dataset_provenanceThe source, the date it was computed, the licence and the citation for the Medcontra 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 provided, the description carries the behavioral transparency burden. It clearly indicates this is a read-only metadata lookup and reveals what information will be returned. It does not describe the exact return format, but for a static provenance tool that is a minor gap.
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?
A single sentence that front-loads the provenance content and then gives a practical use case. There is no repetition of the title and no wasted words.
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 metadata-retrieval tool, the description fully covers what an agent needs: what information is available and why to call the tool. The absence of an output schema is mitigated by the explicit list of fields.
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 and an empty input schema, so there is no parameter-level meaning for the description to add. The baseline of 4 for parameterless tools applies.
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 a specific resource (the Medcontra dataset) and enumerates the exact provenance fields: source, computed date, licence, and citation. This makes it clearly distinct from sibling tools that operate on columns, rows, stats, search, or comparisons.
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?
It explicitly states when to use the tool: to attribute a figure correctly. It does not name alternatives or exclusion conditions, but the purpose is so distinct from the sibling data tools that an agent can reliably infer when it applies.
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 query pattern: schema, provenance, exact match, substring search, multi-value comparison, aggregations, and sorted top rows. While dataset_row and dataset_compare both do exact column matching, their different purposes (single value vs ordered X vs Y comparisons) are clearly described.
All tools share a consistent dataset_ prefix followed by lowercase snake_case names, making the family recognizable. There is a minor mix of noun-like names (columns, row, stats, provenance) and verb-like names (compare, search, top), but the pattern is still predictable and readable.
Seven tools is well within the ideal range and each tool provides a distinct operation for exploring a dataset. The count feels neither bloated nor thin for the server's stated purpose.
The tool surface covers schema discovery, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom rows—covering the main ways one would interrogate a dataset. A minor gap is the lack of a general paginated 'all rows' or arbitrary filtering tool, but the provided tools handle most realistic queries.