dataset_provenanceThe source, the date it was computed, the licence and the citation for the EmployeeDeskHQ 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?
No annotations are provided, so the description carries the behavioral disclosure burden. 'Read this' signals a read-only lookup, and the listed fields describe what the agent will receive. It does not discuss auth or rate limits, but for a parameterless metadata endpoint this is a minor gap.
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 two short sentences with no filler. The identifying content (source, date, licence, citation) is front-loaded, and the usage instruction follows naturally.
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 parameterless metadata tool, this is complete: it says what the tool returns, why it exists, and when to use it. The lack of an output schema is compensated by the explicit list of fields the agent can expect.
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 has zero properties, so there are no parameters to document; the baseline for 0-parameter tools is 4. The description appropriately focuses on the output content rather than inventing parameter guidance.
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 identifies the exact resource (EmployeeDeskHQ dataset provenance) and the specific content it provides: source, computed date, licence, and citation. It also connects the tool to a clear goal ('attribute a figure correctly'). This distinguishes it cleanly from the sibling dataset tools, which focus on data inspection and manipulation.
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 explicitly states the intended use case: read this to attribute a figure correctly. It does not enumerate when not to use it or name alternatives, but the parameterless provenance scope and the sibling tool names make the appropriate selection obvious.
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 are clearly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping filtering behavior that could lead an agent to pick the wrong one. Schema, provenance, stats, and top are unambiguous.
All tools share the dataset_ prefix with snake_case, which is easy to follow. However, the suffixes mix nouns (columns, row, stats) and verbs (compare, search), so it is not a fully consistent verb_noun convention.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct mode of exploration without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, full-text search, comparison ordering, numeric stats, and top/bottom rows. A minor gap is the lack of a general paginated 'all rows' or arbitrary aggregation tool, but common dataset questions are well supported.