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MCP File Context Server

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ファイルコンテキストサーバー

鍛冶屋のバッジ

大規模言語モデル（LLM）にファイルシステムコンテキストを提供するモデルコンテキストプロトコル（MCP）サーバー。このサーバーにより、LLMは高度なキャッシュ機能とリアルタイムファイル監視機能を利用して、コードファイルの読み取り、検索、分析を行うことができます。

特徴

  • ファイル操作

    • ファイルとディレクトリの内容を読み取る

    • 詳細なメタデータを含むファイルを一覧表示する

    • リアルタイムのファイル監視とキャッシュ無効化

    • 複数のファイルエンコーディングのサポート

    • 再帰的なディレクトリトラバーサル

    • ファイルタイプのフィルタリング

  • コード分析

    • 循環的複雑度の計算

    • 依存関係の抽出

    • コメント分析

    • 品質指標:

      • 重複行検出

      • 長い行の検出（100文字以上）

      • 複雑な機能の識別

      • 行数（合計、空でない行、コメント）

  • スマートキャッシング

    • LRU（最長時間未使用）キャッシュ戦略

    • ファイル変更時の自動キャッシュ無効化

    • 設定可能な制限を備えたサイズを考慮したキャッシュ

    • キャッシュ統計とパフォーマンスメトリック

    • 効率的な検索のための最終読み取り結果のキャッシュ

  • 詳細検索

    • 正規表現パターンマッチング

    • 設定可能な周囲の線によるコンテキスト認識結果

    • ファイルタイプのフィルタリング

    • マルチパターン検索のサポート

    • キャッシュされた結果の検索

    • 除外パターン

Related MCP server: TokenScope

インストール

Smithery経由でインストール

Smithery経由で Claude Desktop の File Context Server を自動的にインストールするには:

npx -y @smithery/cli install @bsmi021/mcp-file-context-server --client claude

手動インストール

npm install @modelcontextprotocol/file-context-server

使用法

サーバーの起動

npx file-context-server

利用可能なツール

  1. リストコンテキストファイル

    • 詳細なメタデータを含むディレクトリ内のファイルを一覧表示します

    {
  "path": "./src",
  "recursive": true,
  "includeHidden": false
}

  2. 読み取りコンテキスト

    • メタデータを含むファイルまたはディレクトリの内容を読み取ります

    {
  "path": "./src/index.ts",
  "encoding": "utf8",
  "maxSize": 1000000,
  "recursive": true,
  "fileTypes": ["ts", "js"]
}

  3. 検索コンテキスト

    • コンテキストを使用してファイル内のパターンを検索します

    {
  "pattern": "function.*",
  "path": "./src",
  "options": {
    "recursive": true,
    "contextLines": 2,
    "fileTypes": ["ts"]
  }
}

  4. コードを分析する

    • コードファイルを分析して品質メトリクスを算出します

    {
  "path": "./src",
  "recursive": true,
  "metrics": ["complexity", "dependencies", "quality"]
}

  5. キャッシュ統計

    • キャッシュ統計とパフォーマンスメトリックを取得します

    {
  "detailed": true
}

エラー処理

サーバーは、具体的なエラー コードとともに詳細なエラー メッセージを提供します。

  • FILE_NOT_FOUND : ファイルまたはディレクトリが存在しません

  • PERMISSION_DENIED : アクセス権限の問題

  • INVALID_PATH : 無効なファイルパス形式

  • FILE_TOO_LARGE : ファイルのサイズ制限を超えています

  • ENCODING_ERROR : ファイルのエンコードの問題

  • UNKNOWN_ERROR : 予期しないエラー

構成

カスタマイズ用の環境変数:

  • MAX_CACHE_SIZE : キャッシュされるエントリの最大数（デフォルト: 1000）

  • CACHE_TTL : キャッシュの有効期間（ミリ秒）（デフォルト: 1時間）

  • MAX_FILE_SIZE : 読み取り時の最大ファイルサイズ（バイト単位）

発達

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

# Start in development mode
npm run dev

ライセンス

マサチューセッツ工科大学

貢献

貢献を歓迎します！行動規範とプルリクエストの送信手順の詳細については、貢献ガイドをお読みください。

クロスプラットフォームパスの互換性

注: 2025 年 4 月現在、File Context Server のすべてのファイルおよびディレクトリ パスの処理は、クロスプラットフォーム互換性 (Windows、macOS、Linux) を向上させるために更新されています。

