agent4.io

improve_prompt

Rewrite a field's text to work better as an AI system prompt; returns the improved text.

field ∈ persona | task | greeting | storyline_task | storyline_opening
        | storyline_ai_trigger | storyline_ai_criteria
        | skill_description | skill_instructions | kb_description | kb_instructions
— determines the rewrite target (a Task should read like commands, a KB description says
"what's inside / when it's relevant", a skill description says "when to use" in one line, …).
current = the current text (empty = draft from scratch); hint = the direction you want this
time (empty = general polish); context = on-site context (which KB is being edited, values of
sibling fields) — the more specific, the better the fit.
Returns only the improved text, no explanation, no wrapper — the caller places it next to the
original for a human to accept or reject.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`hint`	No
`field`	Yes
`context`	No
`current`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 28, 2026

TDQS

A4.9/5.0

Behavior5/5

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

With no annotations, the description fully carries behavioral disclosure. It explicitly states the return format: 'Returns only the improved text, no explanation, no wrapper,' and mentions that the caller places it next to the original for human approval. It also describes edge behavior (empty current = draft from scratch). This is transparent and actionable.

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 moderately sized but every sentence earns its place. The line-separated list of field values is structured and readable, though slightly dense. It avoids fluff and stays focused on essential operational details.

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 4 parameters, no annotations, and an output schema present, the description covers all necessary aspects: purpose, parameter roles, expected output, and usage context. It is sufficiently complete for an agent to select and invoke the tool correctly.

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

Parameters5/5

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

Schema coverage is 0%, but the description compensates thoroughly. It explains 'field' with the full enumerated list and semantics, describes 'current' as existing text, 'hint' as direction, and 'context' as on-site context with a note on specificity. This adds meaning far beyond the bare schema.

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 the tool's function: 'Rewrite a field's text to work better as an AI system prompt; returns the improved text.' It specifies the resource (a field's text) and distinguishes from siblings like diagnose_prompt by focusing on rewriting rather than analyzing.

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?

Provides explicit guidance on when to use: for improving any of the listed field types. It explains the meaning of each field category and advises providing context for better results. It also clarifies optional parameters (hint, current, context) and what they do, giving clear direction for effective use.

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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