EvidenceLens MCP
EvidenceLens MCP
EvidenceLens MCP は、決定的で読み取り専用のエビデンスレビュー契約のための TypeScript Model Context Protocol サーバーです。唯一の review_evidence ツールは、Phase 2 のインラインコンテンツと、明示的に設定された場合の Phase 3 のファイルシステムソースを受け入れます。テキスト、表、PDF、画像、スクリーンショットを来歴メタデータに正規化し、findings を空のままにし、プロバイダー呼び出しを行いません。
ローカル開発
npm install
npm run dev
npm test
npm run buildnpm run dev は、src/server.ts から stdio 経由で MCP サーバーを起動します。npm test は、契約、正規化、フィクスチャ、安全性、MCP プロトコルのテストスイート全体を実行します。npm run build はサーバーの型チェックとコンパイルを行います。
Related MCP server: Observability Agent MCP
MCP 契約
正確なインライン/ファイルシステムリクエスト契約、ルート設定の文法、バイト制限、filesystem:// の来歴、正規化されたエビデンスメタデータ、安定したエラー、および読み取り専用/プロバイダーなし/findings なしの境界については、docs/mcp-contract.md を参照してください。
プラットフォームに関する注意: デフォルトのファイルシステムリーダーは、Linux ではディスクリプタ相対のコンポーネント走査を使用します。macOS では、このプロジェクトにはサポートされている Node の openat/openat2 バインディングがないため、デフォルトのアンカー付きファイルシステム読み取りは、サニタイズされた ACCESS_DENIED とバイトなしでフェイルクローズします。パス名によるフォールバックはありません。Phase 2 のインラインエビデンスは引き続きサポートされ、埋め込みテストでは、移植可能なファイルシステム読み取りのカバレッジのために、レビュー済みの安全なファイルシステムアダプターを注入できます。
オプションのファイルシステムルート
ルートが設定されていない場合、ファイルシステムアクセスは無効になります。サーバーが現在のディレクトリ、ホームディレクトリ、リポジトリ、またはその他の広範なデフォルトにフォールバックすることはありません。カンマまたはセミコロンで区切った id=absolute-path エントリを繰り返して、明示的なルートを設定します:
EVIDENCELENS_ALLOWED_ROOTS='course=/absolute/path/course;examples=/absolute/path/examples' npm run devルート ID は [A-Za-z][A-Za-z0-9_-]{0,31} に一致します。空のエントリ、重複する ID、正規パスの衝突、読み取り不能なルート、ディレクトリ以外、相対パス、および不正なエントリは、安定したメッセージ Invalid filesystem root configuration で失敗します。パスがエコーされることは決してありません。
Available Tools
1 toolreview_evidenceReview EvidenceCRead-onlyIdempotent
Accept Phase 1 evidence review metadata and return a deterministic skeleton response.
| Name | Required | Description | Default |
|---|---|---|---|
| limits | No | ||
| evidence | No | ||
| reviewId | Yes | ||
| objective | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already document the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), and the description adds the behavioral fact that the response is deterministic and skeleton-shaped. However, it does not say what the skeleton response contains or how input is handled, leaving a partial transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with no filler; the core action and outcome are front-loaded. It is appropriately sized but terse to the point of vagueness, so not a full 5.
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 a nested input schema and no output schema, the description is too thin: it does not explain the return shape, what 'Phase 1' means, or what evidence metadata is expected. Annotations cover safety but not operational context needed to invoke 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 0%, so the description needed to compensate for parameter meaning, but it mentions no parameter names or semantics. The schema itself provides constraints and enums, but the tool description contributes nothing to clarify reviewId, objective, evidence, or limits.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a concrete verb-resource pair ('Accept Phase 1 evidence review metadata') and names the outcome ('return a deterministic skeleton response'), so an agent can tell this is a submission/stub endpoint for the review workflow. However, 'skeleton response' is unexplained and there is no sibling differentiation, though no siblings are listed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance about when to call this tool versus any alternative, no workflow conditions, and no preconditions or exclusions. The only hint is the phrase 'Phase 1,' which is not elaborated.
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 tool update
v0.1.2- First observed
review_evidence
TDQS
Scored across 1 tool
There is only one tool, so there is no ambiguity between tools, but the single tool's purpose is vague and could overlap with future tools. Since there is no set to disambiguate, it scores minimal.
With only one tool, naming consistency is trivial but the naming is not informative; 'review_evidence' is a generic name that doesn't indicate what it does with evidence. No pattern can be assessed.
A single tool for a server called 'EvidenceLens MCP' that claims to 'review evidence' is too thin. The scope suggests a larger toolset for evidence review, but only one trivial tool is provided.
The tool description says 'Accept Phase 1 evidence review metadata and return a deterministic skeleton response.' This covers only a tiny fraction of what an evidence review workflow would need; there's no way to submit actual evidence, get details, update reviews, or handle later phases.
Maintenance
Related MCP Connectors
Read-only game, setup, place, evidence and travel decision tools with explicit provenance.
Agent memory that refuses to guess: evidence-gated recall, exact-source reads, verifiable deletion.
Read-only finance and operations controls for AI agents with evidence and safe next actions.
Browser-local and CLI static evidence for deployed AI model artifacts.
Related MCP Servers
AlicenseNot gradedqualityFmaintenanceProvides a universal read path for agents to extract facts, metadata, and provenance from local files and guarded remote URLs without using generative LLMs.MIT- AlicenseBqualityCmaintenanceA portable, read-only Model Context Protocol server for turning observability data into bounded evidence that AI agents can inspect safely.7Apache 2.0
- AlicenseNot gradedqualityDmaintenanceEnables step-debugging, deterministic replay, and signed audit evidence for AI agents, compliant with EU AI Act.62 PyPIMIT
- AlicenseNot gradedqualityCmaintenanceEnables secure, read-only access to local project files (including text, DOCX, PDF, and XLSX) through MCP, with strict directory whitelisting and no write, edit, or command-execution tools.1MIT