update_nicheUpdate a niche profile — name, industry, language, country, target customer, core problem, related terms. Use after the operator refines their market definition; omitted fields keep their current values.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| name | Yes | ||
| country | No | ||
| industry | No | ||
| language | No | ||
| coreProblem | No | ||
| relatedTerms | No | ||
| targetCustomer | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and destructiveHint=true, so the mutation/destruction profile is covered. The description adds one genuinely useful behavioral trait: 'omitted fields keep their current values', clarifying the partial-update semantics. However, it does not disclose what the destructive aspect damages or any consequences of an update.
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 lean sentences, front-loaded with the action and target, followed by usage timing and a key behavioral clause. Every sentence earns its place; no filler or repetition of schema type information.
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 an 8-parameter mutation with no output schema, the description covers purpose, timing, and merge semantics — a solid baseline. However, it does not describe the return/response value at all, which matters since no output schema exists, nor does it explain error conditions or what destructive side effects the update might trigger. Adequate but with clear 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%, so the description must compensate. It lists most parameters semantically (industry, country, core problem, related terms), which goes beyond the bare schema properties, but it adds no format, constraints, or deeper meaning beyond what the schema's types and names already imply. The partial-update rule is the most valuable semantic addition.
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 (Update) and resource (niche profile) and enumerates the updatable fields (name, industry, language, country, target customer, core problem, related terms). This clearly differentiates it from sibling tools like update_asset_profile or update_article_status by naming the target resource.
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 provides clear when-to-use context: 'Use after the operator refines their market definition.' It does not name alternative tools or explicit when-not-to-use conditions, but the trigger for invoking it is well specified.
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.