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prismeai

Prisme.ai MCP Plugin

Official
by prismeai

ai_knowledge_query

Query an AI Knowledge project with RAG to get LLM answers or retrieve document chunks as context.

Instructions

Legacy AI Knowledge API: query an AI Knowledge project with RAG or retrieve context only. For new one-product agents, use Agent Factory messages/send or messages/stream with Storage-backed file_search.

Use method='query' (default) for full RAG response with LLM answer. Use method='context' to retrieve document chunks only without LLM response.

Requires a legacy AI Knowledge project API key (from AI Knowledge > API & Webhooks).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesUser question or query text
apiKeyYesLegacy AI Knowledge project API key (from AI Knowledge > API & Webhooks)
methodNoquery=RAG with LLM response, context=chunks only
filtersNoDocument filters for RAG context
historyNoConversation history for context
projectIdYesLegacy AI Knowledge project ID
environmentNoOptional environment name (from PRISME_ENVIRONMENTS) to use specific API URL
tool_choiceNoForce specific tools to be used
numberOfSearchResultsNoNumber of chunks to retrieve (for context method)
Behavior4/5

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

Discloses legacy nature, requirement for specific API key, and the two operational modes. Though read-only is implied, it does not explicitly state no side effects, but this is reasonable for a query tool. No annotations to contradict.

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

Conciseness5/5

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

Four concise sentences, each providing essential information: purpose, alternatives, method behaviors, and authentication. No redundancy, front-loaded with key distinction.

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?

Covers usage and authentication well, but lacks description of return format or response structure. Given no output schema, this is a gap. Otherwise complete for the core functionality.

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%, but description adds critical context for 'method' (default and behaviors) and 'apiKey' (source and requirement). Other parameters are well-documented in schema; description reinforces key choices.

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

Purpose5/5

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

Clearly states it is a legacy AI Knowledge API for querying projects with RAG or context retrieval. Distinguishes from newer Agent Factory alternatives and differentiates between two methods (query and context).

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

Usage Guidelines5/5

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

Explicitly advises against using for new one-product agents, directing to Agent Factory tools. Clearly explains when to use 'query' vs 'context', and notes the requirement for a legacy API key.

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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