accept_ideaCommit an expanded idea (from expand_idea) to the content plan as a brief-ready article suggestion. Stores the verbatim idea as provenance, lands the suggestion in generating_brief, and forges its brief automatically (free tier: queues past the daily cap). Returns the new suggestionId.
| Name | Required | Description | Default |
|---|---|---|---|
| angle | Yes | ||
| title | Yes | ||
| ideaText | Yes | ||
| nicheFit | No | ||
| projectId | Yes | ||
| reasoning | No | ||
| searchIntent | Yes | ||
| demandEstimate | No | ||
| relatedQueries | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, but no detail on side effects. The description adds meaningful behavioral context: it stores provenance verbatim, 'lands the suggestion in generating_brief', and 'forges its brief automatically' – explaining the side effects beyond the annotation. It also disclosure free-tier queue behavior. This goes beyond the annotations, adding valuable transparency about what happens in the system state.
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?
Three sentences, each earning its place: the action + provenance, the state transitions, and the return value. Front-loaded with the verb and resource, no filler.
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?
Given the tool's complexity – it has side effects, workflows, and queue behavior – the description covers the purpose, usage context, side effects, and return value. However, it doesn't explicitly describe what happens to the idea if the daily cap is exceeded (does it still store provenance? is it queued immediately?), and it doesn't mention any prerequisites beyond 'from expand_idea'. With 9 parameters, it could clarify which optional ones matter, but the return type (suggestionId) is clear. Overall, strong but with small gaps.
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?
Schema description coverage is 0%, but the description references the parameters conceptually: it mentions the idea (ideaText), title, angle, and searchIntent as inputs to the committed suggestion. However, it doesn't explain the meaning of optional parameters like nicheFit, demandEstimate, relatedQueries, reasoning – those are left to the schema's type definitions. Given 0% coverage, the description partially compensates but not fully, so a 3 is appropriate.
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 ('Commit'), resource ('an expanded idea'), and outcome ('brief-ready article suggestion'), and explicitly references the upstream expand_idea workflow. This clearly distinguishes it from siblings like add_article_suggestion or create_article_suggestion_with_input, which likely handle direct creation rather than committing an expanded idea with provenance.
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 description implies the tool is the next step after expand_idea ('from expand_idea') and notes the free-tier queue behavior ('queues past the daily cap'). It doesn't explicitly state when NOT to use it or name alternative tools for direct creation, but the context is clear enough for an agent following a workflow. A small deduction for not stating exclusions explicitly.
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 have distinct purposes, but a few pairs could confuse an agent: add_article_suggestion vs create_article_suggestion_with_input, and get_article_brief vs download_brief_markdown vs get_write_handoff all deal with brief content. The detailed descriptions help disambiguate, but the overlap is real.
All tool names follow a consistent verb_noun pattern in lowercase snake_case (create_project, list_opportunities, generate_article_brief, lint_draft). There is no mixing of camelCase, acronyms, or vague verbs, making the naming predictable and readable.
50 tools is excessive for an MCP server, even for a broad platform like content operations. While the scope is large, this many tools will overwhelm agents and increase latency and context cost. Most practical servers are well under 25.
The tool surface covers the full content lifecycle: project creation, research, opportunity clustering, content planning, briefs, drafting, linting, publishing, and reporting. Minor gaps exist (e.g., no delete_project, no remove_destination, no direct analytics beyond distributions), but they are workarounds or handled in the web UI.