dataset_provenanceThe source, the date it was computed, the licence and the citation for the Orgplanly 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 signals a read-only informational operation via 'Read this' and discloses the provenance fields the agent will encounter. It doesn't describe output formatting, but for a zero-parameter metadata lookup this is largely sufficient.
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 tight sentences front-load the substantive fields and close with the intended use case. Every phrase earns its place, with no redundancy or 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 simple metadata tool with no parameters, the description supplies the provenance fields, the resource, and the correct usage context. It is complete enough for an agent to invoke and understand the result without an output schema.
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 no parameters, so the no-params baseline applies. There is no parameter meaning for the description to add, and the described content matches the empty-schema design.
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 (Orgplanly dataset) and enumerates the specific contents returned (source, computed date, licence, citation). It is clearly distinct from sibling tools like dataset_search or dataset_stats, and the action 'Read this' adds operational clarity.
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 gives an explicit use case: read this to attribute a figure correctly. It doesn't name alternative tools or exclusions, but for a provenance-specific metadata lookup the when-to-use guidance is clear enough.
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 maps to a distinct query mode—schema, provenance, exact match, substring search, ordered multi-value comparison, numeric stats, and top/bottom ranking—so an agent can generally choose based on question type. The only mild overlap is between dataset_row and dataset_compare for single-value lookups, but the wording clarifies exact equality versus ordered multi-value matching.
All seven tools share the dataset_ prefix and consistent snake_case, making the family instantly recognizable. The suffix varies between noun-like and verb-like forms, but the pattern remains predictable and readable across the whole set.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a necessary operation without redundancy, and none feel superfluous or missing.
The toolset covers the full range of common data exploration needs for the Orgplanly dataset: schema discovery, provenance attribution, exact lookups, substring search, comparisons, numeric summaries, and ranked extremes. For a read-only dataset server, there are no obvious dead ends or significant gaps.