dataset_provenanceThe source, the date it was computed, the licence and the citation for the Eposvo 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 provided, the description carries the full burden of behavioral disclosure. It does state the kind of information returned, implying a read-only metadata operation, but it does not explicitly confirm no side effects, nor does it describe any access or formatting constraints. This is acceptable but not deeply transparent.
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 compact and front-loaded: it immediately lists the key provenance fields and then gives a practical instruction on when to read it. Every sentence contributes meaning, with no 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?
For a simple, parameterless provenance tool, the description covers the essential information: source, computed date, licence, and citation, plus the intended use case. It does not describe the output format, but given the simplicity and no output schema, the description is sufficiently complete for an agent to call it 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 has zero parameters, so parameter semantics are not a concern. The description correctly focuses on what data the tool returns rather than inputs, and the empty schema needs no further elaboration.
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 identifies the tool's purpose: returning provenance information for the Eposvo dataset, including source, computed date, licence, and citation. It is distinct from sibling tools like dataset_rows or dataset_stats, which deal with data content rather than metadata and attribution.
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 clear use case: 'Read this to attribute a figure correctly.' This tells the agent when to call the tool. It does not explicitly exclude alternatives or mention sibling tools, but the context is strong enough for a tool with no parameters and a focused provenance purpose.
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.
Most tools have clearly distinct purposes: schema, provenance, exact lookup, substring search, list comparison, summary stats, and top/bottom rows. There is mild overlap between dataset_compare and dataset_row since both return exact matches, but their use cases are described distinctly enough.
All tool names follow a consistent dataset_<operation> pattern with lowercase snake_case. While the suffixes mix nouns and verbs, the pattern is uniform and predictable across the entire set.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct common query pattern without unnecessary bloat or duplication.
The tools provide complete read-only coverage for exploring and reporting on a dataset: schema discovery, provenance, exact lookup, substring search, multi-value comparisons, numeric summaries, and top/bottom ranking. No major query pattern needed for this domain is missing.