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informatics-isi-edu

Deriva MCP Server

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remove_synonym

Remove an alternative name from a controlled vocabulary term to keep term lists accurate and up to date.

Instructions

Remove a synonym from an existing vocabulary term.

Args: vocabulary_name: Name of the vocabulary table (e.g., "Dataset_Type"). term_name: Primary name of the term to remove synonym from. synonym: Alternative name to remove.

Returns: JSON with status, name, updated synonyms list.

Example: remove_synonym("Dataset_Type", "Training", "train") -> removes "train" as synonym

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
synonymYes
term_nameYes
vocabulary_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool removes a synonym, returns JSON with status/name/updated synonyms, and gives an example. However, it does not explain error behavior (e.g., what happens if the synonym or term doesn't exist) or state whether the operation is reversible. This is reasonable for a simple removal tool but leaves gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-sentence summary followed by Args, Returns, and an Example. Every section adds value, and the example clarifies usage without waste.

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

Completeness4/5

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

For a 3-parameter tool with an output schema, the description gives the key information: what it does, parameter meanings, return format, and an example. It lacks detail on edge cases and prerequisites, but this is a minor gap given the tool's simplicity.

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?

The schema has 0% coverage with no descriptions, but the description documents each parameter in the Args section: vocabulary_name, term_name, and synonym, with an example mapping. It fully compensates for the schema's lack of detail.

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 states a specific verb+resource+object: 'Remove a synonym from an existing vocabulary term.' This clearly distinguishes it from sibling tools like add_synonym and delete_term, and the example reinforces the exact action.

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

Usage Guidelines4/5

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

The description clearly implies when to use the tool—when a synonym needs to be removed from a term—and provides a concrete example. However, it does not explicitly mention when not to use it or name alternative tools (e.g., add_synonym), so it lacks explicit exclusions.

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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