ranked_statsCounts of what this server holds — file-backed dataset by evidence tier, kind and regulator with each register's access date; whether the live database is configured — plus the provenance rules. Use to explain provenance to a user.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
The description mentions 'file-backed dataset' and 'whether the live database is configured,' giving some insight into internal checks. It does not contradict the readOnly/idempotent annotations, and it adds context beyond them. However, it does not elaborate on potential edge cases or side effects (which are minimal for a read-only tool).
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 long sentence with a dash and semicolon, creating a run-on structure that is difficult to parse. Phrases like 'file-backed dataset by evidence tier, kind and regulator with each register's access date' are cluttered and reduce clarity. It would be more concise and structured if broken into separate sentences or bullet points.
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?
The description provides fragmented details (counts, configuration status, provenance rules) but does not clearly specify the output format or what exactly the tool returns. It lacks a straightforward explanation of the return value, making it incomplete for an agent to confidently use the tool without further inference.
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 no parameters, so schema coverage is effectively 100%. Per the guidelines, this yields a baseline score of 3. No additional parameter details are necessary.
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 suggests a summary/statistics tool with 'Counts of what this server holds' and explicitly states 'Use to explain provenance to a user.' However, the verb is missing; it is unclear whether the tool returns counts, displays them, or performs something else. The phrasing is more of a content description than a clear action.
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 gives a direct usage scenario: 'Use to explain provenance to a user.' This provides clear guidance on when to invoke this tool. It does not contrast with sibling tools, but the stated purpose is specific enough.
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 query type: bulk regulator lists, single-name licence checks, single-product payment eligibility, multi-product comparison, dataset search, place details, rankings, hub browsing, and stats. Descriptions explicitly cross-reference the related tools (e.g., find_business directs users to rankings/place), so an agent should not confuse them.
All tool names share the consistent ranked_ prefix and snake_case style, which is predictable. However, the suffix mixes clean verb-object forms (check_licence, find_business, compare) with other patterns (by_regulator, can_nigerian_pay, place, rankings, hubs), so it is not a uniform verb_noun convention.
Nine tools is well within the ideal range for a read-only Nigerian business/regulator data server. Each tool earns its place by covering a distinct query workflow: register lookups, licence checks, payment eligibility, comparison, search, place details, rankings, hub navigation, and dataset stats.
The surface covers the core domain thoroughly: bulk regulator access, name-based licence checking, product usability, place details, rankings, search, and provenance stats. Minor gaps remain, such as no explicit state-level ranking endpoint and limited ability to list every place in a category beyond the top-25 published ranking, but there are no obvious dead ends.