list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
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 read-only and idempotent behavior. The description adds value by specifying return fields (id, type, params, created_at, etc.) and stating that it returns active subscriptions by default, which is useful context 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 with zero waste. It front-loads the purpose and return values, then provides usage guidance. Every sentence is necessary and informative.
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 listing tool with one optional parameter and no output schema, the description is complete. It covers purpose, return fields, and usage context. It does not mention pagination or limits, but that is acceptable for this straightforward tool.
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 the parameter 'include_inactive' already described. The description does not add extra meaning about the parameter beyond what the schema provides, so it meets the baseline for a well-covered 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 clearly states it lists the caller's active subscriptions, specifying the exact verb and resource. It also differentiates from sibling tools like subscribe and unsubscribe by mentioning use cases for reviewing before adding or finding an id to cancel.
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 explicit use cases: review subscriptions before adding more or find an id to cancel. It gives clear context for when to use the tool relative to siblings, though it does not explicitly state when not to use it.
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.
Many tools have overlapping purposes, e.g., multiple ask/tools for querying (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple company analysis tools (entity_profile, compare_entities, recent_changes). The prediction market tools (bet_research, polymarket_arbitrage, etc.) further blur distinctions. Agents would frequently select the wrong tool.
Naming conventions are inconsistent: snake_case (ask_pipeworx, dataset_info), camelCase (ai_visibility_check, scan_competitor_ai_presence), and phrases (pipeworx_feedback, polymarket_arbitrage). No pattern emerges, making tool discovery harder.
33 tools is excessive for a server ostensibly about Île-de-France Open Data. Only 3 tools (dataset_info, query, search_datasets) relate to that domain, while the rest are a general-purpose data agent with many disjoint capabilities. The count feels bloated and unfocused.
For the declared purpose (Île-de-France data), the tool set is missing CRUD operations (no create/update/delete). For the actual general data use, there are gaps like missing person entity resolution, data visualization, and file handling. The deep_research tool requires an account, creating a barrier.