dataset_provenanceThe source, the date it was computed, the licence and the citation for the Coshhvo 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 the informational contents and implies a read-only operation, but it does not explicitly state that it is read-only or mention any permissions or side effects. The disclosed content helps, but behavioral transparency is not complete.
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 concise sentences, front-loads the core content, and adds a practical usage note. There is no redundant or vague wording.
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 metadata tool, the description is complete: it names the dataset, enumerates the returned provenance fields, and indicates the intended use case. No output schema exists, but the description adequately describes the return contents.
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, so the description cannot meaningfully add parameter-level semantics. The schema already covers this case fully, and the description appropriately focuses on what the tool returns rather than parameters.
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 states exactly what the tool provides: source, computed date, licence, and citation for the Coshhvo dataset. It also links the tool to a concrete action — attributing a figure — making it easy to distinguish from the sibling data 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 clearly indicates when to use the tool: when you need to attribute a figure correctly. It does not explicitly name alternatives or exclusions, but the purpose is specific enough that an agent can infer when it applies versus sibling tools.
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 targets a distinct dataset operation: schema, provenance, exact lookup, multi-value comparison, substring search, numeric stats, and ranking. The only near-overlap is dataset_row and dataset_compare, but the multi-value/ordered behavior of compare makes its purpose clearly different.
All tools follow a consistent dataset_<noun> snake_case pattern. The convention makes the tool surface predictable and easy to navigate.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct querying need without the set feeling bloated or sparse.
The tool set covers the full lifecycle of exploring a read-only dataset: schema discovery, provenance, exact filtering, fuzzy search, comparison, statistics, and top/bottom ordering. There are no obvious dead ends for common dataset questions.