search_datasetsSearch Tours Métropole Open Data for datasets by keyword (mobility, urban services & 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 / examplesPrevious value: -[
- {
- "query": "mobility"
- },
- {
- "limit": 50,
- "offset": 0,
- "query": "urban services environment"
- }
-]New value: +[
+ {
+ "query": "transport"
+ },
+ {
+ "limit": 50,
+ "offset": 0,
+ "query": "urbanisme"
+ }
+]Input schema / examplesAdded value: +[
+ {
+ "query": "mobility"
+ },
+ {
+ "limit": 50,
+ "offset": 0,
+ "query": "urban services environment"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds behavioral output traits (returns specific fields) and domain scope (Tours Métropole Open Data), which is beyond what annotations provide. No contradiction 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?
The description is a single, front-loaded sentence that states purpose, scope, and return values without any redundant or filler text. Every element earns its place.
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?
The tool is simple (3 params, no output schema), and annotations cover safety. The description covers purpose, return fields, and a usage hint. It could mention pagination behavior or empty-result handling, but given the simplicity and comprehensive schema/annotations, it is sufficiently complete.
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%, so all parameters are documented. The description adds context by providing example keyword categories (mobility, urban services & environment) and mentions the output includes dataset_ids, which indirectly guides parameter use. This goes slightly beyond the schema.
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 a specific verb ('Search'), a resource ('Tours Métropole Open Data'), and a scope ('by keyword (mobility, urban services & environment)'). It also lists the return fields (dataset_ids, titles, themes, record counts) and explicitly mentions passing dataset_ids to query/dataset_info, differentiating it from sibling tools.
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 clearly implies this tool is the entry point for searching datasets by keyword and indicates a downstream workflow ('pass to query/dataset_info'). It does not explicitly state when NOT to use it or name alternatives, but the context is strong enough for an agent to infer appropriate usage.
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 has a clearly distinct purpose. Even closely related tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by their use cases and safety guarantees. The multiple polymarket tools each focus on a unique aspect (arbitrage, edge scanning, persistence, fill risk, cross-venue spreads), avoiding ambiguity.
All tool names use lowercase with underscores, following a mostly verb_noun or domain_prefix_noun pattern (e.g., ask_pipeworx, entity_profile, resolve_entity). A few names like dataset_info and ai_visibility_check deviate slightly from a strict verb_noun structure, but the overall pattern is predictable and readable.
With 33 tools, the server is on the heavier end of the well-scoped range. However, the count is justified by the breadth of functionality: data queries, prediction markets, entity resolution, memory, monitoring, and more. The tools each serve a specific purpose, and none feel redundant.
The tool surface covers a wide range of use cases including data retrieval, comparison, research, monitoring, and memory. Minor gaps exist (e.g., no tool for placing prediction market trades or creating/updating Tours Métropole datasets), but these are likely intentional scope choices. Core workflows are well-supported.