file-analysis-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@file-analysis-mcpSummarize the documents in the docs folder and analyze the file structure"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
file-analysis-mcp
"Read all the pdf/docx/pptx/svg/png documents in this folder and summarize them." A personal MCP server that lets you ask Claude Code to do this with a single sentence.
Point it at a folder and it opens the documents inside and returns just the raw text. Summarizing and interpreting is done by the Claude that calls this server during the conversation — not by the server itself. So it runs fully locally and privately, with no separate LLM API key.
Table of Contents
Related MCP server: file-analyzer
How it actually works
This is the result of asking Claude Code to look at a real folder (a folder containing internal PRD documents).
Me: Summarize the documents in the
docsfolder and analyze the file structure
Claude calls analyze_folder_structure → analyze_folder in sequence, then answers based on the raw text it received.
[analyze_folder_structure] pdf 2개, 총 455KB, 최대 파일 prd.pdf(231KB)
[analyze_folder] 2개 파일 모두 추출 성공 (succeeded: 2, failed: 0)
→ Claude의 요약:
두 문서는 같은 프로젝트("ParameterCheck")의 서로 다른 산출물입니다.
- proposal.pdf: RF솔루션 파트의 CAB 파라미터 검증 자동화 기획서
- prd.pdf: 같은 아이디어의 정식 PRD. 목표는 검증 시간 10분→5분 단축,
MVP는 "파라미터 엑셀 입력 시 집계시트 자동 생성 + 오류 빨강 표시"
공통점: 사람 검토·승인은 항상 최종 단계로 남겨둔다는 원칙이 두 문서 모두에 명시됨.The server only passed the text — every sentence in the summary above was written by Claude. That is the division of labor this project aims for.
Quick start
# 1) 의존성 설치 (uv 사용)
uv sync --extra dev
# 2) 확인
uv run pytest # 유닛테스트
uv run python scripts/smoke_stdio.py # 실제 MCP 프로토콜로 서버를 띄워보는 스모크 테스트If you need OCR (PNG analysis), install Tesseract-OCR separately — instructions are in the earlier part of Registering with Claude Code below. The other 4 tools work fine even before you install it.
The 5 tools
Tool | Description | Guardrail |
| Returns the list of target files in a folder (path/size/modified date) and counts by extension | If |
| Returns the tree structure including subfolders, statistics by extension, size, and the list of largest files | Only the tree is capped by |
| Extracts the text of a single pdf/docx/pptx/svg document | Truncates at |
| Reads a single png image via OCR and extracts the text | Same as above + if OCR returns nothing, explains why via next_actions |
| Extracts all target files in a folder at once and returns them as a report (no need to call multiple times) | If |
All tools are read-only and never modify or delete files. Even when a limit (max_files) is hit, files are not silently dropped — the response records exactly how many were skipped, and status becomes PARTIAL so you can tell right away.
Registering with Claude Code
Installing the OCR engine (only needed for PNG analysis)
pytesseract is just a Python binding for the Tesseract-OCR engine; the engine itself must be installed separately.
Download the UB-Mannheim Tesseract installer and install it on Windows. (If you need Korean recognition, check Korean under "Additional language data" during installation.)
Add the install path (default
C:\Program Files\Tesseract-OCR) to your system PATH.Verify the installation with
tesseract --version.
Registering the server
This repository already has a .mcp.json prepared at the root. Running Claude Code from the file-analysis-mcp folder (or a parent folder) will pick it up automatically. After restarting, check that the 5 file-analysis tools appear in the /mcp command or the tool list.
To register manually:
claude mcp add file-analysis -- uv --directory "C:\Users\20223\Desktop\file-analysis-mcp" run python src/file_analysis_mcp/server.pyRecommended flow: first understand the structure with analyze_folder_structure → batch-extract all document text with analyze_folder → Claude summarizes based on the extracted text.
