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.
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?
No annotations are provided, and the description does not explicitly state whether the operation is read-only or has side effects. While 'List' suggests a read operation, the lack of explicit disclosure about side effects or return format leaves some ambiguity for the agent.
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 extremely concise and to the point, using a single sentence to convey the tool's purpose and expected output. No unnecessary words or 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?
The description includes the key output fields (id, name, discipline), giving the agent enough context to know what to expect. For a simple list operation with no parameters and no output schema, this is sufficiently complete.
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 has zero parameters, so schema coverage is trivially 100%. According to the baseline for 0 parameters, a score of 4 is appropriate; the description does not need to add parameter details since none exist.
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 available for a Perspect AI panel, including specific fields (id, name, discipline). It distinguishes itself from the sibling tool run_debate, which performs a different action.
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 when to use this tool (when you need to see available personas) and contrasts with run_debate, but it does not explicitly state 'use this when...' or mention any alternatives. The context is clear enough for a simple list operation.
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?
The description discloses that the tool takes approximately 30-60 seconds, which informs the user about latency. It also lists the expected outputs (debate, contradiction map, synthesized briefing). There are no annotations, so the description carries the burden. It does not mention any failure modes or side effects, but for a content-generation tool, this level of transparency is adequate.
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 concise, using only two sentences to convey the purpose, optional parameters, and expected outputs. It is well-structured and avoids unnecessary detail, making it easy for an agent to parse quickly.
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 provides sufficient context for using the tool: what it does, what it returns, and the optional parameters. It does not define 'contradiction map' or detail the output format, but the schema covers parameter types and the overall purpose is clear. The context is complete enough for an agent to decide to invoke it.
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 schema already describes both parameters (topic and personas), and the description adds the important clarification that omitting personas auto-selects a fitting panel. This directly enhances the understanding of the optional parameter, making the semantics fully clear.
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 purpose: to convene a panel of expert personas to debate a topic and return specific outputs (debate, contradiction map, synthesized briefing). This distinctly differentiates it from the sibling tool list_personas, which is for listing available personas, not running debates.
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 provides practical guidance on when to use it, mentioning that persona ids are optional and can be obtained via list_personas, and that omitting them auto-selects a panel. It does not explicitly state 'use this when you need a debate' but the purpose is self-evident and the reference to list_personas gives a hint for the workflow.
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. Dates show when Glama detected each change.
list_personasrun_debateClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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