How vLLM’s scores are calculated

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

• [probe] https://docs.vllm.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://docs.vllm.ai/llms.txt”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [github] https://github.com/vllm-project/vllm“Install vLLM with uv (recommended) or pip:”
• [github] https://github.com/vllm-project/vllm“Or build from source for development.”

Plug MCP servers into this product so it can use their toolsweight 3

n/a — not applicable to this product: excluded from numerator and denominator

• [claimed-docs] https://docs.vllm.ai“Tool calling and reasoning parsers”
• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”

Connect an agent via an official MCP serverweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”

Use an official CLIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [community] https://news.ycombinator.com/item?id=36409082“Cool, I prefer the OpenAI-Compatible api. Although this is not very technically difficult, it is really intimate, because it make me feel free to use all ChatGPT applications.”
• [community] https://news.ycombinator.com/item?id=44407058“We use vLLM kv cache and continuous batching as a foundation for requests in ScalarLM and also add batching optimizations in a centralized queue and by adding explicit batching support in our client... There is more perf you can squeeze out of vLLM.”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Build against official SDKsweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [community] https://news.ycombinator.com/item?id=36409082“Cool, I prefer the OpenAI-Compatible api. Although this is not very technically difficult, it is really intimate, because it make me feel free to use all ChatGPT applications.”

Subscribe to events via webhooksweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Connect a coding agent to this product as a working backendweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

• [claimed-docs] https://docs.vllm.ai“Tool calling and reasoning parsers”
• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [claimed-docs] https://docs.vllm.ai“Streaming outputs”
• [community] https://news.ycombinator.com/item?id=36409082“Cool, I prefer the OpenAI-Compatible api. Although this is not very technically difficult, it is really intimate, because it make me feel free to use all ChatGPT applications.”
• [community] https://news.ycombinator.com/item?id=49202852“vLLM is originally marketed as paged attention, but in hindsight, separating the web server and GPU process, continuous batching, kv caching / chunking, and a huge model library including low precision mattered more.”

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [probe] https://docs.vllm.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://docs.vllm.ai/llms.txt”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [probe] https://docs.vllm.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://docs.vllm.ai/llms.txt”

Test against a sandbox environment without touching production dataweight 1

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Do everything through the API that I can do in the UIweight 2

n/a — not applicable to this product: excluded from numerator and denominator

• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”

Export all of my data in open formats and leaveweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Read the product's source under an open licenseweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

• [github] https://github.com/vllm-project/vllm“Install vLLM with uv (recommended) or pip:”
• [github] https://github.com/vllm-project/vllm“Or build from source for development.”

Self-host the core productweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

• [github] https://github.com/vllm-project/vllm“Install vLLM with uv (recommended) or pip:”
• [github] https://github.com/vllm-project/vllm“Or build from source for development.”
• [claimed-docs] https://docs.vllm.ai“Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.”
• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”
• [community] https://news.ycombinator.com/item?id=44407058“We use vLLM kv cache and continuous batching as a foundation for requests in ScalarLM and also add batching optimizations in a centralized queue and by adding explicit batching support in our client... There is more perf you can squeeze out of vLLM.”

Get AI-generated insights and suggestions from my data inside the productweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Set up automations that run autonomously in the backgroundweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Delegate tasks to a built-in AI assistant inside the productweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Operate the product with natural-language commandsweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Perform bulk operations across many items at onceweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

• [claimed-docs] https://docs.vllm.ai“Continuous batching of incoming requests, chunked prefill, prefix caching”
• [community] https://news.ycombinator.com/item?id=44407058“We use vLLM kv cache and continuous batching as a foundation for requests in ScalarLM and also add batching optimizations in a centralized queue and by adding explicit batching support in our client... There is more perf you can squeeze out of vLLM.”
• [claimed-docs] https://docs.vllm.ai“OpenAI-compatible API server, plus Anthropic Messages API and gRPC support”

Define rules that trigger actions automatically on eventsweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Schedule recurring jobs or workflowsweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh