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prismeai

Prisme.ai MCP Plugin

Official
by prismeai

get_prisme_documentation

Read-only

Retrieve Prisme.ai documentation by section. Start with 'index' to see available sections covering automations, UI components, workspace config, and more.

Instructions

Returns Prisme.ai documentation by section. Call with 'index' first to see available sections.

SECTIONS:

  • index: Table of contents and quick reference guide

  • automations: Backend logic - triggers, instructions, expressions, memory scopes

  • pages-blocks: UI components - Form, DataTable, RichText, Action, Chat, Charts, Carousel, Tabs, etc.

  • workspace-config: Secrets management, workspace RBAC, one-product IAM notes, native events, versioning with Git

  • advanced-features: Crawler, Custom Code, Agent Factory capabilities, Storage RAG, LLM Gateway, events

  • products-overview: Current one-product platform architecture and integration patterns

  • agent-creation: Agent Factory creation, prompt engineering, RAG, capabilities, evaluations

  • api-selfhosting: REST/webhook API reference, one-product endpoint families, self-hosting deployment

  • product-agent-factory: Agent Factory - agents, publishing, conversations, A2A, tools

  • product-storage: Knowledge (Storage) - files, vector stores, indexing, RAG search

  • product-llm-gateway: LLM Gateway - completions, embeddings, model catalog, routing

  • product-capabilities: Capabilities catalog - MCP, file search, functions, skills, guardrails

  • product-agent-evaluations: Agent Evaluations - test cases, runs, LLM-as-judge

  • product-governance-v2: AI Governance v2 - IAM, API keys, service accounts, observability

  • product-insights-v2: AI Insights v2 - Agent Factory analytics, criteria, feedback, GDPR

  • product-collection-v3: AI Collection v3 - structured data MCP tools for agents

  • product-prompt-library: Prompt Library - MCP prompts and showcases

  • product-builder: Builder - DSUL workspaces, automations, pages, apps

  • capability-workspaces: Backing guardrail, memory, search, vector provider, and connector workspaces

  • legacy-products-overview: Legacy product architecture overview

  • legacy-product-securechat: Legacy SecureChat product details

  • legacy-product-store: Legacy AI Store product details

  • legacy-product-knowledge: Legacy AI Knowledge and Knowledge Client details

  • legacy-product-governance: Legacy AI Governance details

  • legacy-product-insights: Legacy AI Insights details

  • legacy-product-collection: Legacy AI Collection details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionNoDocumentation section to retrieve. Use 'index' to see all available sections.
Behavior3/5

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

Annotations already provide readOnlyHint: true, and the description confirms it's a read operation ('Returns'). The description adds value by detailing the sections and their content, but does not disclose any additional behavioral traits beyond what annotations already indicate.

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 starts with a clear instruction, then lists sections with brief descriptions. While the list is lengthy, it is necessary for the tool's purpose. It is well-structured and front-loaded.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, no output schema), the description is complete. It explains the tool's purpose, usage, and all possible parameter values comprehensively.

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 description coverage is 100% with a clear description for the 'section' parameter. The description adds significant meaning by listing all sections and their descriptions, going beyond the schema's enum list.

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?

The description clearly states 'Returns Prisme.ai documentation by section', specifying the verb (returns), resource (documentation), and the action (by section). It distinguishes itself from sibling tools, which are primarily operational (create, delete, get app, etc.).

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

Usage Guidelines4/5

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

The description provides clear usage guidance: 'Call with 'index' first to see available sections.' It also lists all available sections with descriptions, helping the agent choose. However, it does not explicitly mention when not to use or alternatives, but the context makes it clear.

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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