search_datasetsSearch CDC public health datasets by keyword. Returns dataset names, descriptions, IDs, and update dates. Example: search_datasets("influenza surveillance").
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search keywords (e.g., "covid cases", "influenza", "vaccination rates", "mortality") |
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of datasets returned | |
| datasets | Yes |
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. The description adds the return structure (names, descriptions, IDs, update dates) but no additional behavioral traits beyond what annotations 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?
The description is two sentences plus an example, front-loading the core purpose and returning info with no unnecessary words.
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 tool with one parameter, output schema available, and comprehensive annotations, the description adequately covers purpose, return format, and use case, leaving no gaps.
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 coverage is 100% for the single 'query' parameter, with a description including examples. The description adds no new semantic meaning beyond the schema, so baseline 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?
The description explicitly states the verb 'Search', the resource 'CDC public health datasets', and the method 'by keyword', clearly distinguishing it from siblings like 'get_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?
The description provides a clear use case with an example and implies usage for broad dataset search, but does not explicitly state when to avoid this tool or mention alternatives like 'search_within'.
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.
Tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research serve similar querying purposes, and there is overlap among prediction market tools (e.g., polymarket_arbitrage, polymarket_edges). However, descriptions help differentiate them, so agents can usually select the correct one.
Most tools use snake_case with verbs (ask_, resolve_, validate_), but there are noun-style exceptions (entity_profile, recent_changes, pipeworx_trending) and mixed naming among Polymarket tools. The pattern is readable but not fully consistent.
With 32 tools, the server feels bloated, especially given the name 'Cdc' implies a focus on CDC data, yet many tools cover unrelated domains like prediction markets and company profiles. Several tools could be consolidated or removed to align with a narrower scope.
The CDC domain is thinly covered with only search and get for datasets, lacking upload or advanced filtering. Company financials are limited to basic fundamentals from 10-Ks. Prediction markets are well-covered with arbitrage, edges, and fill risk. The server has notable gaps in its core domain.