Perspect convenes a panel of expert AI personas that debate any topic, idea, or decision from conflicting angles, rebut each other, then return a synthesized briefing and a contradiction map of exactly where they disagree. Not a yes-man, every side.
Scored across 2 tools
The two tools have clearly distinct purposes: one lists available personas, the other runs a debate. No functional overlap or ambiguity.
Both names follow the same verb_noun pattern (list_personas, run_debate), with consistent snake_case style.
With only two tools, the server stays minimal and focused—exactly the right size for its narrow purpose of listing personas and running debates.
The tool surface covers the full lifecycle implied by the server: discovering available personas and executing a debate. No obvious missing operations.
List the expert personas available to sit on a Perspect AI panel (id, name, discipline).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. The verb 'List' clearly indicates a read-only operation with no side effects. It does not mention error conditions or empty results, but for a simple list operation this is sufficient.
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?
The description is a single, concise sentence that imparts all necessary information without redundant words. It is well-structured and directly to the point.
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?
Given that there is no output schema, the description adequately specifies the returned fields (id, name, discipline). It fully communicates what the tool returns and its purpose, leaving no critical gaps for a simple list operation.
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?
There are no parameters, so no explanation is required. The baseline score of 4 is appropriate since the description does not need to elaborate on any inputs.
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 clearly states the tool's function: listing expert personas, and specifies the exact fields returned (id, name, discipline). The verb 'List' is unambiguous, and the scope ('available to sit on a Perspect AI panel') is well-defined. It also distinguishes itself from the only sibling tool 'run_debate' by its purpose.
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 implies usage (to see available personas before running a debate) but does not explicitly say when to use it versus 'run_debate'. There is no direct contrast or conditional guidance, so the agent must infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convene a panel of expert personas to debate a topic, idea, or decision from conflicting angles, then return the debate, a contradiction map (where the experts disagree), and a synthesized briefing. Optionally pass specific persona ids (see list_personas); omit to auto-select a fitting panel. Takes ~30-60s.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The topic, question, idea, or decision to explore. | |
| personas | No | Optional persona ids to use (2 to 5). Omit to auto-select. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the expected duration (~30-60s) and the three outputs (debate, contradiction map, briefing), giving the agent a clear picture of what happens. It does not mention side effects or permissions, but for a debate simulation this is likely non-destructive. The description adds meaningful behavioral context beyond the schema.
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 concise sentences, front-loaded with the core purpose, followed by optional usage and duration. No filler or redundant wording; every sentence contributes.
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?
The description covers the tool's purpose, optional usage, expected duration, and return structure (debate, contradiction map, briefing). With no output schema, this explanation of outputs is essential and provided. It does not cover edge cases like invalid personas, but that is minor given the clarity provided.
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% for both parameters, so the description adds value by clarifying that personas is optional, that ids can be obtained from list_personas, and that omitting it triggers auto-selection. This enriches the schema's basic description of personas as an optional array.
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 (convene), resource (panel of expert personas), and action (debate). Clearly distinguishes from sibling list_personas which is about listing personas, not debating. The return outputs (debate, contradiction map, briefing) further clarify the purpose.
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?
Provides guidance on persona selection: either pass specific ids or omit for auto-selection, and references list_personas for obtaining ids. Implicitly indicates when to use this tool (when a debate is needed) versus list_personas (when listing personas), though it does not explicitly state exclusions or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
list_personasrun_debate
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