Fetch any BLS time series by its raw series ID (e.g. 'LNS14000000' for the unemployment rate, or a state/industry series). For when you know the exact BLS series ID.
| Name | Required | Description | Default |
|---|---|---|---|
| end_year | No | End year (optional). | |
| series_id | Yes | BLS series ID, e.g. 'LNS14000000'. | |
| start_year | No | Start year (optional). |
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, and the description's 'Fetch' aligns as a safe read operation. The description adds no behavioral caveats such as output format, rate limits, or year-range constraints, so it only mildly extends what annotations already provide.
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 the action and example front-loaded, followed by the exact-ID selection criterion. No filler, fluff, or redundant restatement of schema fields.
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 read-only fetch with one required parameter and clearly documented optional years, selection and invocation are well covered. With no output schema, the response shape is left unstated, but the description still provides enough for correct tool usage.
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%, with series_id, start_year, and end_year already described. The description repeats an example ID also found in the schema and does not clarify year range behavior, so it meets the baseline without adding meaningful parameter insight.
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 'Fetch any BLS time series by its raw series ID', which is a specific verb, resource, and exact input. The examples and the 'when you know the exact BLS series ID' clause distinguish it from lookup or search siblings.
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?
It explicitly identifies the right condition: use when the exact BLS series ID is known. It does not name sibling alternatives or provide a when-not, so exclusions are left implicit.
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.