dataset_provenanceThe source, the date it was computed, the licence and the citation for the TermsBird 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 the exact information returned (source, computed date, licence, citation) and implies a read-only, informational operation. It does not specify output format, but that is a minor gap for a zero-parameter metadata 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?
Two tight sentences: the first lists the content, the second states the use case. There is no redundant wording 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?
Given zero parameters and no output schema, the description is complete. It identifies what is returned and why an agent would invoke this tool. Nothing else is needed for correct invocation.
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 zero parameters, so the baseline is 4. The description correctly avoids parameter details since none exist; there is nothing more it needs to communicate.
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 states what the tool provides: source, date computed, licence, and citation for the TermsBird dataset. It also names the intended use ('attribute a figure correctly'). This differentiates it from sibling tools like dataset_columns or dataset_stats, which handle different data operations.
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 explicit usage context: 'Read this to attribute a figure correctly.' It does not name alternatives or exclusions, but the sibling tool names are semantically distinct enough that an agent can infer when this tool is relevant versus data manipulation tools.
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 distinct purpose: schema discovery, filtering, provenance, exact lookup, substring search, statistics, and top/bottom sorting. No two tools overlap in function, and the descriptions reinforce the boundaries.
All tools follow the consistent dataset_<verb> pattern with no deviations. The naming convention is uniform and predictable.
7 tools is well-scoped for a dataset querying server. Each tool covers a necessary operation without redundancy or bloat.
The tool set covers schema, row retrieval, search, statistics, sorting, comparison, and provenance. The only minor gap is a straightforward 'get all rows' operation, but the existing tools can likely cover most workflows.