Project structure
file-analysis-mcp/
├── pyproject.toml
├── .mcp.json
├── src/file_analysis_mcp/
│ ├── server.py # FastMCP 서버, 도구 5개
│ ├── harness.py # 응답/오류 계약 (BaseResponse, ToolFailure 등)
│ ├── scanner.py # 폴더 스캔/구조 분석
│ └── extractors/ # pdf/docx/pptx/svg/image 텍스트 추출기
├── scripts/smoke_stdio.py
├── tests/
│ ├── test_scanner.py # 도메인 로직(순수 함수) 유닛테스트
│ ├── test_extractors.py # 포맷별 추출기 유닛테스트
│ └── test_server_contract.py # 하네스 규약(도구 계약) 테스트
└── data/sample_docs/ # 테스트용 샘플 문서Design principle: harness engineering
This server prioritizes making it so the model can tell from the response alone what to do next over "adding more features." Of the principles introduced in awesome-harness-engineering, it selectively applies only the ones that actually fit this project's nature — local, single-user, read-only. (OpenTelemetry observability, prompt-injection sandboxing, and mcp-guardian-style scope-approval gating are meant for multi-user, long-running agents, so they would be overkill for a personal tool of this scale and were not applied.)
What was applied | Its form in this project |
Clear tool boundaries | Each tool docstring states its purpose + Returns + "use / don't use" examples, so the model picks the right one among the 5 tools |
Next-action guidance | Every response includes |
Actionable errors |
|
Context economy (per response) |
|
Context economy (guardrails) |
|
Keeping useful failures in context |
|
No silent loss | Even when a limit is exceeded, files are not quietly skipped — |
Tool contract tests |
|
Deliberately not applied
Summarization/grounding features: This server is designed to "extract only" (summarizing is the host model's job), so these do not apply.
Line-number citation anchors (
L12 | ...): Without a separate tool that verifies cited evidence, this would only clutter the text, so it was not applied. If it ever becomes necessary, it can be added by reusingharness.number_lines().Approval-token-based save workflows: This server does not write files, so it does not apply.
Available Tools
5 toolsanalyze_folderARead-onlyIdempotent
폴더 안의 모든 대상 파일(pdf/docx/pptx/svg/png)을 한 번에 추출해 리포트로 반환합니다.
파일마다 read_document/read_image_text를 따로 호출하지 않아도 되는 배치 도구입니다. 한 파일이 실패해도 나머지 파일 처리는 계속되며, 실패 이유는 파일별로 남습니다. 파일 수가 max_files를 넘으면 앞쪽부터 max_files개만 처리하고, 넘겨받지 못한 개수는 skipped_due_to_limit로 투명하게 알려줍니다(조용히 누락되지 않습니다).
Returns:
AnalyzeFolderResponse: reports[]의 각 항목은 status(OK/ERROR)와 함께
text 또는 error를 담습니다. status가 PARTIAL이면 일부 실패했거나(failed>0)
상한 때문에 스킵된 파일(skipped_due_to_limit>0)이 있다는 뜻입니다.
Examples: - 사용: "이 폴더 문서들 다 요약해줘", "폴더 구조 분석하고 내용도 정리해줘" - 사용하지 않음: 파일 목록만 필요할 때 → scan_folder - 사용하지 않음: 특정 파일 하나만 자세히 → read_document / read_image_text
| Name | Required | Description | Default |
|---|---|---|---|
| max_files | No | 이번 호출에서 실제로 추출을 시도할 최대 파일 수(컨텍스트/시간 보호용 상한). 초과분은 시도조차 하지 않고 skipped_due_to_limit로 개수를 알려줍니다. | |
| recursive | No | 하위 폴더까지 재귀적으로 탐색할지 여부 | |
| extensions | No | 필터링할 확장자 목록. 생략하면 pdf/docx/pptx/svg/png 전체를 대상으로 함 | |
| folder_path | Yes | 분석할 폴더의 절대 경로 | |
| max_chars_per_file | No | 파일당 반환할 최대 문자 수(컨텍스트 보호용 절단) |
Output Schema
| Name | Required | Description |
|---|---|---|
| failed | Yes | |
| folder | Yes | |
| status | Yes | OK 또는 PARTIAL 등 처리 결과 상태 |
| reports | Yes | |
| succeeded | Yes | |
| total_files | Yes | 실제로 추출을 시도한 파일 수 (= len(reports)) |
| next_actions | No | |
| total_matched | Yes | 조건에 맞는 전체 파일 수(상한 적용 전) |
| skipped_due_to_limit | Yes | max_files 상한 때문에 시도조차 하지 않은 파일 수 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description goes beyond by revealing that it is a batch tool that continues processing on per-file errors, transparently reports skipped files via skipped_due_to_limit, and returns statuses (OK/ERROR/PARTIAL). This adds significant behavioral context not present in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured: main purpose first, followed by batch/error/limit behavior, then return format, then examples. It is somewhat lengthy but every section adds value, including explicit '사용' and '사용하지 않음' examples. The front-loading of the core purpose is effective.
