dataset_provenanceThe source, the date it was computed, the licence and the citation for the RollCallWorks 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 disclosing behavior. It states the returned provenance fields and implies a read-only operation with no side effects, which is adequate for this simple tool.
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 wasted words. The key purpose is front-loaded, and the usage guidance is immediately actionable.
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, read-only provenance lookup, the description covers the returned content and the intended use case. Nothing essential is missing given the tool's simplicity.
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?
There are no parameters, so the baseline applies. The description adds meaningful context about what the tool returns, even though there is no parameter schema to elaborate on.
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 resource (RollCallWorks dataset provenance) and states the exact content: source, computation date, licence, and citation. It distinguishes itself from sibling dataset tools by focusing on attribution metadata.
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 second sentence, 'Read this to attribute a figure correctly,' gives a clear when-to-use instruction. It does not explicitly contrast with sibling tools, but the use case is unambiguous enough for an agent.
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 lookup, fuzzy search, comparison, stats, top-N), but dataset_row, dataset_search, and dataset_compare all return matching rows and could be confused without careful reading of their filter semantics.
All tools share the dataset_ prefix and use clear lowercase snake_case names. The second part is sometimes a noun (columns, provenance, row) and sometimes a verb/search-style word, but the pattern is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct query mode without bloat or redundancy.
The set covers schema discovery, provenance/citation, exact value lookup, substring search, comparisons, numeric statistics, and top/lowest ranking. For a read-only dataset MCP server this is a complete lifecycle with no obvious dead ends.