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informatics-isi-edu

Deriva MCP Server

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add_synonym

Expand vocabulary search by adding alternative names to existing terms. Specify vocabulary, term, and synonym to improve lookup flexibility.

Instructions

Add a synonym to an existing vocabulary term.

Synonyms are alternative names that can be used to look up a term.

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

Returns: JSON with status, name, synonyms list.

Example: add_synonym("Dataset_Type", "Training", "train") -> adds "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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention the return format (JSON with status, name, synonyms list) and gives an example. However, it does not disclose important behaviors such as error handling when the term does not exist, duplicate synonym handling, or idempotency. This is a moderate disclosure but not comprehensive for a mutation tool.

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 well-structured with Args, Returns, and Example sections. Every sentence adds value, and the example clarifies usage. It is appropriately sized and front-loaded with the core purpose.

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

Completeness5/5

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

For a simple tool with three parameters and no nested objects, the description covers the purpose, all parameter semantics, return format, and provides an example. It is self-contained and sufficient for an agent to select and invoke the tool correctly, even without annotations.

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 description provides detailed explanations for all three parameters in the Args section, including an example for vocabulary_name. Since schema description coverage is 0%, this fully compensates by adding meaningful semantics beyond the schema's bare type definitions.

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 clearly states the action ('Add a synonym to an existing vocabulary term') with a specific verb and resource. It distinguishes this tool from siblings like 'remove_synonym' and 'add_term' by focusing on adding an alternative name to an existing term.

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

Usage Guidelines3/5

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

The description provides context that synonyms are alternative names for lookup, which implicitly suggests when this tool is useful. However, it does not explicitly mention when not to use it or name alternative tools (e.g., 'add_term' for new terms, 'remove_synonym' for removing). The usage guidance is implied rather than stated.

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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