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 complexity (5 parameters, output schema present), the description covers all essential aspects: batch semantics, failure handling, limit transparency, return structure, and usage alternatives. It also includes practical example prompts. Nothing an agent needs to correctly invoke the tool is missing.
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 provides complete descriptions for all 5 parameters (100% coverage), so the baseline is 3. The description does not add new meaning to parameters beyond repeating max_files' skipped_due_to_limit behavior, which is already in the schema. No additional semantic value is provided.
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 extracts all target files (pdf/docx/pptx/svg/png) in a folder and returns a report. It explicitly distinguishes itself from siblings: scan_folder (file list only) and read_document/read_image_text (single file). The verb '추출' and resource '폴더' make the purpose specific and unambiguous.
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 explicitly states when to use the tool (batch extraction, folder document summarization) and when not to use it, naming the alternatives: scan_folder for file lists and read_document/read_image_text for individual files. It also clarifies behavior under max_files limit and partial failures, leaving no ambiguity about invocation context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
analyze_folder_structureARead-onlyIdempotent
폴더의 하위 디렉터리 트리와 확장자별 통계, 용량, 최대 파일을 분석합니다.
scan_folder보다 한 단계 더 깊은 "구조" 파악용 도구입니다. 폴더가 어떻게 구성돼 있는지(하위 폴더별 파일 분포, 가장 큰 파일 등)를 먼저 감 잡고 싶을 때 사용하세요.
Returns:
FolderStructureResponse: tree는 {"하위폴더명": {...}, "_files": [...]}
형태의 중첩 딕셔너리입니다. 파일이 max_files보다 많으면 tree에는 앞쪽
max_files개만 반영되고 list_truncated=True가 되지만, total_files/
by_extension/total_size_bytes 등 통계는 항상 전체 파일 기준입니다.
Examples: - 사용: "이 폴더 구조 좀 분석해줘", "가장 큰 파일이 뭐야?" - 사용하지 않음: 파일 목록만 빠르게 보고 싶을 때 → scan_folder
| Name | Required | Description | Default |
|---|---|---|---|
| max_files | No | 트리(tree)에 포함할 최대 파일 수(컨텍스트 보호용 상한). 통계(개수/용량 등)는 상한과 무관하게 항상 전체 기준입니다. | |
| extensions | No | 필터링할 확장자 목록. 생략하면 pdf/docx/pptx/svg/png 전체를 대상으로 함 | |
| folder_path | Yes | 분석할 폴더의 절대 경로 |
Output Schema
| Name | Required | Description |
|---|---|---|
| tree | Yes | |
| folder | Yes | |
| status | Yes | OK 또는 PARTIAL 등 처리 결과 상태 |
| max_depth | Yes | |
| total_files | Yes | 조건에 맞는 전체 파일 수(통계는 항상 전체 기준) |
| by_extension | Yes | |
| next_actions | No | |
| largest_files | Yes | |
| list_truncated | Yes | 파일이 너무 많아 tree에는 max_files개까지만 반영됐으면 True |
| total_size_bytes | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds crucial behavioral detail beyond that: it explains that the tree includes only up to max_files entries with list_truncated flag, while statistics are always based on the full file set. It also details the default extension set when no filter is given. No contradiction with annotations; this transparency is valuable.
