Lightweight fallback extraction from raw text.
This is mainly for debugging and fallback use.
It is NOT the preferred production path.
Preferred production path:
- external model understands raw text
- external model outputs standard UserProfile fields
- call update_user_profile_from_model(...)
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'lightweight fallback' and 'for debugging', but does not explain what the extraction does internally, potential limitations, failure modes, or side effects. Given the lack of annotation support, this is insufficient.
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 extremely concise, using just a few lines and bullet points. It front-loads the core purpose and efficiently conveys the critical distinction from the preferred path. No filler or redundant information.
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?
The tool has one simple parameter and an output schema, so return values are covered. The description provides strong usage context and alternatives. However, it lacks behavioral details like error handling, edge cases, or what happens when extraction fails. For a fallback tool, this leaves some gaps, making it minimally adequate.
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?
The schema has one 'text' parameter with 0% description coverage. The description says 'raw text', which adds minimal meaning beyond the parameter name. It doesn't specify expected input format, content type, length limits, or examples. With no schema descriptions, the description should compensate but fails to provide substantial parameter guidance.
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 it's a 'lightweight fallback extraction from raw text', which identifies the verb (extract) and resource (user profile). It also distinguishes itself from the preferred path by explicitly naming update_user_profile_from_model as the production alternative, making the purpose specific and differentiated.
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 states this tool is 'mainly for debugging and fallback use' and 'NOT the preferred production path'. It goes further by describing the preferred path step-by-step, naming the alternative function. This provides clear when-to-use and when-not-to-use guidance.
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.
Most tools have clear distinct purposes, though 'update_user_profile' and 'update_user_profile_from_model' serve similar functions with different input methods, which could cause confusion. Overall, the boundaries are generally clear.
All tool names follow a consistent verb_noun pattern using snake_case, such as 'search_hotels', 'update_stay_dates', and 'get_hotel_details'. No naming style mixing or irregularities.
With 20 tools covering hotels, homestays, flights, activities, restaurants, profile management, and session handling, the count is well-scoped for a travel assistant server. Each tool serves a distinct function without bloat.
The tool set covers core travel planning needs: search, details, offers, and profile management. However, it lacks a tool to actually confirm or book an offer, which is a noticeable gap for a complete booking flow.