fred_series
FRED economic data series.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| series_id | No |
tollbooth
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
- Removed
- Added
TDQS
C2.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations and description provides no behavioral context beyond data source. Agent doesn't know if it's read-only, what operations it performs, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single phrase is brief but sacrifices necessary detail. It's front-loaded but incomplete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers only data source, lacking return format, parameter usage, and sibling differentiation. Agent likely cannot use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description provides no explanation of parameters limit and series_id, even though schema coverage is 0%. Agent has no context on how to use them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description states it deals with FRED economic data series but lacks a specific verb or resource scope. It doesn't distinguish from sibling tools like economic_indicators or fred_surprises.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No information on when to use this tool versus siblings like economic_indicators or fred_surprises.
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
C2.2/5.0
Disambiguation3/5
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming Consistency2/5
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
Tool Count1/5
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Completeness2/5
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.