dataset_provenanceThe source, the date it was computed, the licence and the citation for the HardFM 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 fully discloses behavior by listing the exact data fields returned (source, date, licence, citation). It implies a read-only informational retrieval with no side effects, which is accurate and transparent.
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 redundant words. It front-loads the core purpose and immediately follows with the practical usage instruction, making it efficient and well-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 tool with no parameters and no output schema, the description provides all necessary context: what information is available (source, date, licence, citation) and when to use it (for attribution). Nothing more is needed.
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 no parameters, and the description correctly reflects this by not mentioning any inputs. Schema coverage is 100% (empty), so there is nothing missing or ambiguous.
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 purpose: to provide source, computation date, licence, and citation for the HardFM dataset. The verb 'attribute' and the phrase 'where this data comes from' leave no ambiguity about the tool's function.
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 explicit when-to-use guidance: 'Read this to attribute a figure correctly.' This tells the agent exactly when to invoke this tool without needing to infer from context.
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 lookup, substring search, value comparison, stats, and top/bottom—so the surface is easy to navigate. The only minor ambiguity is between dataset_row, dataset_search, and dataset_compare, all of which retrieve rows but with different matching semantics.
All tools follow a consistent dataset_ prefix with lowercase snake_case names. The naming is predictable and immediately signals the domain, making it easy for an agent to infer the purpose of any tool.
Seven tools is a well-scoped count for a single-dataset read-only MCP server. Each tool covers a meaningful query mode without unnecessary duplication or bloat.
The toolset covers the main dataset exploration needs: schema discovery, provenance, exact and substring search, multi-value comparison, numeric summaries, and extreme rows. A few advanced workflows—such as arbitrary filtering, grouping, or custom aggregations—are not directly supported, but the provided tools cover most common questions.