find_recoveringReturn datasets whose published freshness trend is recovering, with the fastest staleness reductions first. Includes pipeline-computed trend and publish-reliability evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum ranked recovering datasets to return; integer from 1 to 200, e.g. 50. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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?
Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about ordering ('fastest staleness reductions first') and included evidence ('pipeline-computed trend and publish-reliability evidence'), going beyond the annotations.
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, front-loaded with the core functionality and followed by a concise note on included evidence. There is no redundant or filler content.
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 tool with one optional parameter and an existing output schema, the description covers purpose, ordering, and result contents. It lacks explicit when-not-to-use guidance, but given the strong annotations and schema, it is sufficiently complete.
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 only parameter 'limit' is fully described in the schema with type, range, default, and example. The description does not add additional semantic meaning about the parameter, but the schema coverage is 100%, so the baseline of 3 is appropriate.
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 returns datasets with a recovering freshness trend, sorted by fastest staleness reductions. This is a specific verb-resource pair and distinguishes it from siblings like find_deteriorating or find_stale.
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 implies usage for identifying recovering trends but does not explicitly state when to prefer this over alternatives like find_deteriorating or find_stale. No exclusions or alternative tool references are provided.
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 workflow—search, detail retrieval, freshness checking, licence enumeration, and citation metadata. The only mild overlap is between get_dataset and get_provenance, both exposing metadata, but their descriptions differentiate full health/freshness detail from citation-ready provenance.
All tool names follow a clear verb_noun pattern in snake_case: find_, get_, and search_ prefixes are used consistently. Minor stylistic variation between find_by_licence and find_stale does not undermine predictability.
Five tools is well-scoped for a dataset catalog server, covering discovery, inspection, health assessment, licence scoping, and citation. No redundant or excessive tools are present.
The core lifecycle is covered: search to find datasets, get_dataset for full detail, find_stale for freshness risk, and get_provenance for citation. A minor gap is the lack of a general list-all or status filter beyond stale, but the domain is narrow enough to work around this.