List every keyword this MentionDrop account monitors, with its id, role (own_brand, competitor, or industry), and active state. Call this first to discover the keyword ids and role names the other tools accept.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds concrete behavioral detail beyond the readOnlyHint and destructiveHint annotations by specifying exactly what the tool returns (id, role, active state). It doesn't mention potential pagination or volume limits, but for a simple list endpoint this is a minor omission.
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: the first states the action and output fields, the second gives usage guidance. Every word is purposeful and there is no redundancy or fluff.
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 zero-parameter, read-only list tool, the description fully covers what the return contains and why to call it first. It is self-sufficient even without an output schema, and the annotations confirm its safe read-only nature.
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 tool has zero parameters, so the description naturally doesn't add parameter details. With 100% schema coverage (empty schema), the baseline is 4, and the description appropriately focuses on output scope instead.
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 ('List') and resource ('every keyword this MentionDrop account monitors') and explicitly names the output fields (id, role, active state). It clearly differentiates itself from sibling tools by positioning it as the discovery endpoint for keyword ids and role names.
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 provides explicit usage guidance: 'Call this first' to discover the ids and role names needed by other tools. While it doesn't name alternatives or state when not to use it, the instruction is clear and actionable for a discovery tool.
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 the specialized mention getters (get_competitor_signals, get_pain_signals) overlap with get_recent_mentions since that function also supports role filters. The unique filtering criteria for signals are described, reducing ambiguity, but an agent might hesitate between these options.
Tool names mostly follow a verb_noun snake_case pattern, but there is inconsistency between list_keywords and get_recent_mentions for list-like operations. The specialized signal getters also deviate slightly from the standard pattern, though all names remain readable.
With 11 tools, the server is well-scoped for a mention monitoring service. The tool set covers keyword management, mention retrieval, search, digest, and reply drafting without unnecessary overlap or excessive granularity.
The surface provides strong coverage of the domain: keyword lifecycle (create, list, update with pause), mention retrieval (by id, recent, search, digest, signals), and a feedback loop. Minor gaps exist, such as the lack of a delete_keyword or single-keyword getter, but these are workaround-able.
