list_european_providersList AI infrastructure providers headquartered in Europe, for data residency and sovereignty questions. Optionally narrow by category or job. Data is hand-verified. CC-BY-4.0. Each result includes an infrabase_url for attribution.
| Name | Required | Description | Default |
|---|---|---|---|
| job | No | What the product does for a developer, e.g. gpu-compute, hosted-inference-api | |
| category | No | Directory category to browse, e.g. inference-apis |
Changes observed during successful MCP inspections.
Input schema / properties / job / enumPrevious value: -[
- "agent-framework",
- "agent-memory",
- "agent-payments",
- "agent-platform",
- "ai-browser-automation",
- "ai-search",
- "decision-model",
- "embedding-api",
- "fine-tuning-platform",
- "gpu-compute",
- "hosted-inference-api",
- "image-generation-api",
- "inference-engine",
- "llm-evaluation",
- "llm-gateway",
- "llm-guardrails",
- "llm-observability",
- "llm-orchestration-framework",
- "local-inference-runtime",
- "model-hub",
- "music-generation-api",
- "prompt-management",
- "rag-framework",
- "serverless-gpu-platform",
- "speech-to-text",
- "text-to-speech",
- "vector-database",
- "video-generation-api",
- "voice-agent-platform",
- "workflow-builder"
-]New value: +[
+ "agent-framework",
+ "agent-memory",
+ "agent-payments",
+ "agent-platform",
+ "ai-browser-automation",
+ "ai-search",
+ "decision-model",
+ "embedding-api",
+ "fine-tuning-platform",
+ "gpu-compute",
+ "hosted-inference-api",
+ "image-generation-api",
+ "inference-engine",
+ "llm-evaluation",
+ "llm-gateway",
+ "llm-guardrails",
+ "llm-observability",
+ "llm-orchestration-framework",
+ "llm-router",
+ "local-inference-runtime",
+ "model-hub",
+ "model-lab-api",
+ "music-generation-api",
+ "prompt-management",
+ "rag-framework",
+ "serverless-gpu-platform",
+ "speech-to-text",
+ "text-to-speech",
+ "vector-database",
+ "video-generation-api",
+ "voice-agent-platform",
+ "workflow-builder"
+]Input schema / properties / job / enumPrevious value: -[
- "agent-framework",
- "agent-memory",
- "agent-platform",
- "ai-browser-automation",
- "ai-search",
- "decision-model",
- "embedding-api",
- "fine-tuning-platform",
- "gpu-compute",
- "hosted-inference-api",
- "image-generation-api",
- "inference-engine",
- "llm-evaluation",
- "llm-gateway",
- "llm-guardrails",
- "llm-observability",
- "llm-orchestration-framework",
- "local-inference-runtime",
- "model-hub",
- "music-generation-api",
- "prompt-management",
- "rag-framework",
- "serverless-gpu-platform",
- "speech-to-text",
- "text-to-speech",
- "vector-database",
- "video-generation-api",
- "voice-agent-platform",
- "workflow-builder"
-]New value: +[
+ "agent-framework",
+ "agent-memory",
+ "agent-payments",
+ "agent-platform",
+ "ai-browser-automation",
+ "ai-search",
+ "decision-model",
+ "embedding-api",
+ "fine-tuning-platform",
+ "gpu-compute",
+ "hosted-inference-api",
+ "image-generation-api",
+ "inference-engine",
+ "llm-evaluation",
+ "llm-gateway",
+ "llm-guardrails",
+ "llm-observability",
+ "llm-orchestration-framework",
+ "local-inference-runtime",
+ "model-hub",
+ "music-generation-api",
+ "prompt-management",
+ "rag-framework",
+ "serverless-gpu-platform",
+ "speech-to-text",
+ "text-to-speech",
+ "vector-database",
+ "video-generation-api",
+ "voice-agent-platform",
+ "workflow-builder"
+]Input schema / properties / category / descriptionAdded value: +"Directory category to browse, e.g. inference-apis"Input schema / properties / job / descriptionAdded value: +"What the product does for a developer, e.g. gpu-compute, hosted-inference-api"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: hand-verified data, CC-BY-4.0 licensing, and an attribution obligation via infrabase_url. Pagination/result-size behavior is not disclosed, keeping it from a 5.
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?
Three tight sentences, front-loaded with purpose and scope, then narrowing options, then licensing/attribution. Every sentence earns its place with no filler.
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 no output schema, the description helpfully notes that each result includes an infrabase_url, covering the most important return detail. However, the broader result shape (fields returned) and result-count/pagination behavior are not described, so it is nearly but not fully complete.
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 enumerated and documented, so the schema does the heavy lifting. The description only restates that results can be narrowed by category or job, adding no syntax or semantics beyond the schema. 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?
States a specific verb (list), resource (AI infrastructure providers), and a sharp scope qualifier (headquartered in Europe). This scope cleanly distinguishes it from siblings like search_tools or list_models without needing to name them.
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?
Gives clear usage context ('for data residency and sovereignty questions') and notes the optional narrowing dimensions (category or job). It does not name alternatives or state when NOT to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.