get_keyword_detailGet a keyword detail — pages ranking for it with positions and ownership, plus related keywords already tracked in the project.
| Name | Required | Description | Default |
|---|---|---|---|
| projectId | Yes | ||
| queryHash | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description does not need to restate safety. It adds value by disclosing the response contents (ranking pages, positions, ownership, related keywords), which goes beyond the annotation. However, it does not mention potential errors, rate limits, or pagination, but for a simple read operation this is acceptable.
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 a single, front-loaded sentence that identifies the resource and summarizes the output with no filler or unnecessary detail. Every word adds value.
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 read-only tool with two parameters and no output schema, the description provides a high-level summary of the return value. However, it omits parameter semantics and does not explain terms like 'ownership' or 'related keywords'. It is minimally adequate but could be improved by clarifying queryHash.
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 does not explain what queryHash or projectId represent beyond their names. The tool name implies queryHash identifies the keyword, but this is not explicit. The description fails to clarify the parameters, leaving the agent to infer their purpose.
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 and resource ('Get a keyword detail') and enumerates the returned data (pages ranking, positions, ownership, related keywords). This clearly distinguishes it from sibling detail tools like get_competitor_detail and get_opportunity_detail, and from list_keywords which would provide a list.
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 usage when keyword-specific detail is needed, but it does not explicitly contrast this with list_keywords or other alternatives, nor mention any exclusions (e.g., 'use list_keywords for an overview'). The guidance is inferred from the tool name and context rather than explicitly stated.
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.