ping
Read-onlyIdempotent
Health-check tool that verifies the MCP connection is alive.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
OpenRouter
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the tool's safety profile. The description adds a small behavioral context beyond annotations by explicitly stating the tool is a health-check that verifies liveness. It does not contradict annotations, and the description adds a value beyond what the schema alone provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: a single sentence with no wasted words. It front-loads the core value ('health-check') and provides the purpose immediately. It slightly lacks structure, but for such a simple tool, this is near optimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is indeed simple—zero parameters, no output schema—but the annotations cover the safety profile well. The description does not mention what would happen if the connection is unhealthy or what the response looks like, but that is unnecessary for a health check tool whose entire purpose is to confirm liveness and whose annotations already declare non-destructive behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty with 0 parameters and schema description coverage is 100%, so the description adds no additional semantic value. The baseline for 0 parameters is 4, indicating the description is clear that this is a parameterless operation, which is appropriate for the ping use case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description simply restates the tool's purpose with a generic phrase: 'Health-check tool that verifies the MCP connection is alive.' It does not differentiate this tool from sibling tools or specify the exact resource being checked. While 'health-check' and 'verifies the MCP connection is alive' provide some clarity, the description is essentially a tautology of the name 'ping'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when checking MCP connectivity, but there is no explicit guidance on when to use this tool versus alternatives like list-benchmarks or get-endpoint-uptime-history. A clear context is implied, but no exclusions or alternative comparisons are provided.
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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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A3.9/5.0
Disambiguation4/5
Most tools are clearly differentiated by resource and action: the eight list-* tools each target a distinct view (models, endpoints, rankings, apps, providers, presets, tasks, benchmarks), and cross-references between them reduce mis-selection. A few mild boundaries exist—list-models and list-benchmarks both include benchmark data, and install-ori-harness vs spawn-ori-eval are both Ori recipe tools—but their detailed descriptions mostly resolve these.
Naming Consistency4/5
The naming is overwhelmingly consistent with a verb_noun pattern using the same prefix set: generate-, get-, list-, send-, along with install-, spawn-, search-, and transcribe-. The only deviation is ping, which is a standard bare health-check tool and does not follow the verb_noun convention.
Tool Count3/5
At 22 tools, the set feels heavier than the ideal 3-15 range, though each tool is arguably purposeful given the broad surface: model catalog, rankings, benchmarks, presets, generation, audio, image, docs, uptime, credits, and Ori workflows. The variety justifies the size to some extent, but the sheer number puts it in borderline territory.
Completeness4/5
Core workflows are well covered: model discovery (get-model, list-models, list-model-endpoints), generation (send-message, generate-image, generate-speech, transcribe-audio), observability (get-credits, get-generation, get-endpoint-uptime-history), and docs. Notable gaps include no create/update/delete for presets and no persistent provider configuration methods, but these are workable since presets are dashboard-managed and providers can be pinned per request.