list_competitorsList companies for a project — marked competitors (important / default / ignored) and SERP-discovered companies (unmarked). Sorted important → default → unmarked → ignored, then by domain. Pass importance to filter to one bucket. Capped at 100.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | Yes | ||
| projectId | Yes | ||
| importance | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint: true, and the description adds sorting order, cap at 100, and the importance bucket behavior. The description does not contradict annotations and adds detail about the output ordering and filtering, which is useful for the agent to predict behavior. It doesn't mention pagination or what happens if limit is exceeded, but annotations cover the read-only nature.
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 two sentences with no fluff. It front-loads the main purpose, then provides sorting, filtering, and cap details. Every sentence adds value and it is appropriately compact.
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 is a listing operation with 3 params and no output schema, the description covers the input usage, output ordering, and limits. It doesn't detail the return structure (fields per competitor) but that might be expected from the domain or not required. The annotations and description together are sufficient for an agent to call it correctly, though a note about default behavior without 'importance' could be clearer, but it's not a major gap.
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 explain parameters. The description explains 'importance' (filter to one bucket) and 'projectId' is implied as the project. It mentions the limit cap of 100 implicitly via 'Capped at 100' when limit is set. Without parameter-specific descriptions, the description compensates reasonably, but some parameters like 'limit' semantics are clear from schema, and the description adds the sorting context. It adds meaning beyond the schema, so a 4 is justified.
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 the tool lists companies for a project, distinguishes between marked (important/default/ignored) and unmarked (SERP-discovered) competitors, and specifies the sorting order and cap. This is specific to the 'list' verb plus resource, and the mention of importance filtering distinguishes it from sibling tools like get_competitor_detail and set_competitor_importance.
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 conveys when to use it for listing competitors and explicitly mentions the optional 'importance' parameter for filtering. It implies this tool is for listing rather than modifying (which is done by set_competitor_importance) but does not name alternatives or when-not-to-use. The context of sorting and cap helps the agent decide if it suits the need.
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.