COVID-19 vaccination coverage by US county (8xkx-amqh). Returns booster + primary series percentages over time. Useful for public-health gap analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 50) | |
| recip_state | No | Two-letter state code (e.g. 'CA') | |
| recip_county | No | County name |
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 useful context: the specific Socrata dataset ID (8xkx-amqh), the breakdown of returned metrics, and the time-series nature. However, it does not disclose default scope when no state/county filter is applied or the time window returned, and no contradiction exists 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?
Three compact clauses front-load the subject, then state the output composition and a use case. Every sentence earns its place; the dataset ID is slightly esoteric but useful for provenance when cross-referencing with cdc_dataset_query.
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 query tool with all-optional parameters and no output schema, the description covers what is returned. But it leaves gaps: what happens with zero filters (all counties? national aggregate?), what date range is covered, and how 'over time' is represented in the response. These are material for an agent invoking it correctly.
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% — limit, recip_state, and recip_county are all documented in the input schema. The description only hints at county-level granularity ('by US county') and adds no parameter semantics beyond the schema, so the baseline of 3 applies.
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?
Description states a clear verb+resource: returns COVID-19 vaccination coverage by US county, with explicit output content (booster + primary series percentages over time). This distinguishes it from sibling CDC tools covering flu, overdose deaths, and other indicators, though it doesn't explicitly differentiate from the generic cdc_dataset_query which could query the same dataset.
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?
'Useful for public-health gap analysis' implies a use case, but there is no explicit when-to-use/when-not-to-use guidance or mention of alternatives. Among many cdc_* siblings, an agent gets no routing help beyond the narrow subject matter.
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.