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 well-organized: opens with the core purpose, then a usage hint, a Returns section, and examples. Each section contributes to understanding, though it is slightly longer than strictly necessary. The structure is front-loaded with the main purpose and comparison, making it easy to skim. Optimally concise for the complexity it covers.
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 an output schema present and safety annotations provided, the description still adds essential behavioral details (truncation semantics, stats being global) and usage examples. It covers the necessary context for correct invocation, including how max_files affects results and the default extension pool. No missing critical information for typical usage.
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%, with each parameter already having detailed descriptions (folder_path as absolute path, max_files with truncation explanation, extensions with default set). The tool description does not add supplementary meaning beyond the schema; it neither repeats nor enhances parameter semantics, 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?
The description clearly states it analyzes the folder's subdirectory tree, extension statistics, size, and largest files, with a specific verb and resource. It distinguishes itself from scan_folder by noting it goes one level deeper into structure. However, it does not differentiate from the similarly named sibling analyze_folder, which could create ambiguity about which tool to use.
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 explicit guidance: use it when you need an understanding of folder organization (subfolder distribution, largest files) and explicitly says not to use it when you only need a quick file list, directing to scan_folder. However, it omits any comparison with analyze_folder, another sibling that might be a relevant alternative, leaving some usage ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_documentARead-onlyIdempotent
문서 파일(pdf/docx/pptx/svg) 하나의 텍스트를 추출합니다. 요약은 하지 않습니다.
scan_folder/analyze_folder에서 찾은 파일 중 하나를 더 자세히 보고 싶을 때, 또는 analyze_folder의 일괄 요약이 잘렸을 때(truncated=True) 이어서 읽을 때 씁니다.
Returns:
ReadDocumentResponse: text에 원문(잘렸으면 앞부분)이 담깁니다.
truncated=True면 max_chars를 늘려 다시 호출해 나머지를 볼 수 있습니다.
Examples: - 사용: "이 pdf 파일 내용 좀 자세히 보여줘" - 사용하지 않음: png 이미지 → read_image_text - 사용하지 않음: 폴더 전체를 한 번에 훑고 싶을 때 → analyze_folder
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | 읽을 문서의 절대 경로 (pdf, docx, pptx, svg) | |
| max_chars | No | 반환할 최대 문자 수(컨텍스트 보호용 절단) |
Output Schema
| Name | Required | Description |
|---|---|---|
| path | Yes | |
| text | Yes | |
| status | Yes | OK 또는 PARTIAL 등 처리 결과 상태 |
| metadata | Yes | |
| extension | Yes | |
| truncated | Yes | |
| next_actions | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it returns the original text (possibly truncated), and explains the truncation contract (increase max_chars to retrieve the rest). This goes beyond annotations and clarifies the tool's output semantics.
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 well-structured with a clear statement of purpose, usage context, return details, and examples. It is slightly lengthy but every section earns its place, and the core purpose is front-loaded. Minor redundancy with the annotation 'readOnlyHint' doesn't detract significantly.
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 output schema exists and the tool is simple, the description covers all necessary aspects: what it does, when to use it, how output is truncated, and how to recover full text. The examples reinforce correct usage. Nothing critical is missing for an agent to call it 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 description coverage is 100%, so parameters are fully documented. The description reinforces the purpose of max_chars in the truncation note but doesn't add new semantic details beyond the schema. Baseline 3 is appropriate when the schema carries the full 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 clearly states the tool extracts text from document files (pdf/docx/pptx/svg) and explicitly notes it does not summarize. It differentiates from siblings by naming the specific use case (inspecting files from scan_folder/analyze_folder) and alternative tools (read_image_text, analyze_folder), making the purpose unmistakable.
