Search SCOTUS and federal appellate oral argument audio. Returns audio URLs and transcript snippets.
| Name | Required | Description | Default |
|---|---|---|---|
| court | No | Optional court ID filter. | |
| limit | No | Max results (1-50, default 10). | |
| query | Yes | Search text. |
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the return value context (audio URLs and transcript snippets), which is useful but does not disclose other behavioral traits such as pagination, court ID format, or result structure. This matches the baseline for a read-only tool with some added return info.
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, efficient sentence: 'Search SCOTUS and federal appellate oral argument audio. Returns audio URLs and transcript snippets.' Every word contributes to the purpose and output, with no filler or repetition. It is front-loaded with the core action.
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 search tool with 3 well-documented parameters, the description plus schema is largely complete. It names the scope and return content, and annotations cover the read-only behavior. The lack of an output schema means the agent does not know the exact result shape, but the basic response type is stated. This is adequate for the tool's simplicity.
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 has 100% description coverage, with each parameter (query, court, limit) already documented. The description adds no additional parameter-level detail beyond the schema. Baseline 3 is appropriate since the schema carries the full semantic burden for parameters.
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 clearly states the specific verb and resource: searching oral argument audio from SCOTUS and federal appellate courts. It also specifies the return type (audio URLs and transcript snippets), which distinguishes it from sibling tools like court_opinion_search and court_case_search. The resource scope is precise enough for an agent to identify when to use it.
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 provides clear context: it is for finding oral argument audio and transcripts in federal appellate courts. It does not explicitly name alternatives or exclusions, but the scope is so specific that confusion with sibling tools is unlikely. A slight improvement would be mentioning that written opinions belong to court_opinion_search.
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.