rewriteGet rewrite instructions for adapting text to a specific style: technical, plain, socially_gentle, concise, detailed, or direct. Use when communication needs tone adjustment.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to rewrite. | |
| style | No | Target audience style. | plain |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. Description adds behavioral context by stating the tool returns 'rewrite instructions' and lists the style enum, which goes beyond schema. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the core action, and the style list is compact. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a two-parameter tool with annotations, the description is largely sufficient. However, it introduces ambiguity by saying 'Get rewrite instructions' versus the title's 'Rewrite text', leaving uncertainty about whether the output is rewritten text or instructions about rewriting. No output schema exists, so this ambiguity is not resolved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters (text and style) with descriptions and enum values. The description's mention of styles is redundant with the schema's enum, so it adds no additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Get rewrite instructions for adapting text to a specific style' with a clear verb and resource. It lists six target styles, distinguishing it from sibling tools that focus on interpretation or normalization. The use case 'tone adjustment' further differentiates it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use when communication needs tone adjustment' provides an explicit condition for when to invoke the tool. It doesn't name alternatives or state when not to use it, hence a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes: interpret, prepare_prompt, and route_intent all deal with ambiguity detection and clarification decisions. Their boundaries are subtle (e.g., prepare_prompt returns a commit-vs-clarify decision but defers to route_intent as authority), which will likely confuse an agent trying to select the right tool. The descriptions try to differentiate them, but the functional overlap is significant.
The tool names mix conventions: single verbs (interpret, normalize, rewrite) and verb_noun compounds (calibrate_profile, prepare_prompt, render_reply, route_intent). While the verb_noun names are consistent among themselves, the single-verb names break the pattern, giving an overall mixed but readable set.
8 tools is a well-scoped count for the server's purpose (agent tuning and translation bridge). Each tool serves a distinct function in the pipeline without being excessive, and the number is within the ideal 3-15 range.
The core lifecycle (calibrate -> prepare -> route -> render) is well-covered, and auxiliary tools (interpret, normalize, rewrite, recommend) fill common side needs. Minor gaps exist, such as no explicit profile update tool and redundancy between prepare_prompt and route_intent, but agents can work around these.