Skip to main content
Glama
rabidlego25

macro-mcp

by rabidlego25

fetch_data

Fetch macro statistics, FX rates, or company data by specifying provider, flow, and dimension key. Get observations with labels, units, and period coverage.

Instructions

Fetch observations. key maps dimension ids to codes, e.g. {"FREQ": "M", "CURRENCY": "USD"}.

A dimension takes several codes joined by +, so {"REF_AREA": "JP+XM"} asks
for both in one request rather than two. A dimension left out of the key
matches every code, which is how to ask which countries a flow covers:
fetch it with only FREQ pinned and read the series keys.

Returns the invariant part of the key once under "key", and observations as
[period, value] pairs grouped into "series" by whichever dimensions vary.
"names" gives the label for every code in the response, so a series keyed
XDC or CP01 can be read without asking search_codes again.
"units" says what the numbers are measured in, including any multiplier:
read it before comparing or converting. "range" is the period span actually
returned; periods with no value are omitted and counted under "empty".
"total" counts what the provider sent, which is the whole series unless
"truncated" says it is a floor: only the newest observations are fetched,
so a longer history exists and was not counted. Narrow start/end to read
it.

An empty response echoes the key it was given and says so under "note".
Any single entry in the key can empty a result and they all look alike, so
drop a dimension to find which one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
keyYes
flowYes
limitNo
startNo
providerYes
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so thoroughly. It explains return structure ('key', 'series', 'names', 'units', 'range'), missing data handling ('empty'), truncation semantics ('total' vs 'truncated'), and empty-response behavior. This far exceeds what would be expected for a data-fetch operation.

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 dense but every sentence earns its place. It opens with 'Fetch observations,' then systematically walks through request semantics, response structure, edge cases, and debugging guidance. No filler or repetition of trivial details.

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

Completeness5/5

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

Given the absence of an output schema, the description remarkably explains the full response shape, including 'key', 'series', 'names', 'units', 'range', 'empty', 'total', and 'truncated'. It also documents the empty-response behavior and a debugging technique. For a complex data tool with nested objects and no output schema, this is exceptionally complete.

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

Parameters4/5

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

The description compensates for 0% schema coverage by thoroughly explaining the 'key' parameter: how codes map to dimensions, how '+' joins codes, and how omitting a dimension matches all codes. 'start' and 'end' are also mentioned in the context of reading truncated histories. 'provider', 'flow', and 'limit' remain implied by their names, but the most complex parameter is well-covered.

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?

States a specific verb and resource: 'Fetch observations.' The description goes beyond the generic name by explaining what the returned observations contain and how they are organized, making it clearly distinct from sibling tools like search_codes, list_providers, or describe_flow. The detail about series, keys, and codes leaves no ambiguity about this tool's role.

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?

Provides clear context for when to use this tool, including how to ask which countries a flow covers by 'fetching with only FREQ pinned' and how to read labels 'without asking search_codes again.' It does not explicitly name all alternatives or state when not to use it, but the usage patterns and hints about narrowing start/end are strong contextual guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rabidlego25/macro-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server