Lucairn (lucairn.eu) 客户端 — 隐私保护 AI 网关。此代码仓库托管了三个功能对等的官方 SDK：TypeScript、Python 和 Go。
1.0 版本前。所有三个 SDK 在可观测层面保持对等；通过共享的 Go 汇编生成的测试用例锁定跨语言字节等效性。
Related MCP server: ArkForge Trust Layer
语言 | 软件包 | 版本 |
TypeScript |
| 1.0.0 |
Python |
| 1.0.0 |
Go |
| v0.1.0 |
MCP server |
| 1.0.0 |
请参阅 CHANGELOG.md。
MIT — 请参阅 LICENSE。
Send a chat request through the Lucairn privacy gateway with cross-provider BYOK (Anthropic + OpenAI). PII is detected and replaced with placeholders before reaching the upstream LLM. The gateway picks the upstream provider based on the model parameter: claude-* / anthropic-* use ANTHROPIC_API_KEY; gpt-* / openai-* / o1-* / o3-* / o4-* use OPENAI_API_KEY. Wire format follows the Anthropic Messages API. Developer-tier responses contain raw placeholders; Pro and Enterprise tiers can enable automatic re-linking back to the original values.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model identifier. Routing rules: `claude-*` and `anthropic-*` route to Anthropic via ANTHROPIC_API_KEY; `gpt-*`, `openai-*`, `o1-*`, `o3-*`, and `o4-*` route to OpenAI via OPENAI_API_KEY. Examples: `claude-sonnet-4-6`, `gpt-4o-mini`, `o3-mini`. Set one or both of ANTHROPIC_API_KEY and OPENAI_API_KEY in your MCP client env for BYOK; matching is case-insensitive. | |
| max_tokens | Yes | Maximum tokens to generate in the response. Required by the Anthropic Messages API. | |
| messages | Yes | Conversation messages. Each item is { role: "user" | "assistant", content: string | array }. | |
| system | No | Optional system prompt. May be a string or an array of content blocks. Sanitization policy is per-API-key on the gateway side (sanitize or passthrough_audit). | |
| temperature | No | Optional sampling temperature (0..1). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of behavioral disclosure. It reveals PII replacement, provider routing, wire format (Anthropic Messages API), and tier-dependent placeholder handling. It could mention rate limits or error handling, but current coverage is strong.
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 information-dense without fluff. Every sentence adds value, covering key aspects in a logical order. It could be slightly shorter, but it remains concise for the complexity involved.
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 absence of an output schema and sibling tools, the description covers routing, PII detection, tier behavior, and wire format comprehensively. It is complete enough for an agent to understand and invoke the tool 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?
Since schema coverage is 100%, the baseline is 3. The description adds significant meaning beyond the schema by explaining model routing rules, BYOK environment variables, system prompt sanitization policy, and the requirement for max_tokens. This justifies a score above baseline.
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 specifies the tool's function: sending a chat request through the Lucairn privacy gateway with BYOK and PII detection. It distinguishes itself from any sibling tools (none exist) by detailing its unique privacy and cross-provider routing features.
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 explains when to use the tool (chat requests requiring privacy and BYOK) and provides detailed routing rules based on the model parameter. However, it does not explicitly state when not to use it or mention alternatives, as there are no siblings.
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.
v0.1.1chat_via_lucairn1 field changedInput schema / properties / model / descriptionPrevious value: -"Anthropic model identifier (e.g. \"claude-sonnet-4-6\")."New value: +"Model identifier. Routing rules: `claude-*` and `anthropic-*` route to Anthropic via ANTHROPIC_API_KEY; `gpt-*`, `openai-*`, `o1-*`, `o3-*`, and `o4-*` route to OpenAI via OPENAI_API_KEY. Examples: `claude-sonnet-4-6`, `gpt-4o-mini`, `o3-mini`. Set one or both of ANTHROPIC_API_KEY and OPENAI_API_KEY in your MCP client env for BYOK; matching is case-insensitive."v0.1.0chat_via_lucairnScored across 1 tool
With only one tool, there is no possibility of ambiguous selection. The single tool clearly defines its action and scope, so agents cannot confuse it with anything else.
The sole tool name, `chat_via_lucairn`, follows a clean snake_case verb-phrase convention. There are no other names present, so there are no conflicting naming styles to navigate.
One tool is slightly below the typical 3–15 tool range, but it is a substantial operation rather than a trivial placeholder. For a narrow privacy-gateway chat use case, the single-tool scope is reasonable.
The tool fully covers the server's apparent purpose: sending chat requests through the Lucairn gateway with provider routing and PII handling. There are no dead ends in the core workflow, and configuration concerns are handled through parameters and environment keys.
Security gateway for AI agents: policy, approval, and audited execution, no secrets shared.
Deterministic trust gate for AI output: leaked-secret, prompt-injection & PII in one call.
Zero-trust gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
