dataset_provenanceThe source, the date it was computed, the licence and the citation for the Clipboardo 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 transparency burden. It makes clear the tool returns provenance information and is read-oriented ('Read this'), but it does not explicitly state side-effect behavior or return formatting. For a simple zero-parameter metadata lookup, this is adequate though not exhaustive.
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 with no filler. The concrete content fields are front-loaded, and the practical instruction follows immediately. Every word earns its place.
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 provenance lookup with no output schema, the description names the key returned fields and the intended use case. Nothing essential is missing for an agent to correctly select and invoke this tool.
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 0 parameters, so the baseline is 4; the description does not need to explain parameter semantics. It instead adds value by describing the informational content the tool returns.
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?
Clearly identifies the tool as providing provenance metadata (source, computed date, licence, citation) for the Clipboardo dataset and ties it to the use case of attribution. This distinguishes it from the sibling dataset_* tools, which focus on columns, rows, search, stats, and comparison.
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 instruction 'Read this to attribute a figure correctly' gives a clear, actionable context for when to use the tool. It does not explicitly list exclusions or alternatives, but the use case is specific enough to guide 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 has a clearly scoped function: schema, provenance, exact lookup, substring search, numeric summaries, top/bottom, and multi-value comparison. Even where dataset_row and dataset_search overlap conceptually, their descriptions make the distinction obvious.
All tools share the consistent dataset_ prefix followed by a concise operation or noun. The naming is uniform, predictable, and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query type without redundancy or unnecessary bloat.
For a read-only dataset exploration server, the surface is complete: schema discovery, provenance, exact lookups, free-text search, statistics, top/bottom rows, and comparisons. There are no obvious dead ends or missing operations for its stated purpose.