dataset_provenanceThe source, the date it was computed, the licence and the citation for the Extinvo 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 burden of explaining behavior. It discloses the exact output content (source, computed date, licence, citation) and the intended use, making the read-only, metadata-only nature clear.
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?
Two sentences, each earning its place: the first states what the tool provides, and the second states when to read it. No redundant or vague filler.
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 zero-parameter tool with no output schema, the description sufficiently lists the returned provenance fields and the reason to call it. The agent can decide to invoke it and understand the result without additional information.
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 schema information to elaborate on. The description appropriately focuses on what the tool returns rather than parameters.
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 names the exact resource ('the Extinvo dataset') and the specific provenance attributes it returns (source, computed date, licence, citation). This clearly sets it apart from the sibling tools, which focus on data columns, search, stats, and rows rather than provenance.
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?
It gives a clear trigger for use: 'Read this to attribute a figure correctly.' It does not explicitly state when not to use it or name alternatives, but the use case is unambiguous.
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 tools are mostly distinct: columns, provenance, stats, and top each have a single clear role, while dataset_row, dataset_search, and dataset_compare all return rows but differ by exact match, substring containment, and ordered value comparison. The descriptions explain these differences clearly, so misselection is unlikely but still possible.
Every tool follows the same dataset_<operation> pattern with a clear noun or verb suffix. The naming is predictable and the row-returning tools use distinct names (row, search, compare) that match their behavior.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a meaningful operation and none feel redundant or superfluous.
The set provides schema, provenance, exact lookup, free-text search, compare, stats, and top/bottom ranking, which covers the main ways an agent would query this dataset. No obvious dead-end or missing core operation is apparent.