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Glama

ko-mcp

by io.github.SharpLu

get_fed_rates

Get daily U.S. policy and money-market interest rates as a markdown table: Effective Fed Funds Rate, SOFR, Prime Rate, and benchmark Treasury yields (3M, 2Y, 10Y, 30Y) per date, newest first. Use for monetary-policy questions like 'Where is the Fed funds rate now?' or 'How has SOFR moved this quarter?', or to compare policy rates against long-end yields for inversion analysis. Covers up to 10 years of daily history. For the full Treasury curve across all maturities, use get_treasury_yields instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days of history (default 30)

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the output format (markdown table), data coverage (up to 10 years daily history, newest first), and that it returns multiple rate series. It does not mention update frequency or data source, but for a read-only tool this is acceptable. Slightly more detail on behavior (e.g., intraday vs end-of-day) would justify a 5.

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?

The description is concise—three sentences that front-load the purpose and immediately follow with usage guidance and alternatives. Every sentence adds value without repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter and no output schema, the description fully explains what data is returned (list of specific rates), format (markdown table), ordering (newest first), and time range (up to 10 years). It also includes example questions. Nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a description for the single parameter 'days' (default 30, min 1, max 3650). The description does not add extra meaning beyond the schema, so baseline 3 is appropriate.

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 identifies the tool as returning a markdown table of U.S. policy and money-market interest rates (Fed Funds, SOFR, Prime, Treasury yields). It specifies the verb 'Get', the resource 'daily rates', and distinguishes from the sibling 'get_treasury_yields' which covers the full Treasury curve.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance (monetary-policy questions, inversion analysis) and when-not-to-use (for full Treasury curve, use get_treasury_yields instead). This directly helps an agent decide between sibling tools.

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
Disambiguation5/5

Each tool targets a distinct resource and action, and descriptions explicitly disambiguate similar pairs (e.g., get_congress_member vs get_congress_trades, get_crypto_holder vs get_crypto_holders). No two tools appear to do the same thing.

Naming Consistency4/5

The dominant pattern is get_<noun>, with list_<noun> for enumerations. Minor deviations exist: a bare 'search' tool and the 'sec_' prefix on SEC filing tools break the uniform verb_noun style, but the pattern remains predictable.

Tool Count4/5

At 24 tools, this is on the heavier side, but the server spans multiple financial data domains (SEC filings, stocks, insider trading, crypto, rates, economics), so each tool has a clear purpose. It is just below the 'too many' threshold.

Completeness5/5

The tool surface is remarkably complete for financial data retrieval: SEC filing lifecycle is covered (list -> index -> document), institutional ownership is available from both stock and institution perspectives, and insider/congress/crypto/economic data are all present. No obvious gaps or dead ends.

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