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 explicit when-to-use (detailed look at a found file, or continuing a truncated analyze_folder summary) and when-not-to-use (PNG images -> read_image_text, folder-wide scanning -> analyze_folder). Also gives a concrete example of user phrasing, leaving no ambiguity about when this tool should be selected.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_image_textARead-onlyIdempotent
PNG 이미지 하나를 OCR로 읽어 텍스트를 추출합니다(Tesseract-OCR 필요).
스크린샷, 스캔한 메모, 다이어그램에 박힌 라벨처럼 이미지 안의 문자를 읽어야 할 때 사용하세요. 순수 그림/사진처럼 텍스트가 없는 이미지는 빈 결과가 나올 수 있습니다(비전 해석이 아니라 OCR이므로 그림 자체의 의미는 파악하지 못합니다).
Returns:
ReadImageTextResponse: text에 인식된 문자열이 담깁니다. 결과가 비어
있으면 next_actions에서 그 이유를 안내합니다.
Examples: - 사용: "이 스크린샷에 뭐라고 적혀 있어?" - 사용하지 않음: pdf/docx/pptx/svg 문서 → read_document
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes | OCR로 읽을 PNG 이미지의 절대 경로 | |
| max_chars | No | 반환할 최대 문자 수(컨텍스트 보호용 절단) |
Output Schema
| Name | Required | Description |
|---|---|---|
| path | Yes | |
| text | Yes | |
| status | Yes | OK 또는 PARTIAL 등 처리 결과 상태 |
| metadata | Yes | |
| truncated | Yes | |
| next_actions | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses important behavioral traits: it requires Tesseract-OCR, it is OCR not vision (so it does not understand image meaning), and it may return empty results for text-less images, with next_actions explaining why. This adds significant value over annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the core purpose, followed by usage guidelines, return details, and examples. It is slightly verbose with the examples, but each section carries useful information without redundancy.
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 OCR tool, the description covers purpose, usage, limitations, and dependencies. The output schema is mentioned (though not shown) and the max_chars truncation is addressed. The only minor gap is that it could specify what happens on file-not-found or unsupported formats, but these are likely covered elsewhere.
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%, with both parameters clearly described (file_path as absolute path, max_chars as truncation limit). The description itself does not add further parameter detail, so per the calibration baseline of 3 for high coverage, this score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the exact verb and resource: 'Reads a PNG image with OCR to extract text' and explicitly distinguishes it from read_document for document formats. It clearly states the tool's scope and what it does not do.
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 explicitly states when to use the tool ('for screenshots, scanned notes, labels on diagrams') and when not to use it ('for pdf/docx/pptx/svg documents → read_document'), providing clear exclusions and routing to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_folderARead-onlyIdempotent
폴더 안의 대상 파일(pdf/docx/pptx/svg/png) 목록과 확장자별 개수를 조회합니다.
문서 내용을 읽지 않고 메타데이터(경로/크기/수정일)만 가볍게 훑어보는 도구입니다. 폴더 안에 어떤 파일이 몇 개 있는지 빠르게 확인하고 싶을 때 사용하세요.
Returns:
ScanFolderResponse: files[]에 경로/이름/확장자/크기/수정일이 담깁니다.
파일이 max_files보다 많으면 list_truncated=True이고, by_extension의
개수는 상한과 무관하게 항상 전체 매칭 파일 기준입니다.
