Elicitly Pro

Check an elicitation's status

elicit_status

Read-only

Non-blocking status check for an elicitation you created: returns { status } — pending | submitted | declined | canceled | expired | consumed. Read-only and repeatable (never consumes the result); use elicit_await to block on a decision and elicit_result to fetch the outcome.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`elicitationId`	Yes	The `elicitationId` returned by the creating tool (elicit_approval, elicit_selection, or elicit_proposal).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes	The request's current lifecycle state.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed2 schema fields changedSep 5, 2026

addedInput schema / properties / elicitationId / description

Added value: +"The `elicitationId` returned by the creating tool (elicit_approval, elicit_selection, or elicit_proposal)."

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "status": {
+      "description": "The request's current lifecycle state.",
+      "enum": [
+        "pending",
+        "submitted",
+        "declined",
+        "canceled",
+        "expired",
+        "consumed"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "status"
+  ],
+  "type": "object"
+}

First observedSep 4, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as readOnlyHint=true, and the description adds important behavioral context: it is non-blocking, never consumes the result, and returns only the status, not the full outcome. This goes beyond the annotation by explaining the exact side-effect-free contract and the scope 'for an elicitation you created'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no wasted words. The core purpose and status values are front-loaded, and the alternative-tool guidance is placed in the second sentence. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully covers what an agent needs: the action, the exact return shape, the statuses, the non-consuming behavior, and the routing to sibling tools. An output schema exists, so return-value detail need not be repeated, and nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% description coverage for its single required parameter, and the schema already explains that `elicitationId` is returned by the creating tool. The description adds no additional parameter-level semantics beyond reinforcing 'you created', so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('status check'), a clear resource ('elicitation'), and an explicit outcome: returns `{ status }` with all six possible status values. It also differentiates itself from sibling tools by naming `elicit_await` and `elicit_result` as alternatives for other behaviors, so an agent can distinguish it without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use this tool: it is non-blocking, read-only, and repeatable, and it says to use `elicit_await` to block and `elicit_result` to fetch the outcome. This directly tells the agent which sibling to select for adjacent use cases, leaving no ambiguity.

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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TDQS

A4.1/5.0

Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness4/5

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.

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