normalizeClean up text with ellipses, fragments, and run-on thoughts into coherent prose. Use when a user types stream-of-consciousness or fragmented input.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Fragmented text to normalize. |
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, idempotentHint: true, and destructiveHint: false, covering the safety profile. The description adds minimal behavioral context beyond what the annotations provide; it mentions the transformation but not details about output format or edge cases.
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?
The description is two sentences with no redundant phrasing. It front-loads the primary action and adds a usage condition, every word earning its place.
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 simple one-parameter tool with strong annotations, the description is adequate. It explains what the tool does and when to use it. The lack of an output schema is mitigated by the phrase 'into coherent prose,' hinting at the return value, though an explicit output type would improve completeness.
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 description coverage is 100% (the 'text' parameter is described as 'Fragmented text to normalize'). The description enriches this by specifying what fragmented text looks like: ellipses, fragments, and run-on thoughts. This adds meaning beyond the schema, so a score above baseline is warranted.
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?
The description clearly states the tool's function: 'Clean up text with ellipses, fragments, and run-on thoughts into coherent prose.' This provides a specific verb and resource. It does not explicitly distinguish from the sibling tool 'rewrite', but the focus on fragmented input implicitly 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?
The description explicitly says 'Use when a user types stream-of-consciousness or fragmented input,' giving clear context for when to apply the tool. It does not mention when not to use it or alternatives, so it falls short of a 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.