Skip to main content
Glama

Macro Snapshot

by io.github.pipeworx-io

Indicator

indicator
Read-onlyIdempotent

Read recent history for a single FRED (Federal Reserve economic data) series — drill into anything in the macro_snapshot or any other FRED series id. Common ids: UNRATE (unemployment), DFF (Fed funds), DGS10/DGS2/DGS3MO (Treasury yields), CPIAUCSL (CPI index), CPILFESL (core CPI index), PAYEMS (nonfarm payrolls), VIXCLS (VIX), SP500, MORTGAGE30US (30y mortgage rate), WALCL (Fed balance sheet), DTWEXBGS (broad USD index), T10Y2Y/T10Y3M (curve spreads). Returns observations most-recent-first plus the latest value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many recent observations to return (default 12, max 60).
series_idYesAny FRED series id, e.g. "UNRATE", "DGS10", "MORTGAGE30US", "WALCL".

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that results are 'observations most-recent-first plus the latest value', and mentions default/max limit. No contradictions with annotations.

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?

Two concise sentences, front-loaded with purpose and usage, no fluff. Every sentence 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 the simplicity of the tool (read-only, two parameters) and rich annotations, the description is sufficient. It covers the return format and common use cases, though it could mention that the output is an array of observations.

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?

Schema coverage is 100%, so the baseline is 3. The description enriches parameters by listing common series IDs and stating default and maximum limit values, adding meaning beyond the schema.

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 specifies the verb 'Read' and the resource 'FRED series', with examples of common IDs. It distinguishes from sibling tools like 'macro_snapshot' by explicitly stating it can drill into any FRED series.

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 implicitly guides when to use this tool (to get recent history for any specific FRED series) and contrasts with 'macro_snapshot'. It provides many common IDs as usage context, but no explicit when-not or alternative naming.

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

All Macro Snapshot toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and ai_visibility_check duplicates scan_competitor_ai_presence at a smaller scale. The detailed descriptions do separate most of these by routing, mode, or output, but the currently identical beta router and the broad ask/research family create real ambiguity.

Naming Consistency3/5

Names are all lowercase snake_case and there are coherent prefixes like polymarket_ and pipeworx_, but the set mixes imperative verb_noun names (list_subscriptions, validate_claim) with descriptive noun phrases (macro_snapshot, entity_profile, polymarket_edge_tracker) and bare verbs. The inconsistency is readable but not a single predictable pattern.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent server, and the set spans many unrelated domains: data routing, prediction markets, memory, subscriptions, AI visibility, package scanning, and llms.txt generation. Even if each cluster has a purpose, the server is overloaded and several high-level wrappers could be consolidated.

Completeness4/5

The main clusters are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, and company research has resolve_entity, entity_profile, recent_changes, and compare_entities. Minor gaps exist (no direct trade placement, no general web search, no account/profile management), but agents can complete most workflows without dead ends.

Resources