FRED rates surprise — DGS10 10Y + DGS2 2Y csv public-domain no-key, spread + inversion flag, delta scoring for FOMC Fed funds + rates markets. TokenBucket 2/s polite.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Days back, default 14 | |
| min_score | No | Min score |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so description carries full burden. It discloses output format (CSV), public domain, no key needed, and rate limit (TokenBucket 2/s polite). It also mentions spread and inversion flag computation. However, it doesn't explicitly state read-only nature or side effects, and the scoring mechanism is unclear.
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 short but cryptic, using abbreviations and fragmented phrasing. It is front-loaded with 'FRED rates surprise' but lacks sentence structure. While concise, it sacrifices clarity.
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?
Tool has 2 optional parameters and no output schema. Description does not fully explain the output beyond 'csv' and components. The surprise score meaning, delta scoring details, and data source (FRED API) are omitted. Given no output schema, the description should elaborate on return values.
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?
Schema coverage is 100% with descriptions for both parameters (days, min_score). Description does not add meaning beyond the schema; it mentions 'delta scoring' but not min_score. Baseline 3 is appropriate as the schema already documents parameters adequately.
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 lists components (DGS10, DGS2, spread, inversion flag, delta scoring) but lacks a clear verb stating the tool's action. It vaguely conveys a derived metric computation but doesn't explicitly say 'calculates' or 'computes'. It distinguishes from siblings like fred_series by implying a derived output, but the purpose remains somewhat ambiguous.
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?
No explicit guidance on when to use this tool versus alternatives. While it mentions 'public-domain no-key' and rate limiting, it doesn't specify contexts or exclusions. Siblings include fred_series and economic_indicators, but the description offers no decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.