  • すべての glob パターンは内部的に POSIX スタイルのパス (スラッシュ) を使用するため、OS に関係なく一貫したファイル マッチングが保証されます。

  • すべてのファイル システム操作 (読み取り、書き込み、統計など) では、信頼性を確保するために正規化された絶対パスが使用されます。

  • サーバーを開発または拡張している場合は、glob パターンにpath.posix.joinを使用し、ファイル システム アクセスにpath.normalize使用します。

  • この変更により、異なるオペレーティング システムでのパス区切り文字とファイルの一致に関する問題が防止されます。

エンドユーザー側では変更は必要ありませんが、開発者はプロジェクトに貢献する際にこれらの規則に従う必要があります。

Available Tools

6 tools
generate_outlineC

Generate a code outline for a file, showing its structure (classes, functions, imports, etc). Supports TypeScript/JavaScript and Python files.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the file to analyze

TDQS

C2.9/5.0
Behavior2/5

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 tool generates an outline and supports specific languages, but it doesn't describe key behavioral traits such as what the output format looks like (e.g., structured data, text), whether it handles errors for unsupported files, or if there are performance considerations. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 concise and well-structured in two sentences: the first states the core purpose, and the second adds language support. There's no wasted text, and it's front-loaded with the main function. However, it could be slightly more efficient by integrating the language support into the first sentence, but it's still highly effective.

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

Completeness2/5

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

Given the tool's complexity (analyzing code structure) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the generated outline includes (e.g., depth, formatting) or how errors are handled, which are crucial for an AI agent to use it correctly. With no structured data to fill these gaps, the description should provide more context to be fully helpful.

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?

The input schema has 100% description coverage, with the 'path' parameter fully documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't clarify path formats or constraints). Since the schema does the heavy lifting, the 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.

Purpose4/5

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

The description clearly states the tool's purpose: 'Generate a code outline for a file, showing its structure (classes, functions, imports, etc).' It specifies the verb ('generate'), resource ('code outline'), and scope ('file'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'getFiles' or 'read_context', which might also involve file 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.

Usage Guidelines2/5

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

The description provides minimal usage guidance: it mentions supported languages (TypeScript/JavaScript and Python), which implies when to use it for those file types. However, it doesn't offer explicit guidance on when to choose this tool over alternatives like 'getFiles' (which might list files) or 'read_context' (which might read file contents), nor does it mention prerequisites or exclusions. This lack of comparative context limits its helpfulness.

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

get_chunk_countA

Get the total number of chunks that will be returned for a read_context request. Use this tool FIRST before reading content to determine how many chunks you need to request. The parameters should match what you'll use in read_context.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to file or directory
encodingNoFile encoding (e.g., utf8, ascii, latin1)utf8
maxSizeNoMaximum file size in bytes. Files larger than this will be chunked.
recursiveNoWhether to read directories recursively (includes subdirectories)
fileTypesNoFile extension(s) to include WITHOUT dots (e.g. ["ts", "js", "py"] or just "ts"). Empty/undefined means all files.

TDQS

A4.2/5.0
Behavior3/5

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 explains the tool's role in planning for 'read_context' and that parameters should match, which adds useful context. However, it lacks details on performance characteristics, error handling, or output format, leaving gaps in behavioral understanding for a tool with no annotation support.

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 highly concise and well-structured with two sentences. The first sentence states the purpose, and the second provides critical usage guidance. Every word serves a clear purpose, with no redundancy or unnecessary elaboration.

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?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is reasonably complete. It clarifies the tool's purpose and usage relative to 'read_context', but lacks details on output format or error scenarios. With 100% schema coverage, it provides adequate context for basic use, though more behavioral transparency would enhance completeness.

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 description coverage is 100%, so the schema fully documents all 5 parameters. The description adds minimal value beyond the schema by implying parameter alignment with 'read_context', but does not provide additional syntax or format details. This meets the baseline score of 3 when schema coverage is high.

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's purpose: 'Get the total number of chunks that will be returned for a read_context request.' It specifies the verb ('Get') and resource ('total number of chunks'), and distinguishes it from sibling tools like 'read_context' by focusing on pre-calculation rather than content retrieval.

