Generate a draft reply or outreach email for one processed mention. Returns draft text for a human to review and never posts, sends, or publishes anything.
| Name | Required | Description | Default |
|---|---|---|---|
| mention_id | Yes | ||
| draft_intent | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond the annotations by disclosing that it never posts, sends, or publishes anything, and that the output is for human review. This is particularly useful given readOnlyHint=false, as it clarifies the true side-effect profile, though it does not cover storage or retrieval of drafts.
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 states the core action and then adds a critical safety qualifier. Every part is informative, and there is no filler or repetition.
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 absence of an output schema, the description explains the return value ('draft text for human review'), notes the prerequisite ('processed mention'), and clarifies side effects. It is mostly complete for a simple two-parameter tool, though it could mention error cases or whether a draft is persisted.
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?
With 0% schema description coverage, the description must compensate for parameter meaning. It does so by referencing 'one processed mention' (mapping to mention_id) and 'reply or outreach email' (mapping to draft_intent enum), thereby adding semantic context even though it does not literally name the parameters.
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 ('Generate') with a clear resource ('a draft reply or outreach email') and scopes it to 'one processed mention.' It also explicitly distinguishes itself from siblings by stating it never posts, sends, or publishes anything, making its purpose unambiguous.
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 context for when to use the tool: for generating a draft for a processed mention. It implicitly differentiates from sibling tools (get/search/list mentions, create/update keywords) by focusing on draft generation, but it does not explicitly state when not to use it or name alternative tools.
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.
