dataset_provenanceThe source, the date it was computed, the licence and the citation for the HostingByStack dataset. Read this to attribute a figure correctly.
| 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?
With no annotations, the description carries the burden of explaining behavior. It implies a read-only informational lookup by describing the provenance fields and the attribution purpose, but it does not explicitly state that the tool has no side effects or that it never modifies data.
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 brief and to the point, consisting of two short sentences. It contains no redundant wording and immediately conveys the tool's purpose and the data it returns.
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 sufficiently explains what information is returned (source, date, licence, citation) and why it matters (attribution). It does not describe the output format, but there is no output schema and the content focus is clear enough for an agent to use the tool correctly.
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 accepts zero parameters, which corresponds to the baseline score of 4 for parameter semantics. The description does not need to explain parameters because there are none.
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 that the tool provides provenance metadata — source, computed date, licence, and citation — for the HostingByStack dataset, and the title reinforces this purpose. It is easily distinguishable from sibling tools that handle columns, search, stats, or submissions.
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 'Read this to attribute a figure correctly' gives clear contextual guidance on when to use the tool. It does not explicitly name alternative tools or state when not to use it, but the intended use case is still clear 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.
The dataset_* tools are mostly distinct, but dataset_row and dataset_compare overlap: both retrieve rows by matching a column value, and compare can effectively do row's job with a single value. The enquiry_* tools are clearly separated by behavior, fields, and submission.
Tool names consistently use lowercase snake_case with clear domain prefixes: dataset_* and enquiry_*. However, some names are nouns (dataset_columns, dataset_row, dataset_stats) while others are verbs or adjectives (dataset_compare, dataset_search, dataset_top), so the pattern is not perfectly uniform.
Ten tools is well-scoped for a server covering two clear areas: read-only dataset exploration and enquiry submission. Each tool has a practical role, and the count is comfortably within the ideal range.
The dataset side covers schema, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom rows, which is strong for a read-only dataset server. The enquiry side covers describing the process, listing fields, and submitting with a two-step confirmation, though there is no way to check submission status or cancel an enquiry.