Return the most recent U.S. CPSC product recalls (date-sorted, newest first) via SaferProducts.gov (keyless). Looks back a configurable window (default 90 days). Each result links to the official CPSC recall page. Data: CPSC/SaferProducts.gov.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of recent recalls to return (default 10). | |
| lookback_days | No | How many days back to scan (default 90). |
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 and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context: keyless access via SaferProducts.gov, date sorting, configurable lookback window, and that each result links to the official CPSC recall page. It goes beyond the annotations without contradicting them.
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?
Three sentences, no fluff, with the core purpose and sort order front-loaded. The second sentence adds the configurable window and the third clarifies output behavior (links to official pages). Every sentence contributes useful information.
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 low-complexity list tool with no required parameters and no output schema, the description is largely complete: it states source, access method, sorting, default window, and linkage. It does not enumerate the fields in each result or mention pagination, but the simple result shape and annotations make this a minor gap.
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 lookback_days) are already documented in the schema. The description adds the default lookback window (90 days) and implies limit controls result count, but it does not significantly extend the schema's parameter meaning. 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 names a specific verb ('Return'), a specific resource ('most recent U.S. CPSC product recalls'), and adds distinctive scope details: date-sorted newest first, configurable lookback, and links to official CPSC pages. It also clearly distinguishes itself from the sibling cpsc_recall_search by emphasizing recency rather than search.
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 implies when to use the tool: when the caller wants the most recent recalls within a time window rather than a keyword-based search. However, it does not explicitly mention alternatives like cpsc_recall_search or state when not to use this tool, leaving the routing decision to inference.
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.