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mess_delete_extra_registration

DestructiveIdempotent

Remove an extra meal registration from the IIITH Mess System by its ID to manage your meal schedule effectively.

Instructions

Delete an extra registration by its ID (passed as query param).

Args: params: auth_key/session, id (extra registration ID)

Returns: JSON array of remaining ExtraRegistration objects

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations provide readOnlyHint=false, destructiveHint=true, idempotentHint=true, and openWorldHint=true, covering key behavioral traits. The description adds that it deletes by ID and returns a JSON array of remaining objects, which offers some context beyond annotations (e.g., output format and effect on data). However, it doesn't detail permissions, rate limits, or error conditions. No contradiction with annotations exists.

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 front-loaded with the core action, followed by Args and Returns sections in a structured format. It avoids unnecessary words, but the Args section could be more integrated (e.g., merging with the first sentence). Overall, it's efficient with minimal waste, though not perfectly streamlined.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a destructive tool with 0% schema coverage and an output schema (implied by 'Returns'), the description is moderately complete. It covers the action, parameters at a high level, and return value, but lacks details on authentication nuances, error handling, or sibling differentiation. The output schema likely documents the JSON array structure, reducing the burden, but gaps remain for safe invocation.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must compensate. It lists params as 'auth_key/session, id' and notes the ID is for the extra registration, adding basic semantics beyond the schema's property names. However, it doesn't explain the relationship between auth_key and session (e.g., alternatives), format of ID, or other nuances. With 0% coverage, this partial compensation justifies a baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Delete') and resource ('an extra registration by its ID'), making the purpose unambiguous. It distinguishes from siblings like 'mess_cancel_registration' or 'mess_delete_monthly_registration' by specifying 'extra registration', though it doesn't explicitly contrast with them. The title is null, so the description carries the full burden, which it does adequately.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'mess_cancel_registration' or 'mess_delete_monthly_registration'. It mentions the ID is passed as a query param, but this is a technical detail rather than usage context. There are no prerequisites, exclusions, or comparisons to sibling tools, leaving the agent with minimal contextual direction.

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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