agentseo_backlinks_new_lost_timeseriesTrack new and lost backlinks/referring domains over time and classify link momentum, leakage, and volatility.
| Name | Required | Description | Default |
|---|---|---|---|
| sync | No | ||
| target | Yes | ||
| date_to | No | ||
| date_from | No | ||
| group_range | No | month | |
| include_subdomains | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses the behavioral trait of tracking over time and classifying, but does not mention permissions, rate limits, or output format. Adequate but not comprehensive.
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 concise sentence of about 15 words, front-loaded with the core action. No redundant 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?
Given the complexity of the tool (multiple parameters, time-series analysis) and lack of output schema, the description is too brief. It does not explain parameters, output, or how to use it alongside sibling tools.
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% and the description does not explain any parameter meaning. It only describes the tool function, so the agent gets no help understanding parameters like date range, group_range, or sync.
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 it tracks new and lost backlinks and referring domains over time, and classifies link momentum, leakage, and volatility. This distinguishes it from siblings like backlinks_list or backlinks_referring_domains which are simple lists.
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?
No guidance on when to use this tool versus alternatives like backlinks_summary or backlinks_competitors. It does not specify prerequisites or typical use cases.
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.
Each tool has a clearly distinct purpose due to specific naming (e.g., backlinks_anchors vs backlinks_list) and grouped prefixes. Overlaps are minimal and clarified by descriptions.
All tools follow a consistent snake_case pattern with the 'agentseo_' prefix and domain-specific nouns/verbs (e.g., agentseo_backlinks_anchors, agentseo_content_brief). No mixed conventions.
With 45 tools, the server is on the heavy side for an agent to navigate, though the tools are logically grouped into subdomains (backlinks, content, etc.), justifying the count.
The toolset covers a wide range of SEO tasks including backlinks, content, keywords, SERP analysis, and local SEO. Minor gaps exist (e.g., advanced automation), but core workflows are well-represented.