FX Timeseries
fx_timeseriesDaily rate series for a single base→quote pair across a date range. Useful for charts and trend analysis.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | ISO end date. | |
| base | Yes | ||
| quote | Yes | ||
| start | Yes | ISO start date. |
fx_timeseriesDaily rate series for a single base→quote pair across a date range. Useful for charts and trend analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| end | Yes | ISO end date. | |
| base | Yes | ||
| quote | Yes | ||
| start | Yes | ISO start date. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, open-world, and non-destructive, so the safety profile is well covered. The description adds that it returns daily data over a range, but it does not disclose return shape, ordering, handling of missing days, or rate type. This is acceptable for a simple read tool but adds limited behavioral context 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?
Two short sentences with no wasted words. The first sentence carries the essential definition, and the second adds practical application context. Nothing is duplicated from the schema or annotations.
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 read-only tool with four required string parameters and no output schema, the description is workable but minimal. An agent can likely infer the intended call, but it lacks guidance on currency code formats and does little to disambiguate this tool from the larger fx sibling cluster.
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 schema documents start and end as ISO dates, covering 50% of parameters. The description reinforces that start and end form a date range and introduces the base→quote pair relationship, which is useful. However, base and quote lack format or code guidance in both schema and description, so the description only partially compensates for the coverage gap.
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 identifies the resource: a daily rate series for a single base→quote pair over a date range, and it states the use case ('charts and trend analysis'). It lacks an explicit imperative verb like 'retrieves' or 'returns,' and it does not name sibling tools, but the function is still unambiguous.
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 gives clear context for when this tool is appropriate: daily series, single currency pair, date range, and chart/trend use cases. However, it does not explicitly distinguish it from closely related fx siblings such as fx_rates, fx_historical, or fx_pair, nor does it state when not to use this tool.
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.