dataset_provenanceThe source, the date it was computed, the licence and the citation for the DailyLogDesk 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, the description carries the burden of conveying behavior. 'Read this' implies a read-only, non-destructive operation, and the listed content (source, date, licence, citation) describes what the agent can expect. It does not discuss edge cases or return formatting, but for a simple metadata endpoint this is adequate.
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 deliver the essential content and the usage trigger. No filler or redundant schema repetition; the title is slightly redundant but the description itself is tightly structured and front-loaded.
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, no-output-schema provenance tool, the description is complete: it names every piece of information returned (source, computed date, licence, citation) and states when to read it. There are no hidden inputs or complex return expectations to document.
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 there is no parameter documentation burden. The description does not need to explain any input semantics; the baseline of 4 applies because there is nothing to document.
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 identifies the tool's resource (DailyLogDesk dataset) and the specific information it provides: source, computed date, licence, and citation. The imperative 'Read this to attribute a figure correctly' gives a concrete purpose, and the scope is obviously distinct from the sibling data-access tools.
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 explicitly tells the agent when to use this tool: when attribution or citation for a figure is needed. It does not enumerate alternatives or exclusions, but the usage context is clear enough for a zero-parameter provenance tool.
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 stated query mode, but several return rows from the same dataset with overlapping semantics (exact match, substring search, multi-value compare, top-N). An agent could sometimes confuse dataset_row and dataset_search, though the descriptions provide enough detail to disambiguate.
All tools follow a consistent dataset_ prefix pattern with short, descriptive operation names. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct access pattern without unnecessary duplication or bloat.
The toolset covers schema discovery, provenance attribution, exact row lookup, substring search, value comparison, numeric statistics, and top/bottom ranking. For a read-only dataset querying server, this is a complete surface with no obvious dead ends.