elicit_pingConnectivity check for the hosted Elicitly server: echoes message back as pong: <message> over the authenticated session.
| Name | Required | Description | Default |
|---|---|---|---|
| message | No | Text to echo back (default "ping"). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / message / descriptionAdded value: +"Text to echo back (default \"ping\")."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so no mutation concern. The description adds meaningful context beyond the annotation by specifying that it echoes the message as 'pong: <message>' and that it requires an authenticated session, which helps the agent understand expected behavior.
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?
A single sentence that front-loads the purpose ('Connectivity check'), states the resource, and explains the returned output. Every word earns its place with zero redundancy.
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?
For a tool with one optional parameter and no output schema, the description is complete: it conveys purpose, behavior, the authenticated-session requirement, and the exact return format. An agent has everything needed to invoke it correctly.
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 coverage is 100%, with the 'message' parameter fully described as 'Text to echo back (default "ping")'. The description reinforces this by showing the output format, but does not add substantial new parameter-level meaning beyond the schema.
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 verb ('Connectivity check') and resource ('hosted Elicitly server'), and explains the exact echo behavior. This clearly distinguishes it from sibling elicit_* tools, which are all task-oriented operations rather than a ping.
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 clearly implies the tool is for verifying connectivity and that it operates over an authenticated session. It does not explicitly list when-not-to-use it, but the dedicated ping nature makes the usage context unambiguous among the siblings.
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.
Most tools have distinct roles in the elicitation lifecycle, but elicit_approval's form mode heavily overlaps with elicit_form, and elicit_confirm is explicitly a wrapper over elicit_form, creating real selection ambiguity. Status, await, result, cancel, and history are clearly separated, so the confusion is limited to a couple of closely related tools.
All tools share the elicit_ prefix and use snake_case, which makes the namespace predictable and easy to scan. However, the second token mixes nouns (approval, form, proposal, status) with verbs (await, cancel, confirm, ping), and elicit_doctor is a non-obvious metaphor, so the pattern is not perfectly uniform.
Twelve tools is within the ideal scope for a specialized elicitation server: creation variants, status polling, blocking wait, result retrieval, cancellation, history, and diagnostic helpers all have a place. The count feels purposeful rather than padded, and it fits the domain without becoming unwieldy.
The lifecycle is well covered: agents can create varied elicitations, check status, block on decisions, fetch results, cancel pending requests, and review history, plus diagnostic and connectivity tools. The main gap is the inability to update or extend a pending elicitation after creation, but agents can work around this by canceling and recreating.