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 provides explicit usage guidance: 'Use this tool FIRST before reading content to determine how many chunks you need to request.' It directly addresses when to use this tool (before 'read_context') and why (to determine chunk count), offering clear context for its application relative to alternatives.

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

getFilesC

Retrieve multiple files by their paths, returning content and metadata for each file

ParametersJSON Schema
NameRequiredDescriptionDefault
filePathListYesThe list of file paths for the file content to return.

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions the action ('retrieve') and output ('content and metadata'), but lacks critical behavioral details such as permissions required, error handling for invalid paths, rate limits, or whether this is a read-only operation. This is a significant gap for a tool that interacts with files.

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 a single, efficient sentence that front-loads the core action and output without any wasted words. It's appropriately sized for the tool's complexity.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like safety, error handling, or output structure, which are crucial for a file retrieval tool. The agent would need to guess about these elements.

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 description coverage is 100%, with the parameter 'filePathList' well-documented in the schema. The description adds minimal value beyond the schema by implying retrieval of multiple files, but doesn't provide additional semantics like path format examples or constraints beyond what's in the schema.

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

Purpose4/5

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 specific verbs ('Retrieve multiple files') and resources ('files by their paths'), and specifies what it returns ('content and metadata for each file'). However, it doesn't explicitly differentiate from sibling tools like 'read_context' or 'get_profile_context', which might also involve file/content retrieval operations.

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

Usage Guidelines2/5

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, limitations, or compare it to sibling tools like 'read_context' or 'get_profile_context', leaving the agent without context for selection.

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

get_profile_contextC

Get repository context based on current profile settings

ParametersJSON Schema
NameRequiredDescriptionDefault
refreshNoWhether to refresh file selection before generating context

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden but only states what the tool does without disclosing behavioral traits. It doesn't mention whether this is a read-only operation, if it requires specific permissions, what 'repository context' includes (e.g., files, metadata), or any rate limits, leaving significant gaps in understanding its behavior.

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 a single, efficient sentence that states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, though it could be slightly more informative given the lack of other details, but it earns its place by being clear and direct.

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

Completeness2/5

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

Given no annotations, no output schema, and a vague purpose, the description is incomplete. It doesn't explain what 'repository context' returns, how it relates to 'current profile settings', or provide enough detail for an agent to understand the tool's full scope and usage in context with sibling tools, leaving significant gaps.

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 input schema has 1 parameter with 100% description coverage, so the schema fully documents the 'refresh' parameter. The description adds no parameter-specific information beyond what the schema provides, but with only one well-documented parameter, the baseline is high, and the description doesn't detract from this.

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

Purpose3/5

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

The description states the tool 'Get repository context based on current profile settings', which provides a general purpose (verb+resource). However, it's vague about what 'repository context' entails and doesn't distinguish from sibling tools like 'read_context' or 'getFiles', leaving ambiguity about scope and differentiation.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like 'read_context' or 'getFiles' is provided. The description implies usage for obtaining context related to profiles, but lacks context about prerequisites, exclusions, or comparisons with sibling tools, offering minimal direction.

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

read_contextA

Read and analyze code files with advanced filtering and chunking. The server automatically ignores common artifact directories and files:

  • Version Control: .git/

  • Python: .venv/, pycache/, *.pyc, etc.

  • JavaScript/Node.js: node_modules/, bower_components/, .next/, dist/, etc.

  • IDE/Editor: .idea/, .vscode/, .env, etc.

For large files or directories, use get_chunk_count first to determine total chunks, then request specific chunks using chunkNumber parameter.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to file or directory to read
maxSizeNoMaximum file size in bytes. Files larger than this will be chunked.
encodingNoFile encoding (e.g., utf8, ascii, latin1)utf8
recursiveNoWhether to read directories recursively (includes subdirectories)
fileTypesNoFile extension(s) to include WITHOUT dots (e.g. ["ts", "js", "py"] or just "ts"). Empty/undefined means all files.
chunkNumberNoWhich chunk to return (0-based). Use with get_chunk_count to handle large files/directories.

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits: it specifies automatic directory exclusions (version control, Python artifacts, JavaScript/Node.js, IDE/editor files), describes chunking behavior for large files, and explains the relationship with get_chunk_count. It doesn't mention error handling, performance characteristics, or authentication needs, but provides substantial operational context.

