dataset_provenanceThe source, the date it was computed, the licence and the citation for the Rollupvo 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 specific information returned (source, date, license, citation) and implies a read-only operation. Since there are no side effects or prerequisites, this is sufficient for a simple 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?
The description is concise, with two short sentences that front-load the key information (what it provides) and then the use case. There is no waste or repetition.
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 tool with no parameters and no output schema, the description fully explains what it provides and why it should be used. Nothing essential is missing for an agent to call it correctly.
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 zero parameters, so the baseline is 4. The description does not need to explain parameters, and the schema is trivially covered.
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 that the tool provides the source, computation date, license, and citation for the Rollupvo dataset. This distinguishes it from sibling tools that handle data content (columns, rows, stats, etc.), and the verb 'Read this' implies a read/informational operation.
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 a clear use case: 'Read this to attribute a figure correctly.' It provides context for when to use the tool but does not explicitly mention alternatives or when not to use it. The purpose is distinct from siblings, so the context is clear without exclusions.
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.
Most tools have clearly distinct purposes: schema, provenance, stats, search, and top-N are easy to separate. Dataset_row and dataset_compare both filter rows by column values, though dataset_compare is specifically for ordered multi-value comparisons and dataset_row is exact single-value lookup.
All tools share the consistent dataset_ prefix and snake_case style, making the set feel predictable. The second part mixes nouns and verbs slightly (columns, compare, row, search, stats, top), but the overall pattern is still coherent.
Seven tools is well-scoped for a single dataset exploration server. Each tool covers a distinct query need without redundancy or excessive granularity.
The server covers schema discovery, provenance, exact lookup, substring search, comparisons, numeric stats, and top/bottom ordering. Minor gaps like distinct-value enumeration or arbitrary sampling exist, but the core read-only exploration surface is well covered.