dataset_provenanceThe source, the date it was computed, the licence and the citation for the Fax Line Index 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 full burden. It discloses exactly what information the tool returns (source, date, licence, citation) and implies a read-only nature via 'Read this.' It doesn't mention side effects, but for a static provenance lookup, this is sufficient and does not contradict any structured data.
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 sentences with no redundancy. The key content (what it provides) is front-loaded, followed by a clear usage instruction. Every word earns its place.
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 simple, parameterless tool with no output schema, the description fully explains what will be returned and when to use it. Nothing an agent needs to call it correctly is missing.
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 per the rubric. The description adds context about the purpose, but there are no parameter details to explain, so the baseline holds without penalty.
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 provides provenance details (source, computed date, licence, citation) for the Fax Line Index dataset. It uses a specific resource and distinguishes from sibling data-access tools by focusing on metadata rather than 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?
It gives an explicit use case ('Read this to attribute a figure correctly'), making clear when an agent should invoke it. However, it doesn't name alternatives or state when not to use it, though no direct sibling appears to offer the same provenance functionality.
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, provenance, search, exact match, stats, top, compare, describe, fields, and submit. No overlap or ambiguity between dataset exploration and enquiry submission tools.
All tools follow a consistent snake_case pattern with clear prefixes (dataset_ and enquiry_) and descriptive suffixes (columns, search, stats, submit). Naming is uniform and predictable.
10 tools is well-scoped for a server that handles dataset exploration (7 tools) and an enquiry workflow (3 tools). Each tool earns its place and covers necessary operations without redundancy.
The dataset tools cover schema, provenance, search, exact match, statistics, top values, and comparison, while the enquiry tools cover description, field schema, and submission with confirmation. No obvious gaps for the stated purpose.