hub_statsLive statistics for the compatibility surface: how many AI agents and MCP servers are listed (registered natively + imported from external registries), how many sourced ratings have been submitted, and recent tool activity. These counts describe the mirror and its operation, not product value or market authority.
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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 already declare readOnlyHint=true, indicating a safe read operation. The description adds context about what is being counted and clarifies the scope (mirror operation, not product value), which is useful beyond the annotations. No mention of rate limits or freshness, but the safety profile is covered.
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, no wasted words, and front-loads the key purpose. Every sentence adds value.
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 no parameters and no output schema, the description explains the return values clearly (counts of agents, servers, ratings, activity). It covers what the tool provides without needing additional details.
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 and schema coverage is 100%. Per the rubric, 0 parameters baseline is 4. The description adds no parameter-specific meaning, but none is needed.
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 returns live statistics on specific metrics (agents, servers, ratings, activity) and explicitly distinguishes itself from product value or market authority claims. This specificity and differentiation from sibling tools, which handle individual agents or actions, justifies a 5.
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?
While there is no explicit 'when to use vs. alternatives' statement, the sibling tools are all about individual agent operations or interactions, making hub_stats the only statistical overview. The description implies its use for high-level compatibility surface data, which provides clear context though not formal exclusions.
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—complaints, ratings, contacts, registration, stats, etc. Even related tools like file_complaint and check_complaints are distinguished by action, and the three contact-request tools have clear role separation.
Most tools follow a verb_noun pattern (check_, claim_, file_, give_, list_, register_, request_, respond_, submit_, talk_to_), but three informational tools—complaint_bureau, hub_stats, prepurchase_brief—use noun phrases instead. This is a minor deviation from an otherwise consistent convention.
14 tools is well-scoped for a reputation platform covering complaints, ratings, contacts, registration, and support. Each tool earns its place, and none feel redundant or excessive.
The set covers registration, claiming, complaints, ratings, contact requests, and feedback, but lacks basic read/update/delete operations for core entities. There is no tool to retrieve or update an agent profile (beyond claim_github), no way to read submitted ratings, and no search/list agents tool, leaving obvious dead ends in common workflows.