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tianmu

Perplexica MCP Server

by tianmu

search_academic

Search academic databases for research papers and scholarly content. Obtain AI-generated answers with source citations.

Instructions

Search academic sources using Perplexica's academic search mode.

Args: query: The academic search query chat_provider: Chat model provider (optional, uses env config if not provided) chat_model: Specific chat model to use (optional, uses env config if not provided) embedding_provider: Embedding model provider (optional, uses env config if not provided) embedding_model: Specific embedding model to use (optional, uses env config if not provided) optimization_mode: Speed vs quality tradeoff (optional, uses env config if not provided) output_format: Output format - "formatted" for human-readable text or "json" for raw JSON

Returns: Formatted text with AI response and sources, or JSON if output_format="json"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
chat_providerNo
chat_modelNo
embedding_providerNo
embedding_modelNo
optimization_modeNo
output_formatNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only mentions return format (formatted text or JSON) but omits critical behavioral traits: no mention of safety, idempotency, rate limits, or side effects. This is insufficient for an AI agent to anticipate consequences.

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 uses a clear Args/Returns structure with each parameter on a separate line. There is no redundant wording, but optionality is restated for each parameter, slightly reducing efficiency. Overall, it is well-organized and front-loaded.

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

Completeness3/5

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

Given an output schema exists, the description omits precise return details but covers basics. However, it lacks completeness for a search tool: no mention of pagination, query constraints, or source types. With 7 parameters and no annotations, more context would be beneficial.

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

Parameters3/5

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

Input schema coverage is 0%, so the description must compensate. It provides brief definitions for all 7 parameters, explaining optionality and fallback to env config. However, explanations are often tautological (e.g., 'query: The academic search query') and lack depth (e.g., 'Speed vs quality tradeoff' is vague). Adds moderate value.

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

Purpose4/5

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

The description clearly states it searches academic sources using a specialized mode. While it doesn't explicitly contrast with siblings like search_web or search_youtube, the phrase 'academic search mode' implies distinction. A score of 4 indicates good clarity but lacks explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool vs alternatives. It does not mention use cases, exclusions, or prerequisites. The tool's purpose is stated, but the lack of usage context prevents effective selection among siblings.

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

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