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 appropriately sized and front-loaded with the core purpose. The bulleted list of exclusions is efficient, and the guidance about get_chunk_count is necessary context. While slightly longer than minimal, every sentence earns its place by providing essential operational information that isn't in the schema.

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?

Given the tool's complexity (6 parameters, chunking behavior, filtering logic) and no annotations/output schema, the description does an excellent job covering operational context. It explains the automatic exclusions, chunking workflow, and relationship with sibling tools. The main gap is lack of information about return format/content, but this is reasonable given the tool's primary focus on reading operations.

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 description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds some context about chunkNumber usage ('Use with get_chunk_count to handle large files/directories') and implies filtering through the automatic exclusions list, but doesn't provide additional parameter semantics beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.

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's purpose with specific verbs ('read and analyze code files') and resources ('code files'), and distinguishes it from siblings by mentioning advanced filtering/chunking capabilities and automatic directory exclusions. It goes beyond a simple read operation by describing analysis and filtering features.

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 provides explicit guidance on when to use this tool versus alternatives: it mentions using 'get_chunk_count first to determine total chunks' for large files/directories, and the automatic exclusion list helps users understand when this tool is appropriate versus when manual filtering might be needed elsewhere. It also distinguishes from 'getFiles' by focusing on content reading rather than just file listing.

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

set_profileC

Set the active profile for context generation

ParametersJSON Schema
NameRequiredDescriptionDefault
profile_nameYesName of the profile to activate

TDQS

C2.9/5.0
Behavior2/5

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 states the action ('Set the active profile') but doesn't reveal important behavioral traits: whether this is a persistent configuration change, if it affects subsequent operations, what permissions are required, if there are side effects, or what happens on failure. The description is minimal and lacks operational context.

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 extremely concise - a single 7-word sentence that communicates the core purpose without any wasted words. It's front-loaded with the essential action and purpose. This is an example of efficient communication where every word earns its place.

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

Completeness2/5

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

Given that this is a mutation tool (implied by 'Set') with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'active profile' means in the system context, how this affects other operations, what the expected outcome is, or provide any error handling information. For a tool that likely changes system state, more context is needed.

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 description coverage is 100% (the single parameter 'profile_name' is fully documented in the schema), so the baseline is 3. The description doesn't add any parameter-specific information beyond what's already in the schema - it doesn't explain what constitutes a valid profile name, where profiles come from, or provide examples.

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

Purpose4/5

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 ('Set') and resource ('active profile'), and indicates the functional outcome ('for context generation'). However, it doesn't explicitly differentiate this from sibling tools like 'get_profile_context' or explain how 'set_profile' relates to other profile/context operations.

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

Usage Guidelines2/5

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 like 'get_profile_context' or 'read_context'. There's no mention of prerequisites, when this operation is needed, or what happens if no profile is set. The phrase 'for context generation' hints at a purpose but doesn't establish clear usage boundaries.

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.

  1. 6 tool updates
    • First observedgenerate_outline
    • First observedget_chunk_count
    • First observedget_profile_context
    • First observedgetFiles
    • First observedread_context
    • First observedset_profile
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.3/5.0

Scored across 6 tools

Disambiguation4/5

Most tools have distinct purposes, but 'getFiles' and 'read_context' both involve retrieving file content, which could cause confusion. 'generate_outline' and 'get_profile_context' are clearly separate, and 'set_profile' and 'get_chunk_count' are specialized utilities.

Naming Consistency2/5

Naming is inconsistent with mixed conventions: 'generate_outline' and 'read_context' use snake_case, while 'getFiles' uses camelCase. Verb styles vary (e.g., 'generate' vs. 'get' vs. 'read'), and 'get_chunk_count' includes an underscore while 'getFiles' does not, creating a chaotic pattern.

Tool Count5/5

With 6 tools, the count is well-scoped for a file context server, covering core operations like file retrieval, analysis, and profile management without being overwhelming or too sparse.

Completeness4/5

The toolset covers key file context operations: retrieving files, analyzing code, generating outlines, and managing profiles. A minor gap exists in update or delete operations for profiles or contexts, but agents can likely work around this for the server's purpose.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

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