Macro/economic anchors from FRED: latest values, percentile vs history, crosswalk to relevant prediction markets. For grounding macro theses.
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | FRED series ID (e.g. CPIAUCSL) | |
| category | No | Category (rates, inflation, employment, growth) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the data source (FRED), the types of outputs (latest values, percentiles, crosswalk), and the purpose. It does not explicitly state whether it is read-only, mention rate limits, or describe return format, but as a 'get' tool, the read-only nature is implied. This is adequate but not rich.
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?
The description is a single, well-structured sentence that front-loads the main function (macro/economic anchors from FRED) and lists key outputs. Every word earns its place; no fluff or redundancy.
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?
The tool is simple with two fully described parameters and an output description that covers the main return elements. The description mentions the intended use case and data source, making it complete enough for an agent to understand and invoke. It does not describe return format, but with no output schema, the description gives a reasonable overview.
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 input schema covers 100% of parameters, each with a clear description (FRED series ID and category). The tool description adds no additional parameter-specific meaning beyond the schema. Following the baseline for full schema coverage, a 3 is appropriate.
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 uses a specific verb ('get') with a clear resource ('Macro/economic anchors from FRED') and enumerates what it provides: latest values, percentile vs history, and crosswalk to prediction markets. This distinguishes it from sibling tools like query_econ or get_forecast by naming FRED and its unique output.
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 phrase 'For grounding macro theses' provides a clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it. Given the context is clear but exclusions are absent, this aligns with a 4.
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.
Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.
Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.
108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.
The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.
