Recent US Treasury securities auction results: term, CUSIP, issue/maturity dates, high yield, interest rate, bid-to-cover ratio, and amounts. Optionally filter by security type (Bill, Note, Bond, TIPS, FRN).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows to return. | |
| security_type | No | Filter by security type: 'Bill', 'Note', 'Bond', 'TIPS', 'FRN'. |
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?
Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds data scope and the filtering option, but does not disclose behavioral details such as the recency window behind 'Recent', default row count, or sort order. No contradiction 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste: the first names the resource and its return fields, the second covers the optional filter. The main purpose is front-loaded ahead of the filtering detail, making it highly scannable for an agent.
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?
For a simple tool with two optional parameters, full schema coverage, and strong annotations, the description is nearly complete. The listed fields compensate for the absence of an output schema. The only gap is the vague 'Recent' qualifier — no recency window is specified — and no mention of default limit behavior.
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 description coverage is 100%, so both parameters (limit and security_type) are already documented in the schema. The description adds the security type filter and its enum values (Bill, Note, Bond, TIPS, FRN), which slightly reinforces but does not meaningfully exceed what the schema already provides. Baseline 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 states a specific verb ('recent results') and a precise resource (US Treasury securities auction results), then enumerates the exact fields returned (term, CUSIP, dates, high yield, rate, bid-to-cover, amounts). This is easily distinguished from the treasury_* siblings like treasury_interest_rates, treasury_debt, and treasury_cash_balance, which cover different data domains.
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 description clearly implies when to use it — whenever auction results are needed — and states the optional filter by security type. However, it does not explicitly name alternatives or exclusion conditions (e.g., 'for current market rates use treasury_interest_rates instead'), leaving sibling differentiation to inference from the resource name.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.