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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".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "series_id": "UNRATE"
+  },
+  {
+    "limit": 24,
+    "series_id": "DGS10"
+  }
+]
  2. First observed

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.

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TDQS

A3.9/5.0
Disambiguation2/5

Several tools form near-overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route questions, and bet_research/polymarket_edges/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread all target prediction-market edges. ask_pipeworx_beta is explicitly identical to ask_pipeworx today, so an agent must read long descriptions to pick correctly. Most other tools are distinct, but the ambiguous clusters pull the score down.

Naming Consistency3/5

All names use snake_case and are readable, but conventions mix: many are verb_noun (ask_pipeworx, compare_entities, discover_tools, subscribe), several are noun phrases (macro_snapshot, indicator, entity_profile, polymarket_arbitrage), and a few are adjective_noun (recent_alerts, deep_research). The near-duplicate ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded suffixes form the only consistent family, but overall the naming pattern is not uniform.

Tool Count2/5

33 tools is well beyond the 15-tool well-scoped range and even past the 25-tool 'too many' threshold. The server tries to be a data router, prediction-market desk, AI visibility checker, memory store, and subscription manager all at once, and includes an intentional duplicate (ask_pipeworx_beta). Several tools (remember/recall/forget, subscribe/unsubscribe/list_subscriptions/recent_alerts) could be their own server.

Completeness4/5

The surface covers question answering, deep research, entity resolution/profile/comparison, macro indicators, prediction-market analytics, subscriptions, memory, and feedback—no obvious dead ends for the main workflows. Minor gaps exist (e.g., no direct generic web search, no update for saved memory other than overwrite, and some niche additions like generate_llms_txt feel out of place), but the core data and research lifecycle is well covered.

Resources