Deep Thinking LLMを活用し、自然なフォーマット、文脈に応じた修正、スマートな要約機能を備えた、インテリジェントな文字起こし処理機能を提供するMCPサーバーです。
このMCPサーバーは、文字起こし処理のための4つの強力なツールを提供します：
repair_text - 90%以上の信頼度で文字起こしのエラーを分析・修正します
get_repair_log - 前回の修正から詳細な分析ログを取得します
format_transcript - タイムスタンプ付きの文字起こしを自然な形式のテキストに変換します
summary_text - ACE認知手法を用いてインテリジェントな要約を生成します
Related MCP server: Speak AI MCP Server
Smithery を介してClaude Desktop用のTranscription Toolsを自動的にインストールするには：
npx -y @smithery/cli install @MushroomFleet/transcriptiontools-mcp --client claudeこのリポジトリをクローンします：
git clone https://github.com/mushroomfleet/TranscriptionTools-MCP
cd TranscriptionTools-MCP依存関係をインストールします：
npm installサーバーをビルドします：
npm run buildMCP設定ファイルでMCPサーバーを設定します：
{
"mcpServers": {
"transcription-tools": {
"command": "node",
"args": ["/path/to/TranscriptionTools-MCP/build/index.js"],
"disabled": false,
"autoApprove": []
}
}
}<use_mcp_tool>
<server_name>transcription-tools</server_name>
<tool_name>repair_text</tool_name>
<arguments>
{
"input_text": "We recieve about ten thousand dollars which is defiantly not enough.",
"is_file_path": false
}
</arguments>
</use_mcp_tool><use_mcp_tool>
<server_name>transcription-tools</server_name>
<tool_name>format_transcript</tool_name>
<arguments>
{
"input_text": "/path/to/timestamped-transcript.txt",
"is_file_path": true,
"paragraph_gap": 8,
"line_gap": 4
}
</arguments>
</use_mcp_tool><use_mcp_tool>
<server_name>transcription-tools</server_name>
<tool_name>summary_text</tool_name>
<arguments>
{
"input_text": "Long text to summarize...",
"is_file_path": false,
"constraint_type": "words",
"constraint_value": 100
}
</arguments>
</use_mcp_tool><use_mcp_tool>
<server_name>transcription-tools</server_name>
<tool_name>get_repair_log</tool_name>
<arguments>
{
"session_id": "20241206143022"
}
</arguments>
</use_mcp_tool>話し方のパターンを維持しながらタイムスタンプを削除
休止時間に基づいたインテリジェントな間隔の適用
自然な文法と言語の流れを尊重
文字起こしされたコンテンツを正確に保持
文字起こしの可能性が高いエラーを特定して修正
高い信頼度での修正のために意味的な文脈を使用
すべての変更の詳細なログを保持
修正には90%の信頼度しきい値を設定
元の音声データは不要
処理された文字起こしの簡潔な要約を作成
複数の制約タイプをサポート：
時間ベース（発話時間）
文字数
単語数
重要な情報と文脈を保持
自然な話し方のリズムを維持
/
├── .gitignore # Git ignore file
├── LICENSE # MIT license file
├── README.md # This documentation
├── package.json # Package dependencies and scripts
├── tsconfig.json # TypeScript configuration
├── build/ # Compiled JavaScript files (generated after build)
│ ├── tools/ # Compiled tool implementations
│ └── utils/ # Compiled utility functions
└── src/ # Source TypeScript files
├── index.ts # MCP server entry point
├── tools/ # Tool implementations
│ ├── formatting.ts
│ ├── repair.ts
│ └── summary.ts
└── utils/ # Utility functions
├── file-handler.ts
└── logger.tsソースコードを直接変更することで、サーバーの動作をカスタマイズできます。主要な設定パラメータは、各ツールの実装ファイル内にあります：
// In src/tools/formatting.ts
const paragraph_gap = 8; // seconds
const line_gap = 4; // seconds
// In src/tools/repair.ts
const confidence_threshold = 90; // percentage
// In src/tools/summary.ts
const default_speaking_pace = 150; // words per minuteMIT
TranscriptionTools-MCP — 文字起こし処理
DeepLucid3D-MCP — 認知処理
UNO-MCP — ナラティブ強化
gitea-mcp — Gitea統合
zero-vector-MCP — 手続き型生成
Transforms timestamped transcripts into naturally formatted text
| Name | Required | Description | Default |
|---|---|---|---|
| input_text | Yes | Timestamped transcript text or path to file | |
| is_file_path | No | Whether input_text is a file path | |
| line_gap | No | Seconds gap for line breaks | |
| paragraph_gap | No | Seconds gap for paragraph breaks |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the transformation action but lacks details on permissions, rate limits, output format, or error handling. For a tool with no annotations, this is insufficient to inform the agent about behavioral traits beyond the basic operation.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized, making it easy for an agent to parse quickly.
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 no annotations and no output schema, the description is incomplete. It doesn't explain the return values or behavioral aspects like what 'naturally formatted text' entails. For a transformation tool with 4 parameters, more context is needed to guide the agent effectively.
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 the schema fully documents all parameters. The description doesn't add any meaning beyond what the schema provides, such as explaining how 'line_gap' and 'paragraph_gap' affect formatting. With high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 purpose: 'Transforms timestamped transcripts into naturally formatted text.' It specifies the verb ('transforms') and resource ('timestamped transcripts'), and the outcome ('naturally formatted text'). However, it doesn't differentiate from sibling tools like 'repair_text' or 'summary_text', which might also process text, so it misses full sibling distinction.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'repair_text' or 'summary_text', nor does it specify contexts or exclusions for usage. This leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves detailed analysis log from previous repair operation
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | Session ID or timestamp from previous repair |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves a log, implying a read-only operation, but doesn't disclose critical traits such as whether it requires specific permissions, how data is returned (e.g., format, pagination), error handling, or any rate limits. This is a significant gap for a tool with no annotation coverage.
