Get aggregate platform statistics.
Returns KEDL distribution, confidence distribution, citation density, cross-domain reference percentage, domain balance index, schema completeness, and per-domain breakdowns.
All metrics are computed at build time from content files.
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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?
No annotations are provided, so the description carries the full burden of disclosure. It adds important behavioral context: 'All metrics are computed at build time from content files,' informing the user that data may not be live. It also lists exactly which metrics are returned. It does not mention error conditions or authorization, but for a read-only stats getter, this 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 concise and well-structured. The first sentence states the core purpose, followed by a list of returned metrics, and ends with a note on computation timing. Every sentence adds value; there is no fluff or redundancy.
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 (0 parameters, no annotations) and the presence of an output schema, the description is largely complete. It covers what metrics are returned and that they are build-time computed. It could mention data interpretation or update frequency, but the output schema likely covers return details, so a high score is warranted.
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 input schema is empty (0 parameters), so the baseline is 4. The description reinforces that no inputs are needed by stating metrics are computed at build time from content files, which explains why the tool is parameterless. This adds value beyond the empty schema.
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 the tool's purpose: 'Get aggregate platform statistics.' It enumerates specific metrics (KEDL distribution, confidence distribution, etc.), which distinguishes it from sibling tools like get_cross_references or list_domains. The verb 'get' and resource 'aggregate platform statistics' are specific and 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 implies when to use this tool: whenever aggregate platform statistics are needed. It does not explicitly mention alternatives or exclusion criteria, but the enumeration of metrics and the phrase 'aggregate platform statistics' provide clear context. No conflicting sibling tool exists for this exact purpose, so the guidance is adequate though not explicit.
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 distinct purpose: retrieving entries, cross-references, parameters, stats, open questions, domains, search, registration, and submission. No two tools appear to perform the same function, and the descriptions clarify when to use each.
All tool names follow a consistent verb_noun pattern with snake_case: get_cross_references, list_domains, read_node, submit_proposal, etc. The verb prefixes (get, list, read, search, register, submit) align with their operations, making the pattern predictable.
With 9 tools, the set is well-scoped for a knowledge base system. Each tool covers a core functionality (retrieval, search, stats, submission, registration) without redundancy or bloat, striking the right balance for the stated purpose.
The tool surface covers the main workflows: reading, searching, cross-referencing, statistics, and submission. Minor gaps exist, such as no direct listing of all entries in a domain and no update/delete operations for submissions, but these are workable around and do not severely hinder typical usage.