research_domain_overviewEstimated organic traffic, keyword count and top keywords for ANY domain — use to size up a competitor the user mentions. Costs credits; cached results are free, and repeated queries within a week hit the cache. For the user's own site prefer gsc_* (free, real data).
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain, e.g. competitor.com |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=false and destructiveHint=false, but description adds valuable context: it costs credits and caches results for a week. This goes beyond annotations by disclosing cost and side-effect of credit consumption, though it doesn't detail all potential behaviors (e.g., rate limits). No contradiction.
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?
Three sentences, each serving a distinct purpose: purpose, cost/caching, and alternative tool guidance. Front-loaded with the core purpose, no redundancy or filler.
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 single-parameter tool with no output schema and minimal annotations, the description fully covers purpose, usage, cost, caching, and alternatives. It tells the agent exactly what to expect and when to use it, leaving no critical gaps.
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 covers 100% of the single parameter (domain) with a clear example. The description reinforces that it works for ANY domain and mentions competitor use, but this is tool-level guidance rather than parameter-specific detail. Baseline 3 is appropriate since schema already handles parameter meaning.
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?
Description clearly states it provides estimated organic traffic, keyword count, and top keywords for any domain, with an explicit use case (sizing up competitors). This distinguishes it from sibling tools like research_backlinks and gsc_overview, which focus on other data types.
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?
Explicitly tells when to use (competitor analysis) and when not (for user's own site, prefer gsc_* tools). Also explains caching behavior and cost implications, giving clear context for tool selection.
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 targets a distinct resource and action: backlink_directories vs research_backlinks differ (directories vs actual backlinks), gsc_* tools cover different views of own data (overview/pages/queries), research_* tools each address a separate question (competitors, domain overview, AI mentions, backlinks), and the trace/audit/report lifecycle tools have clear roles (start, poll, read, unlock). No two tools appear to do the same thing.
There is a clear pattern: verb_noun for actions (get_trace, read_report, start_trace, unlock_report, search_reports), research_* prefix for external lookups, gsc_* prefix for own Search Console data, and site_audit_* for audits. Minor inconsistency: backlink_directories doesn't follow the verb_noun style (no verb), but overall the naming is predictable and grouped logically.
At 15 tools, this is at the upper end of the well-scoped range (3-15). Each tool serves a distinct function within the SEO/growth analysis domain, so the count feels justified rather than bloated. However, it's slightly heavy, which is why it misses a 5.
The tool surface covers the full lifecycle of growth reports (search, read, start trace, poll, unlock), ownsite GSC data (overview, pages, queries), competitor research (domain, backlinks, brand, competitors), and site audits (start/get). Minor gaps include no way to delete or manage reports beyond reading, and no direct keyword research beyond GSC queries, but the core workflows are complete.