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perplexity-unofficial-mcp

by exqlnet

Advanced Reasoning

perplexity_reason
Read-only

Generate reasoned answers to complex queries using Perplexity AI, with optional session continuation. Combine sub-questions into one call to reduce repeats.

Instructions

对齐官方 Perplexity MCP:推理(默认 reasoning)。请避免频繁调用;尽量将多个子问题合并到一次 query / 一次 perplexity_search 中查清楚。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
backend_uuidNo续问用的会话标识。通常应直接使用上一轮工具返回的 structuredContent.backend_uuid;若不提供则视为新对话。
strip_thinkingNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

C2.9/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds a behavioral constraint beyond those annotations: avoid frequent calls and merge sub-questions into one query or search. It does not disclose rate limits, authentication needs, or return behavior, but it does add relevant call-efficiency context.

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

Conciseness4/5

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

The description is two sentences and contains no obvious padding. It is reasonably front-loaded with the official-alignment and reasoning purpose, followed by the usage constraint, though the first clause is somewhat vague rather than maximally informative.

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

Completeness2/5

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

For a three-parameter tool with 33% schema description coverage, no output schema, and three sibling tools, the description leaves significant gaps. It does not explain when to prefer this reasoning tool over perplexity_ask or perplexity_research, nor does it define the query or strip_thinking parameters. The call-efficiency advice is present but insufficient for the definition to be complete.

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

Parameters2/5

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

Schema description coverage is only 33%, with backend_uuid described in the schema and query and strip_thinking undocumented. The description mentions query only as a container for merged sub-questions and adds no meaning to strip_thinking or the required query format, so it fails to compensate for the sparse schema coverage.

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 identifies the tool as the 'reasoning (default reasoning)' variant aligned with official Perplexity MCP, which gives a basic sense of purpose. However, it does not clearly state what kind of reasoning output the agent should expect or how it differs from siblings perplexity_ask and perplexity_research. The only sibling contrast is an indirect reference to merging sub-questions into perplexity_search.

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?

It advises avoiding frequent calls and consolidating multiple sub-questions into one query or one perplexity_search call, which is useful operational guidance. Still, it does not explicitly say when to choose perplexity_reason over perplexity_ask or perplexity_research, so an agent must infer its place among siblings.

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