statistics, the warning that @summary omits page_count so ask explicitly, and that list_targets should be used rather than guessing names. This goes beyond a baseline 3 by explaining how the parameters interact with presets and the driver's default set.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Read facts about one local PDF, Microsoft Office, or Apple iWork document') plus a precise resource list (page/slide/sheet counts, title/author/dates, encryption, macros, structure, assets, integrity). The scope is explicit: it reports facts ABOUT the document, never extracts text, renders, runs macros, or opens connections. It also names sibling tools (list_targets, probe_batch) and distinguishes from unzipping/byte-parsing by hand. This is clear and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises 'Start with targets ["@summary"], or ["@security"] to triage an untrusted file.' and 'Prefer this over unzipping the document or parsing its bytes by hand.' It also warns about @summary omitting statistics and instructs to 'Call list_targets rather than guessing a name.' This provides direct when-to-use and when-not-to-use guidance, plus pointers to alternatives.

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

probe_batchProbe several documentsA
Read-onlyIdempotent

Probe many local documents in one call — inventory a folder, triage a batch of uploads, or compare a set of files. One engine process handles the whole batch, so this is much cheaper than calling probe once per file.

Takes literal paths; expand any glob yourself first. Returns one entry per path, in the order given, each carrying that file's report or its own error envelope. A file that fails does not affect the others.

Defaults to the compact "values" view because inventory rarely needs per-target evidence; pass view: "report" when it does.

ParametersJSON Schema
NameRequiredDescriptionDefault
viewNoreport: full evidence — value, confidence, path, and cost per target. values: a compact target-to-value map.
levelNoProbe budget and eligible paths. header: identity only. metadata (default). deep: higher-cost paths, needed only when a target's min_level says so.
pathsYesDocument paths, at most 64 per call. Literal paths only.
budgetNoOverride the level's resource limits. Raise after a BUDGET_EXCEEDED error.
targetsNoShort names (slide_count), canonical names (powerpoint.slide_count), or presets: @header, @summary, @security, @structure, @assets, @quality, @format, @all. Defaults to the driver's own set. Call list_targets rather than guessing a name. @summary omits statistics needing a full-file read, so ask for a PDF's page_count explicitly.
min_confidenceNoWeakest evidence a path may offer. Default high; lower it to accept an approximation.

Output Schema

ParametersJSON Schema
NameRequiredDescription
reportsYesOne entry per requested path, in order. A per-file failure is confined to it.

TDQS

A4.8/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: one engine process for the batch, per-path results in order, error envelopes per file, failure isolation, default view and when to switch. Annotations already cover read-only/idempotent/destructive, and the description does not contradict them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, zero fluff. The first sentence front-loads purpose and use cases; the second covers path handling and return behavior; the third gives view guidance. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters, nested objects, and a full output schema, the description covers the essential usage context: batch behavior, order of results, error isolation, and view default. It does not mention budget override or target selection, but those are adequately documented in the schema. The description is sufficient for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds valuable semantic guidance: 'Takes literal paths; expand any glob yourself first' clarifies the paths parameter, and the view default rationale ('inventory rarely needs per-target evidence') adds context beyond the schema. It does not elaborate on every parameter, but the key ones receive useful extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes many local documents in one call, listing concrete use cases (inventory a folder, triage a batch, compare files). It distinguishes itself from the sibling 'probe' by explicitly being the batch counterpart.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly explains when to use this tool ('Probe many local documents in one call'), gives practical scenarios, and contrasts cost efficiency ('much cheaper than calling probe once per file'). It also gives guidance on view selection ('pass view: report when it does').

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.

  1. 4 tool updatesv0.1.1
    • First observedlist_formats
    • First observedlist_targets
    • First observedprobe
    • First observedprobe_batch

TDQS

A4.7/5.0
Disambiguation5/5

Each tool has a wholly distinct purpose: probe handles a single file, probe_batch handles multiples, list_formats enumerates supported file types, and list_targets enumerates probe targets. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tools follow a consistent lowercase_snake_case convention with clear verb prefixes: probe, probe_batch, list_formats, list_targets. The naming style is uniform and predictable, making the tool set easy to reason about.

Tool Count5/5

With exactly 4 tools, the server is tightly scoped for its purpose of document inspection. Each tool fills a necessary role (single file, batch, format discovery, target discovery) without redundancy or bloat, fitting well within the ideal 3–15 range.

Completeness5/5

The surface covers all core workflows: probing an individual file, probing many files efficiently, discovering supported formats, and enumerating probe targets for a given format. There are no obvious dead ends or missing operations for the server's stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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