Get system-wide LTS statistics. Returns two sections: (1) lts_records stats (total, by confidence, linked/unlinked, active/expired, unique developers/cities), and (2) project LTS stats (total records, verified, expired, expiring within 30 days, projects with LTS).
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently details the two return sections and their contents, giving a clear picture of the output. It does not mention caching, permissions, or computation method, but for a read-only stats endpoint, this level is sufficient.
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, front-loads the main purpose, and uses a clear numbered list for the return sections. Every word contributes value; there is no fluff 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 tool's simplicity (no parameters, no output schema), the description fully accounts for what the caller needs to know: the scope and the exact contents of the response. The sibling tool names further reinforce the system-wide vs. segmented distinction. No additional context is necessary.
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 there is no parameter semantics to explain. The baseline score of 4 applies because there is nothing that requires elaboration, and the description does not need to compensate for schema gaps.
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 begins with a specific verb and resource: 'Get system-wide LTS statistics.' It clearly distinguishes this tool from siblings like lts_by_city or lts_by_developer by emphasizing the system-wide scope. It also enumerates the exact return sections, making the 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 phrase 'system-wide' provides clear context for when to use this tool versus more narrowly scoped sibling tools. However, it does not explicitly name alternatives or state exclusions, so the guidance is implied rather than directly actionable. This is a minor gap.
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 aspect of LTS data (by city, developer, law, region, etc.) with no overlapping purposes. The descriptions clearly delineate their unique use cases.
All tools follow the consistent pattern 'lts_<descriptive_noun_or_phrase>' using snake_case. The naming is uniform and predictable, aiding agent selection.
With 12 tools, the set is well-scoped for a data query and analysis server. Each tool serves a clear function without redundancy or overload.
The tool set covers all necessary query dimensions (city, developer, law, region, expiry, stats, trends) and includes a search, check, and project-level view. No obvious gaps for a read-only analytics server.