Recent docket entries filed in a specific court, ordered newest first.
| Name | Required | Description | Default |
|---|---|---|---|
| court | Yes | Court ID, e.g. 'nysd', 'cand'. | |
| limit | No | Max results (1-50, default 10). |
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 genuine behavioral context beyond annotations: newest-first ordering and that results are scoped to a single court's docket entries. Nothing contradicts the 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?
A single 10-word sentence with zero filler: the core resource ('recent docket entries'), scope ('specific court'), and ordering ('newest first') are all present, and the ordering detail is placed at the end as a qualifier. Every word earns its place.
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 2-parameter, read-only, idempotent tool with no nested objects, the description covers what, where, and ordering. No output schema exists, so return shape is unspecified, but for a recent-list tool this is a minor gap rather than a blocker.
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 the schema fully documents both parameters (court with examples 'nysd'/'cand', limit with range and default). With full coverage the baseline is 3; the description's phrase 'in a specific court' only marginally reinforces the court parameter and adds nothing about limit.
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 names a specific resource (recent docket entries) and scope (a specific court), with a clear verb implied ('list/get'). It is semantically distinct from siblings like court_opinion_search and court_judge_lookup, but it does not explicitly differentiate from court_docket_lookup, which could plausibly overlap in an agent's mind.
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 usage context is implied rather than stated: an agent can infer this is for browsing recent docket activity in a court, but the description never says when to prefer this over court_docket_lookup, court_case_search, or the caselaw tools. No exclusions or alternatives are named.
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.