Minds: Synthetic Market Research

Audience creation progress

get_audience_creation_progress

Read-onlyIdempotent

Read-only; never starts or retries work. operationId: creation and member training. audienceId: training, background research, validation.ready (validation's 10-ready-Mind rule), validation.reason MINDS_TRAINING (poll later) or AUDIENCE_TOO_SMALL (add Minds). recalibrationId: its status, retained result or terminal error. Final score needs all members trained and research done.

Input Schema

Name	Required	Description
`audienceId`	No	An existing Audience, instead of operationId.
`operationId`	No	Audience creation operation id.
`recalibrationId`	No	With audienceId: the operation id returned by recalibrate_audience.

Output Schema

Name	Required	Description
`audienceId`	No	Existing Audience read by audienceId.
`audienceCreation`	No	Current Audience creation and member training snapshot.
`audienceProgress`	No	Existing Audience member readiness, background research, coverage, and validation: the default new unscoped training check (validation.ready, with validation.reason MINDS_TRAINING or AUDIENCE_TOO_SMALL when not).
`audienceRecalibration`	No	One recalibration receipt: id, status, progress, retained result or terminal error.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

changedOutput schema / properties / audienceProgress / description

Previous value: -"Existing Audience member readiness, background research and coverage."New value: +"Existing Audience member readiness, background research, coverage, and validation: the default new unscoped training check (validation.ready, with validation.reason MINDS_TRAINING or AUDIENCE_TOO_SMALL when not)."

Changed7 schema fields changedOct 7, 2026

addedInput schema / properties / audienceId

Added value: +{
+  "description": "An existing Audience, instead of operationId.",
+  "format": "uuid",
+  "pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$",
+  "type": "string"
+}

addedInput schema / properties / operationId / description
```
Added value: +"Audience creation operation id."
```

addedInput schema / properties / recalibrationId

Added value: +{
+  "description": "With audienceId: the operation id returned by recalibrate_audience.",
+  "format": "uuid",
+  "pattern": "^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$",
+  "type": "string"
+}

removedInput schema / required
```
Removed value: -[
-  "operationId"
-]
```

addedOutput schema / properties / audienceId

Added value: +{
+  "description": "Existing Audience read by audienceId.",
+  "type": [
+    "string",
+    "null"
+  ]
+}

addedOutput schema / properties / audienceProgress

Added value: +{
+  "description": "Existing Audience member readiness, background research and coverage."
+}

addedOutput schema / properties / audienceRecalibration

Added value: +{
+  "description": "One recalibration receipt: id, status, progress, retained result or terminal error."
+}

AddedSep 29, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the bar is lower. The description still adds value: it reinforces 'never starts or retries work' and explains what different status outcomes mean (validation's 10-ready-Mind rule, retained result vs terminal error, and the condition for a final score). This is genuine behavioral context beyond the annotations.

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

Conciseness3/5

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

It is extremely compact with no filler, but the structure is telegraphic and fragmentary ('operationId: creation and member training.' / 'recalibrationId: its status, retained result or terminal error.'). The behavioral line is front-loaded while the actual purpose is buried, making it harder to parse than it needs to be.

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

Completeness4/5

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

An output schema exists, so return values needn't be re-explained, and all three optional parameters are documented with added meaning. The description supplies the status/condition semantics an agent needs to act on the response. It is only slightly short of complete due to the murky purpose framing.

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?

Schema coverage is 100%, so baseline would be 3, but the description goes beyond the schema by mapping each parameter to the distinct progress it reports: operationId→creation/member training, audienceId→training/background research/validation, recalibrationId→status/retained result/terminal error. This adds real semantic value over the schema's terse one-line descriptions.

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

Purpose3/5

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

The description never states a clean verb+resource sentence like 'returns progress of an audience creation/calibration operation'; instead it opens with a behavioral constraint ('Read-only; never starts or retries work') and dumps per-parameter meanings. The purpose is only inferable by assembling the parameter fragments (creation, member training, validation, recalibration status). It does distinguish itself from mutation siblings, but the core purpose statement is missing.

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?

'Read-only; never starts or retries work' plus 'poll later' and 'add Minds' give meaningful implied usage guidance (this is a poller; when validation.reason=MINDS_TRAINING, call again; when AUDIENCE_TOO_SMALL, change inputs). However, no alternative tools are named explicitly – 'add Minds' doesn't point to a sibling, and the retry path isn't routed to recalibrate_audience by name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Minds: Synthetic Market Research tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.