Dataset Coverage Metadata
meta_datasetReturn public-safe coverage, limitations, and endpoint references for one dataset slug.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Dataset slug such as location-enrichment. |
meta_datasetReturn public-safe coverage, limitations, and endpoint references for one dataset slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Dataset slug such as location-enrichment. |
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 declare readOnlyHint, idempotentHint, and non-destructive behavior; the description adds concrete context beyond that by stating the returned information is 'public-safe' and includes coverage, limitations, and endpoint references. It also implicitly indicates that raw dataset records are not returned. No contradiction with 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 a single sentence that front-loads the action verb and packs in scope, safety, and return content with no filler. Every word earns its place.
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?
With only one required parameter, no output schema, and safety-related annotations already present, the description names the concrete return components (coverage, limitations, endpoint references) sufficiently for correct invocation. It could mention behavior for unknown slugs or how to enumerate valid slugs, but the low complexity makes the current description mostly 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?
Schema coverage is 100%, and the single 'slug' parameter already has a clear description with an example ('location-enrichment'). The description merely repeats 'dataset slug' without adding new semantic detail, 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 opens with a clear verb ('Return') and specifies the resource ('public-safe coverage, limitations, and endpoint references for one dataset slug'). 'One dataset slug' clearly signals a single-item metadata lookup and distinguishes it from catalog/batch siblings. It does not explicitly name a contrasting sibling, so it stops short of a 5.
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 phrase 'for one dataset slug' implies this tool is for retrieving metadata about a single known dataset. However, it provides no explicit when-not-to-use guidance and does not mention alternatives like meta_catalog for discovering slugs. Usage context is implied rather than explicitly stated.
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.
Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.
All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).
129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.
Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.