Cue
Server Details
Score and improve AI prompts with feedback, intent-preserving rewrites and Cue account access.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one retrieves the authenticated Cue profile and entitlement, while the other reviews and improves prompts. There is no overlap in action or resource, so an agent can easily choose correctly.
Both names follow a consistent verb_noun snake_case convention: get_profile and improve_prompt. The pattern is predictable and readable.
Two tools is quite thin for a server, though it may match a narrow Cue integration focused on profile lookup and prompt improvement. It sits at the borderline where more lifecycle operations would likely be expected.
The surface covers profile retrieval and prompt improvement but lacks supporting operations such as prompt history, usage/limits inspection, or account management. These gaps are notable, though the core stated actions are present.
Available Tools
2 toolsget_profileGet Cue accountARead-onlyInspect
Always use this tool when the user asks which Cue account is connected, which email is signed in, who they are in Cue, or what Cue plan/subscription/Free/Pro status they have. Returns the authenticated Cue profile and entitlement.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| plan | Yes | |
| Yes | ||
| is_pro | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered by structured data. The description adds that the profile is *authenticated* and that entitlement data is included, which implies an auth prerequisite and hints at the payload scope. It stops short of stating failure behavior when unauthenticated or any rate-limit characteristics.
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?
Two sentences: the trigger list first, the return contract second. Each clause in the trigger list corresponds to a genuinely different user phrasing rather than redundant restatement, and the return statement is compressed to a single clause. It is front-loaded with the actionable condition.
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?
With zero parameters and an output schema already present, the description need not explain the return shape, and it does not attempt to. The remaining agent-facing questions — when to call it and what it conceptually returns — are both answered. Nothing needed to invoke it correctly is missing.
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 tool takes zero parameters, so the baseline is 4; there are no parameter semantics for the description to explain or compensate for. Schema description coverage is reported at 100% with an empty properties object, so nothing is left undocumented.
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 names a specific resource — the authenticated Cue profile and entitlement — and states verbatim what it returns. It is the only account/profile tool among the listed siblings (improve_prompt is unrelated), so an agent can select it without ambiguity. The trigger phrases (which account, which email, plan/subscription status) make the resource scope concrete.
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?
It opens with an explicit directive — "Always use this tool when the user asks..." — followed by an enumerated set of user phrasings that map to distinct intents (connected account, signed-in email, identity, plan/subscription tier). No alternative tool is relevant for this intent, so no exclusion clause is needed. The when-to-use condition is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
improve_promptImprove prompt with CueAInspect
Always use this tool when the user asks Cue to review, score, refine, rewrite, or improve an AI prompt. Free usage limits and Cue Pro entitlement are enforced by Cue. Returns a 0–10 score, concise feedback, and one improved prompt in the same language as the input.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt text the user wants Cue to review and improve. |
Output Schema
| Name | Required | Description |
|---|---|---|
| label | Yes | |
| score | Yes | |
| rewrite | Yes | |
| feedback | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare openWorldHint and non-destructive read/write hints, and the description adds genuinely useful context beyond them: entitlement/gating behavior ('Free usage limits and Cue Pro entitlement are enforced by Cue'). The return summary (0-10 score, feedback, improved prompt in same language) is partly redundant with the output schema but still clarifies format expectations.
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?
Three sentences, front-loaded with the triggering directive, followed by entitlement behavior and output description. Every sentence carries information; nothing is padded, though it is slightly dense.
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 single-parameter tool with a full schema, an output schema, and supporting annotations, the description covers trigger conditions, gating, and output shape. It is essentially complete, with only explicit non-use guidance missing.
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% for the single 'prompt' parameter, so the baseline of 3 applies. The description adds no syntax, format, or length guidance beyond what the schema already states.
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?
States a specific verb set (review, score, refine, rewrite, improve) applied to a concrete resource (an AI prompt), so the agent knows exactly what the tool produces. The sibling get_profile operates in a different domain, so the distinction is clear without opening either schema.
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?
Explicitly directs the agent with 'Always use this tool when the user asks Cue to review, score, refine, rewrite, or improve an AI prompt,' giving concrete trigger phrases. It lacks a when-not or an alternative-routing condition, but the sole sibling is unrelated, so little is left ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
get_profile - First observed
improve_prompt
Related MCP Connectors
PQS scores any prompt before the model runs. 8 dimensions. 5 frameworks. Pre-flight, not post-hoc.
A prompt engineer you talk to. It writes prompts, sharpens yours, and keeps them on your shelf.
Test and compare prompts across any AI provider. Bring your own keys.
Generate contextual prompts and reusable agent skills, evaluate prompts with the 16-dimension Prompt Score, and manage saved work in PromptDrive. Twelve MCP tools also provide authorized access to private Memory for source-grounded answers. Connect over Streamable HTTP using OAuth 2.1 and PKCE. Generation consumes account quota and automatically saves successful results; Memory access follows account permissions and plan limits.
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