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Glama

Data Freshness

get_data_freshness
Read-only

Live per-source update status straight from the automated updater's ledger: last successful update, data frontier, and honest stale/failure flags (dates are NEVER fabricated — a silent upstream outage shows here as stale, not papered over). Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoOne source id; omit for the full board

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds value beyond the readOnlyHint annotation by explicitly stating that dates are never fabricated and that silent upstream outages show as stale. This provides honest behavioral insight into data quality and failure handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the core concept ('Live per-source update status'), and wastes no words. Every phrase adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description adequately summarizes what the tool returns (last successful update, data frontier, stale/failure flags). It provides behavioral context and usage guidance, though a more detailed enumeration of return fields would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes the parameter as 'One source id; omit for the full board' (100% coverage). The tool description repeats this in slightly different words ('per-source... omit for the full board'), adding no new semantic meaning beyond a casual tone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides 'live per-source update status' with specific outputs: 'last successful update, data frontier, and honest stale/failure flags'. This verb+resource combination is distinct from all sibling tools, which focus on auth, economics, or family status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies usage with 'per-source' and 'omit for the full board', giving clear context. It does not explicitly state when not to use or list alternatives, but the sibling tools are sufficiently different that no confusion arises.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource/action: auth status, freshness, econ series, metadata, family status, HF/IP download links, dictionaries, source listings, and search. No overlapping functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: get_* for retrieving specific items, list_* for enumerations, search_* for queries. Naming is uniform and predictable.

Tool Count5/5

11 tools is well-scoped for a data library server covering multiple domains (econ, HF, IP) with retrieval, metadata, listings, and status checks. No redundancy or excessive granularity.

Completeness5/5

The toolset covers the full lifecycle for a read-only data service: status/auth, search, retrieval (via download links), metadata, variable dictionaries, and source listings. No obvious gaps for the stated purpose.