weather_forecastDaily forecast with travel-impact score (flight risk, outdoor event suitability) per day plus an overall precipitation outlook (dry/mixed/wet pattern).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | No | ||
| lon | No | ||
| days | No | ||
| units | No | imperial | |
| zip_code | No | US ZIP (preferred). Or pass lat+lon. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish readOnly, idempotent, and non-destructive behavior. The description adds real behavioral context: this tool returns derived travel-impact and precipitation-pattern judgments, not just raw weather values. It does not detail the scoring methodology, but that is not necessary for invocation.
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?
A single, front-loaded sentence that packs the resource, key outputs, and travel use case without filler. Every phrase 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?
The description captures the output essence, and the schema covers days ranges and unit enums, but with no output schema and no required parameters, an agent still needs to infer that either zip_code or lat/lon is necessary and how the forecast horizon maps to days. It is minimally viable but not fully 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?
With only 20% schema description coverage, the description should compensate for undocumented lat, lon, days, and units, but it contains no parameter-level guidance. 'per day' weakly echoes the days parameter, but the critical location (zip_code vs lat/lon) and units semantics are left to the schema.
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 a specific deliverable ('Daily forecast') and unique output components ('travel-impact score ... plus overall precipitation outlook'), which sets it apart from sibling weather tools like weather_current, weather_hourly, and weather_historical. The flight-risk/outdoor-event details make the resource unmistakable.
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 daily-forecast and travel-impact language implies the tool is for multi-day planning, but it never explicitly says when to choose it over weather_route, weather_best_window, or weather_current, nor does it state exclusions. Usage context is present but only by implication.
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.