dataset_provenanceThe source, the date it was computed, the licence and the citation for the Runwayze 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?
There are no annotations, so the description carries the full burden of behavioral disclosure. It discloses the type of information returned (source, date, licence, citation), but does not explicitly state that the tool has no side effects or that it is read-only. Given the simple informational nature, the description is adequate but not exhaustive. It does not contradict anything, but could be more explicit about it being a safe read operation.
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 two sentences with no filler. The first sentence lists the returned details, and the second states the use case. It is front-loaded with the core information and efficiently structured.
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 tool with no output schema, this description is complete. It tells the agent what information will be returned and why to use it. There are no missing prerequisites or edge cases given the simplicity of the tool.
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 does not need to explain any parameters, and it adds no parameter-related meaning because there are none. This is appropriate given the empty schema.
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 the tool's function: it returns source, date, licence, and citation for the Runwayze dataset. The verb 'Read this' and the resource 'Runwayze dataset' are specific. This distinguishes it from siblings like dataset_columns or dataset_stats, which deal with other aspects of the data.
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 it: 'Read this to attribute a figure correctly.' This provides clear context for invocation. However, it does not explicitly mention alternatives or when not to use it, though the purpose is clear enough relative to siblings.
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 distinct role: schema, provenance, exact match, substring search, multi-value comparison, numeric stats, and top-N. There is some overlap between the row retrieval tools, but the descriptions clarify the matching semantics enough to avoid serious confusion.
All tools share a consistent dataset_ prefix and are concise, but they mix noun-style names (dataset_columns, dataset_provenance, dataset_row, dataset_stats) with verb/adjective-style names (dataset_compare, dataset_search, dataset_top). The pattern is still predictable and readable.
Seven tools is well-scoped for a read-only dataset query server. Each tool covers a distinct query mode or metadata need, so none feel redundant or excessive.
The tool set covers schema discovery, provenance, exact lookup, fuzzy search, multi-value comparison, numeric aggregations, and top/bottom ranking. A minor gap is the lack of a generic sample/all-rows or distinct-value exploration tool, but the core question-answering workflows are well supported.