delete_article_suggestionDelete an article suggestion and its associated brief. Cascades to keyword and cluster links.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| context | Yes | One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / contextAdded value: +{
+ "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+ "type": "string"
+}Input schema / requiredPrevious value: -[
- "id"
-]New value: +[
+ "id",
+ "context"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true, so the description adds real value by disclosing the breadth of the deletion: the associated brief and cascading effects to keyword and cluster links. It does not fully define what 'cascades' does to those links, but it is still useful behavioral context beyond the annotations.
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?
Two short sentences with no filler. The primary action is front-loaded, and the second sentence earns its place by warning about side effects.
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?
For a simple destructive tool with two parameters and no output schema, the description covers what is deleted and what cascades. It could mention irreversibility or expected return value, but the destructiveHint annotation already communicates danger, so the definition is largely complete.
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 input schema documents 'context' fully but leaves 'id' semantically bare except for its UUID format. The description implies that id identifies the article suggestion to delete, adding modest meaning, but it provides no explicit parameter-level detail. With 50% schema description coverage, this is adequate but not strong.
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 states a specific verb ('Delete') and resource ('article suggestion') and adds the key scope: 'its associated brief. Cascades to keyword and cluster links.' This clearly distinguishes it from sibling read/create tools such as get_article_suggestion and add_article_suggestion.
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 intended use is implied by the verb 'Delete' and the cascade note, but the description never explicitly says when to use this tool versus alternatives such as update_article_status or set_opportunity_status. No when-not-to-use guidance is provided, leaving the agent to infer selection.
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 target a distinct resource and action, and the descriptions carefully cross-reference close alternatives (e.g., add_article_suggestion vs create_article_suggestion_with_input vs accept_idea). A few brief/read variants like get_article_brief, get_write_handoff, and download_brief_markdown could still be confused despite helpful explanations, so the set is not perfectly unambiguous.
Tool names follow a highly consistent verb_noun snake_case pattern throughout: get_*, list_*, create_*, update_*, set_*, add_*, delete_*, start_*, expand_*, etc. Even the less common names like lint_draft and remap_asset are still clear verb_noun constructions.
At 57 tools, this far exceeds the 50+ extreme threshold for a single MCP server. Even for a broad content workflow, the surface is overwhelming and would benefit from consolidation or splitting into focused servers.
The tool set covers the full content lifecycle: project setup, research runs, opportunity clustering, suggestion creation, brief generation, drafting, linting, publishing, reporting, and account management. Minor gaps exist—destinations and projects cannot be deleted via MCP, and there is no direct update for article suggestion metadata—but these are workable via the web UI or existing tools.