cache-proxy
Server Details
LLM caching proxy (x402 USDC on Base) - exact + semantic cache. Free health.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It does explain the core caching logic (cached response if available, else proxy), which is good. However, it omits details like handling of proxy failures, cache key derivation, or side effects (e.g., writing to cache). These gaps leave behavior partially undisclosed.
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, front-loaded with the main purpose, and contains no redundant information. Every word earns its place, making it highly concise 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?
The description is adequate but incomplete given the absence of annotations and output schema. It specifies the core behavior (cached vs upstream response) but lacks details on error handling, response format, and cache TTL specifics. For a tool with 5 parameters and no output schema, a bit more context would be needed for full completeness.
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?
The input schema has 100% description coverage for all parameters, so the baseline is 3. The description does not add any additional meaning to the parameters beyond the schema, but since the schema already documents each parameter, no deduction is warranted.
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's function: 'Send an LLM request through the caching proxy.' It uses a specific verb ('send') and resource ('LLM request'), and differentiates from the sibling tool 'health' by focusing on proxying and caching. The caching behavior further clarifies the 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 clear context: it is used when needing to send an LLM request via the caching proxy, returning cached or upstream responses. No explicit alternatives are mentioned, but the only sibling is 'health,' which is not a substitute. There are no exclusions, and the intended use is obvious, though no explicit when-not-to-use guidance is provided.
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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the primary behavior: returning server status and echoing an optional string. However, with no annotations provided, the description carries the full burden for behavioral disclosure. It doesn't mention safety, rate limits, or other side effects, but for a health check, the behavior is largely self-evident.
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 extremely concise at two short sentences. Every word is purposeful, and it is front-loaded with the core purpose ('Health check') before the detail.
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 of the tool (one optional parameter, no output schema, no annotations), the description is adequately complete. It explains the function and the optional parameter. It doesn't explain the response format, but for a health check, that is not essential.
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?
The schema describes the single parameter 'echo' with full coverage, and the description merely says 'optional echo', adding no new meaning beyond what the schema already provides. Since schema coverage is 100%, the baseline of 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?
The description states a specific action ('Health check') and the resource is the server status. It clearly indicates a read-only health probe. While it doesn't explicitly mention the sibling tool, the distinction is obvious given the different names.
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 phrase 'Health check' establishes a clear context: this is for verifying server status. It implies when to use it, and there are no exclusions. It doesn't name alternatives, but the sibling tool is different enough that additional guidance is unnecessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
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/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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TDQS
The two tools have completely distinct purposes: cache_query handles LLM requests through the proxy, while health is a simple status check. No ambiguity exists between them.
cache_query follows a verb_noun pattern, but health is a single noun without an action prefix. Minor deviation, but names are clear and readable.
With only 2 tools, the set feels slightly thin but appropriate for a focused proxy service. The core operation and a health check are the essentials.
The main lifecycle of a caching proxy is covered by cache_query. Missing cache-specific operations like invalidation could be useful but are not essential for basic proxy functionality.