expand_ideaScore a raw article idea against the project's niche without writing anything to the plan. Returns a title, angle, search intent, related queries, a coarse demand estimate, and a 0-100 niche-fit score. Pass the returned fields to accept_idea to commit the idea as a brief-ready suggestion.
| Name | Required | Description | Default |
|---|---|---|---|
| ideaText | Yes | ||
| projectId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'without writing anything to the plan', which controls expectations about side effects beyond the raw readOnly/destructive hints. It also details the exact return values (title, angle, search intent, related queries, demand estimate, niche-fit score), which is valuable since there is no output schema. It could add error behavior or rate limits, but it provides solid behavioral context.
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 tight sentences. The action is front-loaded, the core behavior 'without writing anything to the plan' appears prominently, and the return contract is listed efficiently. No filler or redundancy.
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 tool with only two parameters and no output schema, the description covers what inputs mean, what results are produced, and how the tool fits the larger workflow (pass to accept_idea). It is complete enough for an agent to correctly invoke and interpret its result without needing additional documentation.
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%, so the description bears the weight. It maps 'raw article idea' to ideaText and 'the project's niche' to projectId, giving meaningful semantics to both parameters. It does not restate string length limits or UUID format, but it makes the purpose of each parameter clear enough for an agent to call the tool correctly.
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 uses a specific verb ('Score') and a clear resource ('a raw article idea against the project's niche'), and immediately differentiates this from committing an idea by noting it does not write to the plan. It also names the concrete output (title, angle, search intent, related queries, demand estimate, 0-100 niche-fit score), leaving no doubt about what the tool does.
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?
It explicitly says not to write anything to the plan and instructs to pass the returned fields to accept_idea to commit. This provides strong usage context and routes to the appropriate next step. A clear alternative ('check_idea') exists among siblings, but no direct comparison 'use this instead of check_idea' is given, so it stops just short of full guidance.
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.