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add_labels

Assign custom labels to FreshRSS articles for better organization and categorization of your RSS feed content.

Instructions

Add labels to articles

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
articleIdsYesArticle IDs to modify
labelsYesLabel names to add/remove

Implementation Reference

  • The handler function that executes the add_labels tool logic.
    wrapTool('add_labels', async (args: z.infer<typeof modifyLabelsSchema>) => {
      await client.tags.addToArticles(args.articleIds, args.labels);
      return textResult(
        `Added labels [${args.labels.join(', ')}] to ${args.articleIds.length.toString()} article(s).`
      );
    })
  • Registration of the add_labels tool.
    server.registerTool(
      'add_labels',
      {
        description: 'Add labels to articles',
        inputSchema: modifyLabelsSchema,
      },
      wrapTool('add_labels', async (args: z.infer<typeof modifyLabelsSchema>) => {
        await client.tags.addToArticles(args.articleIds, args.labels);
        return textResult(
          `Added labels [${args.labels.join(', ')}] to ${args.articleIds.length.toString()} article(s).`
        );
      })
    );
  • The input schema definition for adding or removing labels.
    export const modifyLabelsSchema = z
      .object({
        articleIds: z.array(z.string()).min(1).describe('Article IDs to modify'),
        labels: z.array(z.string()).min(1).describe('Label names to add/remove'),
      })
      .strict();
Behavior2/5

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

No annotations are provided, so the description carries full burden of behavioral disclosure. It fails to mention idempotency (what happens if labels already exist), side effects, or success/failure behavior for this state-modifying operation.

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?

Extremely concise at three words with no wasted text. However, for a mutation tool, this brevity may be insufficient rather than optimally structured, as it front-loads nothing about risks or constraints.

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

Completeness2/5

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

As a mutation tool with no output schema and no annotations, the description should disclose behavioral traits and return value expectations. It provides neither, leaving significant gaps for safe agent operation.

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?

With 100% schema description coverage ('Article IDs to modify', 'Label names to add/remove'), the schema fully documents parameters. The description adds no additional semantic context beyond what the schema provides, warranting the 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 (add) and resource (labels to articles), providing specific purpose. However, it lacks explicit differentiation from sibling tool 'remove_labels' regarding when to use each.

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?

No guidance provided on when to use this tool versus alternatives like 'remove_labels', nor any prerequisites (e.g., whether labels must exist first) or conditions for use.

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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