dataset_provenanceThe source, the date it was computed, the licence and the citation for the RunbookDesk 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 full burden. It discloses what the tool returns (source, date, licence, citation) and implies a read-only metadata lookup, but it does not explicitly state that there are no side effects or describe the output structure.
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 short sentences, each earning its place: the first enumerates the returned metadata, and the second states the practical purpose. No filler or 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 parameterless provenance lookup, the description is complete: it identifies the dataset, the metadata fields returned, and the correct usage context. No prerequisites, side effects, or alternatives are needed.
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 takes zero parameters, so there is nothing for the description to explain beyond what the empty schema already shows. The baseline of 4 applies here.
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 specific resource (RunbookDesk dataset) and the exact contents returned: source, computation date, licence, and citation. This clearly distinguishes it from sibling tools like dataset_columns, dataset_search, or dataset_stats, which handle data exploration 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?
'Read this to attribute a figure correctly' provides a clear, actionable use case. It does not explicitly contrast this tool with its siblings or state when not to use it, but the intended context is evident.
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 operation: schema, provenance, exact row lookup, fuzzy search, value-set comparison, numeric stats, and top/bottom ranking. Although row and search both retrieve rows, their matching semantics are clearly separated (exact equality vs. cell containment).
All tools share the dataset_ prefix followed by a clear noun or verb indicating the operation, such as columns, row, search, stats, and top. This creates a predictable and uniform naming convention.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct query modality without unnecessary redundancy.
The set covers schema discovery, provenance, exact and fuzzy row retrieval, value-based comparison, numeric statistics, and ranking. This is a complete surface for exploring and reporting on a tabular dataset.