cache-proxy
Server Details
LLM caching proxy (x402 USDC on Base) - exact + semantic cache. Free health.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.7/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: cache_query handles LLM requests with caching, while health is a simple health check. No ambiguity.
Both tool names are concise and follow a similar pattern: cache_query and health are straightforward nouns/verb-noun phrases with consistent style.
With 2 tools, the server is minimal but appropriate for a simple caching proxy. The count feels slightly thin but not problematic.
Core functionality (query with caching) and health check are present. Missing cache invalidation or stats, but acceptable for a basic server.
Available Tools
2 toolscache_queryAInspect
Send an LLM request through the caching proxy. Returns cached response if available, otherwise proxies to upstream LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | JSON request body as string | |
| path | Yes | API path (e.g. /v1/chat/completions) | |
| api_key | Yes | API key for the upstream provider | |
| provider | Yes | LLM provider to proxy to | |
| cache_ttl | No | Cache TTL in seconds (default 86400) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains the core behavior (return cached or proxy) but lacks details on error handling, idempotency, rate limits, or the scope of caching. The description is adequate but could be more transparent about potential side effects or guarantees.
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 two sentences long, front-loading the primary action and key behavior. Every sentence adds value with no redundant or irrelevant content. It is efficient and well-structured.
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 tool's complexity (proxy with caching, 5 parameters including enums and optional TTL), the description is insufficient. It does not explain the response format, caching semantics (e.g., what constitutes a cache hit), or the significance of cache_ttl. Without an output schema, more detail is needed for an agent to use the tool effectively.
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%, so the input schema already documents all parameters. The description adds no additional parameter information beyond what the schema provides. Per guidelines, high coverage sets a baseline of 3, and since the description does not enhance parameter understanding, score remains 3.
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 states the tool sends an LLM request through a caching proxy, specifying the caching behavior. It distinguishes itself from the sibling 'health' tool, which is a health check. The verb 'send' and resource 'LLM request through caching proxy' are specific and unambiguous.
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 does not explicitly state when to use this tool versus alternatives (e.g., a direct LLM call) nor does it mention prerequisites or exclusions. However, since the only sibling is 'health', the context is less critical, but guidance on cache_ttl and when caching is beneficial would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
healthAInspect
Health check. Returns server status and optional echo.
| Name | Required | Description | Default |
|---|---|---|---|
| echo | No | Optional string to echo back |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description adequately discloses that the tool returns server status and an optional echo, implying no side effects. It could mention whether it requires authentication, but for a health check this is sufficient.
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 a single sentence, front-loaded, and contains no unnecessary words.
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 simplicity (1 optional parameter, no output schema), the description covers the essential functionality. It could mention return format or server status details, but it's adequate.
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%, so baseline is 3. The description adds 'optional echo' which mirrors the schema's description, not adding significant new meaning.
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 states 'Health check' as the verb+resource, and specifies 'Returns server status and optional echo.' It distinguishes itself from the sibling tool 'cache_query' by its clear purpose.
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 provides no guidance on when to use this tool vs alternatives or any context about appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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