search_datasetsSearch Centre-Val de Loire Open Data for datasets by keyword (regional services, transport, education & environment). Returns dataset_ids (pass to query/dataset_info), titles, themes and record counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s) to search dataset titles/descriptions. | |
| offset | No | Pagination offset (default 0). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "transport"
+ },
+ {
+ "limit": 50,
+ "offset": 0,
+ "query": "education schools"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safety (readOnlyHint, idempotentHint). The description adds what the tool returns (dataset_ids, titles, themes, record counts) and the regional scope, which is helpful beyond 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?
The description is a single, well-structured sentence that front-loads the purpose and includes key details without 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?
Given the simple schema and no output schema, the description adequately covers the search scope, return values, and how results link to other tools, making it complete for basic 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 covers all 3 parameters with 100% description coverage. The description does not add new details about parameters, so baseline score 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 clearly states the tool searches for datasets by keyword in a specific open data portal, listing example themes and return fields. It distinguishes from sibling tools like dataset_info by noting that dataset_ids can be passed to them.
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 keyword-based discovery and references downstream tools for IDs, giving context. However, it does not explicitly state when to avoid this tool or directly compare with all siblings.
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 tools have overlapping boundaries, especially ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim, all of which route through the same underlying source catalog. The beta variant is explicitly identical to the stable router right now, which forces agents to pick between tools that currently do the same thing. Many of the polymarket tools are also close enough that an agent must read long descriptions carefully to avoid mis-selection.
The set is readable and mostly snake_case, but the naming conventions are mixed: some tools use verb_noun (search_datasets, compare_entities, validate_claim), some are noun-like (entity_profile, dataset_info, bet_research), and some use product prefixes (pipeworx_trending, polymarket_edges). There is no single consistent pattern, though related clusters are internally recognizable.
Thirty-four tools is above the heavy threshold, and the apparent server purpose from the name is a regional open-data portal, which only needs the three dataset tools. The remaining tools are a sprawling Pipeworx research assistant covering prediction markets, npm dependencies, AI visibility, memory, subscriptions, and feedback, making the set feel overstuffed and poorly scoped for the stated server.
Within the broad data/research scope the coverage is quite deep: discovery, querying, grounded verification, entity profiling, comparisons, monitoring, subscriptions, memory, and prediction-market analysis all have dedicated tools. The Centre-Val de Loire core is adequately covered by search_datasets, dataset_info, and query, though a raw download or full-catalog listing endpoint is missing. The real weakness is not missing lifecycle steps but unclear boundaries between overlapping meta-tools.