get_seriesFull metadata for one series: indicator, unit, frequency, geography, coverage, its VERIFICATION STATUS (passing/stale/failing/unverified from golden-value + freshness checks against the live source), recent check records, and the citation to the official table.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Source slug, e.g. 'fred' | |
| seriesId | Yes | Source-native series id, e.g. 'UNRATE' or 'v41690973' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description adds meaningful context by explaining the verification status comes from 'golden-value + freshness checks against the live source' and that recent check records are included. This goes beyond the annotation to inform the agent of the tool's internal behavior without contradicting it.
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 a single, informative sentence that uses a colon and list to convey a wealth of detail without being redundant. Every item adds value, but it is slightly dense; a more structured presentation could improve scanability.
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 metadata-only tool with two well-described parameters and no output schema, the description thoroughly enumerates the return contents, covering the core aspects (metadata fields, verification status, check records, citation). It does not discuss edge cases or error handling, but these are not essential for this simple retrieval operation.
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 input schema already provides complete descriptions for both parameters (source and seriesId) with examples, achieving 100% schema coverage. The tool description does not add additional parameter semantics beyond what the schema already covers, so the baseline score 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 'Full metadata for one series' and enumerates specific fields (indicator, unit, frequency, geography, coverage, verification status, check records, citation). This distinguishes it from sibling tools like get_observations (data) and get_series_stats (statistics), establishing a specific verb+resource+scope.
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 use when you need comprehensive metadata and verification details for a single series, but it does not explicitly mention alternatives or scenarios where another tool should be used. There is no exclusion or comparison to sibling tools, though the phrase 'Full metadata' hints at the intended use.
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 operation: Q&A, deep analysis, series metadata, observations, stats, catalog search, and monitor CRUD. The only potential overlap between answer and deep_analysis is explicitly disambiguated by use-case guidance, and delete_monitor is unambiguous by name even without a description.
Most tools follow a consistent verb_noun pattern (get_*, list_*, create_*, delete_*, search_*). However, 'answer' and 'deep_analysis' deviate from this pattern, making the naming convention slightly inconsistent.
With 11 tools, the server is well-scoped for a world data/statistics domain. It covers discovery, retrieval, analysis, and monitoring without unnecessary bloat, and each tool earns its place in the set.
The toolset provides a complete workflow: discover sources/datasets via list_sources/list_datasets/search_catalog, retrieve series/observations/stats via get_*, analyze via answer/deep_analysis, and manage monitors via create/list/delete_monitor. No critical operations are missing for the stated purpose.