search_datasetsSearch Latvia Open Data (Latvia) for datasets by keyword. Returns each dataset's id/name, title, organization, and its resources (each with a resource_id for query_resource).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s); blank/"*" lists all. | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "population"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "economy"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds valuable behavioral context by specifying the exact return structure (dataset id/name, title, organization, resources with resource_id), which helps the agent understand what the tool provides.
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: the first defines purpose, the second details return fields. Every sentence adds value with no redundancy, and key information is front-loaded.
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?
Given the tool has 3 parameters with full schema coverage, no output schema, and comprehensive annotations, the description provides complete context: it names the data source (Latvia Open Data), specifies what is returned, and links to a sibling tool (query_resource). No gaps remain.
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%, with clear descriptions for all parameters (limit, query, offset). The description does not add any additional parameter semantics beyond what is already in the schema, so baseline score of 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 clearly states the verb 'search' and the resource 'Latvia Open Data datasets.' It lists specific return fields (id/name, title, organization, resources with resource_id), which distinguishes it from sibling tools like query_resource or search_within.
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 usage for searching datasets and mentions query_resource for next steps, but it does not explicitly state when to use this tool over alternatives, nor does it provide exclusion criteria.
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.
Each tool targets a distinct purpose: data lookup (ask_pipeworx vs deep_research), entity profiles, comparisons, memory, monitoring, and prediction market analysis. Overlaps are minimal and mitigated by explicit usage guidance (e.g., ask_pipeworx vs. ask_pipeworx_grounded vs. deep_research).
All tools use descriptive snake_case names following a verb_noun or verb_preposition pattern (e.g., entity_profile, resolve_entity, scan_dependency). The naming is predictable and internally consistent, making it easy for an agent to infer tool purposes.
33 tools is on the high end for typical MCP servers. However, the server covers an exceptionally broad domain (structured data across SEC, FDA, FRED, weather, news, crypto, etc.) and includes meta-tools, monitoring, and memory. The count is justified but may feel excessive for many use cases.
The tool surface covers the full lifecycle of data access and analysis: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, entity_profile), comparison, claim validation, monitoring, memory, and feedback. There are no obvious gaps for the declared domain, and a feedback tool is provided for missing functionality.