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, efficient sentence that front-loads the core purpose ('Retrieves detailed analysis log') with no wasted words. It's appropriately sized for a simple tool with one parameter, making it easy for an agent to parse quickly.
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 (low, with one parameter) but lack of annotations and output schema, the description is incomplete. It doesn't explain what the retrieved log contains, its format, or any behavioral aspects, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, fully documenting the single parameter 'session_id' as 'Session ID or timestamp from previous repair'. The description adds no additional parameter semantics beyond this, so it meets the baseline score of 3 where the schema does the heavy lifting.
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 purpose with a specific verb ('Retrieves') and resource ('detailed analysis log from previous repair operation'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'format_transcript' or 'repair_text', which might also involve repair-related operations, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that a repair operation must have occurred), exclusions, or how it relates to sibling tools like 'repair_text' or 'summary_text', leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Analyzes and repairs transcription errors with greater than 90% confidence
| Name | Required | Description | Default |
|---|---|---|---|
| input_text | Yes | Text content or path to file containing transcribed text | |
| is_file_path | No | Whether input_text is a file path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'Analyzes and repairs' and includes a confidence metric ('greater than 90% confidence'), which hints at reliability but lacks details on error handling, side effects, permissions, rate limits, or response format. For a tool with no annotations, this is insufficient to understand its operational behavior.
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, efficient sentence that front-loads the core functionality ('Analyzes and repairs transcription errors') and adds a useful performance detail ('with greater than 90% confidence'). There is no wasted language or redundancy, making it highly concise and well-structured.
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 complexity of a repair tool with no annotations and no output schema, the description is incomplete. It lacks information on what the tool returns (e.g., repaired text, error logs), how it handles failures, or any behavioral constraints. The confidence metric is helpful but insufficient for full contextual understanding, especially for a tool that likely involves mutation or analysis.
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 the schema fully documents the two parameters ('input_text' and 'is_file_path'). The description adds no parameter-specific information beyond what the schema provides, such as examples of text content or file paths. With high schema coverage, the baseline score is 3, as the description does not compensate but also doesn't detract.
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 purpose with a specific verb ('Analyzes and repairs') and resource ('transcription errors'), and includes a performance metric ('greater than 90% confidence'). It distinguishes from sibling tools like 'format_transcript' (which likely formats rather than repairs) and 'summary_text' (which summarizes rather than repairs), though it doesn't explicitly mention these distinctions. The purpose is not vague or tautological.
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 no guidance on when to use this tool versus alternatives. It does not mention when to choose 'repair_text' over sibling tools like 'format_transcript' or 'get_repair_log', nor does it specify prerequisites, exclusions, or contextual cues for usage. The agent must infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates intelligent summaries using ACE cognitive methodology
| Name | Required | Description | Default |
|---|---|---|---|
| constraint_type | No | Type of constraint to apply | |
| constraint_value | No | Value for the specified constraint | |
| input_text | Yes | Text to summarize or path to file | |
| is_file_path | No | Whether input_text is a file path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'intelligent summaries' and 'ACE cognitive methodology' but doesn't explain what this methodology entails, how summaries are generated, whether there are rate limits, quality expectations, or what the output format looks like. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that gets straight to the point: 'Generates intelligent summaries using ACE cognitive methodology'. There's no fluff or redundant information. However, it could be slightly more front-loaded by specifying the resource (e.g., 'text' or 'documents') immediately.
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 (summarization with constraints and file input options), no annotations, and no output schema, the description is insufficient. It doesn't explain the ACE methodology, output format, error conditions, or usage scenarios. For a 4-parameter tool with behavioral unknowns, this leaves too many gaps for effective agent use.
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 the schema already documents all 4 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema—it doesn't explain how constraint_type/value interact with summarization, or provide examples of input_text formats. With high schema coverage, the baseline is 3 even without param details in the 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 clearly states the tool's purpose: 'Generates intelligent summaries' with the specific methodology 'using ACE cognitive methodology'. It distinguishes itself from siblings like format_transcript, get_repair_log, and repair_text by focusing on summarization rather than formatting, logging, or repair. However, it doesn't specify what resource it summarizes (text vs. documents), which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer summary_text over format_transcript for processing transcripts, or when summarization is appropriate versus repair_text for text correction. There's no context about prerequisites, constraints, or typical use cases beyond the basic purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0format_transcriptget_repair_logrepair_textsummary_textEach tool has a clearly distinct purpose: format_transcript transforms raw transcripts, repair_text fixes errors, summary_text creates summaries, and get_repair_log retrieves logs. There is no overlap in functionality, making tool selection unambiguous.
Three tools follow a consistent verb_noun pattern (format_transcript, repair_text, summary_text), but get_repair_log uses a verb_object pattern, deviating slightly. The naming is still highly readable and mostly predictable.
With 4 tools, the server is well-scoped for transcription processing. Each tool serves a distinct and essential function, and the count is appropriate for the domain without being too sparse or bloated.
The tools cover core transcription workflows: formatting, repairing, summarizing, and logging. Minor gaps might include operations like batch processing or exporting, but the set supports key agent tasks effectively.
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