Examples: - 사용: "이 폴더에 어떤 문서들이 있어?", "pdf 몇 개야?" - 사용하지 않음: 파일 내용까지 요약하고 싶을 때 → analyze_folder - 사용하지 않음: 하위 폴더 구조/통계까지 보고 싶을 때 → analyze_folder_structure
| Name | Required | Description | Default |
|---|---|---|---|
| max_files | No | 목록에 포함할 최대 파일 수(컨텍스트 보호용 상한). 초과분은 스킵되며 list_truncated=True로 표시됩니다(개수 통계는 항상 전체 기준). | |
| recursive | No | 하위 폴더까지 재귀적으로 탐색할지 여부 | |
| extensions | No | 필터링할 확장자 목록(예: ['.pdf', '.png']). 생략하면 pdf/docx/pptx/svg/png 전체를 대상으로 함 | |
| folder_path | Yes | 스캔할 폴더의 절대 경로 |
Output Schema
| Name | Required | Description |
|---|---|---|
| files | Yes | |
| folder | Yes | |
| status | Yes | OK 또는 PARTIAL 등 처리 결과 상태 |
| total_files | Yes | 실제 반환된 파일 수 (= len(files)) |
| by_extension | Yes | 상한과 무관하게 전체 매칭 파일 기준 확장자별 개수 |
| next_actions | No | |
| total_matched | Yes | 조건에 맞는 전체 파일 수(상한 적용 전) |
| list_truncated | Yes | total_matched > total_files이면 True. 목록이 잘렸다는 뜻 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations already declare readOnlyHint=true and idempotentHint=true, the description adds crucial behavioral context beyond this: it explicitly states that document contents are NOT read, only metadata (path/size/modified date). It also discloses truncation behavior (list_truncated flag, by_extension always full). These details inform an agent about side-effects and guarantees that annotations alone do not convey.
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 well-organized with a clear first sentence stating the core purpose, followed by a behavioral note, return format details, and usage examples. It is slightly verbose (multiple paragraphs) but every section earns its place, front-loading the key action and scoping. It could be tightened, but structure is strong.
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 moderate complexity (4 parameters, output schema, multiple siblings), the description covers all essential aspects: what it returns (files array fields and by_extension counts), truncation edge-case behavior, and guidance on when to use alternatives. With an output schema present, the description need not detail return types, yet it already does so sufficiently. No critical information is missing 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?
Input schema has 100% description coverage, so the baseline is 3. The description adds marginal value by reinforcing that max_files truncates the list but not the by_extension counts, which is already implied in the schema's max_files description. Since the schema documents all parameters fully, the description does not meaningfully augment parameter semantics beyond what is already structured.
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: it lists target files (pdf/docx/pptx/svg/png) in a folder and provides counts by extension. It explicitly distinguishes itself from siblings by stating it reads only metadata, not content, and names specific alternatives (analyze_folder, analyze_folder_structure) with conditions for their use. This makes the tool's purpose unmistakable and differentiates it from the four siblings.
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 explicit usage guidance: use when you want to quickly check which documents exist and how many there are, with example queries. It also gives clear exclusions: do not use for content summarization (use analyze_folder) or for subfolder structure/statistics (use analyze_folder_structure). These concrete when-to-use and when-not-to-use instructions leave no ambiguity 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.
5 tool updates
v0.1.0- First observed
analyze_folder - First observed
analyze_folder_structure - First observed
read_document - First observed
read_image_text - First observed
scan_folder
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: read_document handles individual text documents, scan_folder lists metadata, analyze_folder_structure provides hierarchical stats, read_image_text does OCR, and analyze_folder batch-processes all files. Descriptions explicitly state what not to use each tool for, reinforcing boundaries.
All tool names follow a consistent snake_case verb_noun pattern (read_document, scan_folder, analyze_folder_structure, read_image_text, analyze_folder). The verbs are distinct and descriptive, and the pattern is uniform across the set.
With 5 tools, the server is well-scoped for file and folder analysis. Each tool covers a distinct aspect (single read, metadata scan, structure analysis, OCR, batch processing) without redundancy or missing essentials, making the count ideal for its purpose.
The tool surface covers the full lifecycle of file analysis: listing, detailed reading (both text and OCR), structural analysis, and batch processing. Error handling and truncation are addressed. No obvious gaps for the stated domain, such as missing file deletion or modification, which are out of scope.
Maintenance
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Read PDFs and images as markdown or text, with exact costs and hard spend caps. $0.75/1k pages.
Search and reason over your Obsidian-style Markdown vault, right from ChatGPT.
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