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Perssua

@perssua/mcp

Official
by Perssua

Create a Perssua assistant

create_assistant

Build a tailored assistant in Perssua by collecting the user's requirements, writing its system instructions, attaching knowledge, then opening a new session.

Instructions

Create a new custom assistant in the Perssua desktop app (name + system-prompt instructions, optional knowledge) and open a session with it. BEFORE calling this, interview the user briefly so the assistant fits: (1) what is the assistant's goal / what sessions will it support, (2) how should it respond (tone, format, language), (3) what knowledge should it carry (notes, files, background), (4) what should the first session start with. Then write the instructions yourself from those answers. Knowledge text and files become the assistant's permanent context, not part of the first message. Runs on the same machine as the Perssua app.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesAssistant name shown in Perssua, e.g. "Interview Coach".
filesNoLocal text-file paths whose contents are stored as the assistant's knowledge.
sourceNoCalling product, e.g. "claude", "chatgpt", "grok". Defaults to the PERSSUA_MCP_SOURCE env var or "mcp".
categoryNoOptional category label for the assistants library.
knowledgeNoFree-text knowledge stored with the assistant (background, notes, decisions).
autoSubmitNoSubmit the first prompt immediately (default true). When false, it is prefilled for review.
firstPromptNoFirst user message for the session that opens with the new assistant.
instructionsYesSystem prompt defining the assistant: goal, behavior, tone, and response format.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the annotations, the description discloses important behavioral details: knowledge and files become permanent context, they are not part of the first message, a session is opened immediately, and the tool runs on the same machine as the Perssua app. This adds meaningful context without contradicting the annotations.

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?

The description is front-loaded with the core action and then provides structured, numbered pre-call guidance. Every sentence carries useful information, including the environment note about running on the same machine, with no fluff or repetition.

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?

For a tool with 8 parameters and no output schema, the description provides rich context: what to do before calling, how to craft instructions, the permanence of knowledge, and the local-machine runtime. Combined with the fully described schema, an agent has everything needed to invoke the tool correctly.

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?

The schema already documents all parameters with 100% coverage, so the baseline is 3. The description adds extra semantic value by clarifying that knowledge and files become permanent assistant context rather than part of the first message, which directly disambiguates knowledge, files, and firstPrompt.

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 states a specific verb and resource: create a new custom assistant in the Perssua desktop app and open a session with it. It also names the key ingredients (name, system-prompt instructions, optional knowledge), clearly distinguishing this from sibling tools like list_assistants and start_session.

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 gives unusually concrete usage guidance by requiring a brief user interview before calling and specifying exactly what to ask. It does not explicitly list sibling alternatives or when-not-to-use cases, but the context for when this tool is appropriate is very 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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