get_projectGet one project — its canonical name (operator-set, falling back to site brand then niche name), niche profile, target audience, current research status, and postsPerWeek (the operator’s publishing cadence, settable via set_posting_cadence; sizes the pitch report’s 90-day sequence). Address the project by name; niche.name is the market niche, not the project name.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint: true covers the read-only nature. The description adds value by listing the fields returned, which informs the agent of the response shape. However, it does not describe error behavior, permissions, or any other side effects beyond the annotation, so transparency is only partially enhanced.
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?
The description is verbose and redundant. It spends words on clarifying field semantics ('operator-set, falling back...', 'Address the project by name; niche.name is the market niche...') that are not necessary for the tool's purpose. The structure is a single long sentence that could be split and simplified. This detracts from readability.
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 getter with one parameter, the description adequately explains the returned fields, which helps an agent interpret the response. However, it lacks usage guidance (when to call this vs list_projects) and does not mention any potential errors or prerequisites. The annotation provides read-only context, but the overall completeness is moderate.
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 schema fully defines projectId as a UUID with format and pattern. The description does not add any additional meaning to the parameter; it neither clarifies that projectId is the project's unique identifier nor provides any context about where to find it. Thus, the description contributes nothing beyond the schema.
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 clearly states that the tool retrieves a single project and enumerates the returned fields (canonical name, niche, audience, research status, postsPerWeek). The verb 'Get' is specific, and the tool is distinct from list_projects. However, it does not explicitly name the sibling list_projects as the alternative for fetching multiple projects, so it is not fully distinguished.
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 does not explicitly state when to use this tool versus alternatives like list_projects. It implies use when a single project's details are needed, but this is not stated. The agent must infer usage from the tool name and the sibling list, which is a gap.
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.