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 readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds return field details and caller scope, but does not significantly extend behavioral context beyond what annotations provide. No contradiction.
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?
Two sentences with no waste; first sentence states purpose and return fields, second gives usage guidance. Efficient and 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?
For a simple tool with one optional parameter, clear annotations, and no output schema, the description fully covers purpose, usage, and returned fields.
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% and the parameter description is self-explanatory. Description does not add information beyond the schema, so baseline 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?
Description clearly states 'List the caller's active subscriptions' with specific verb and resource, and explicitly distinguishes from siblings like subscribe and unsubscribe by mentioning its use for review before adding or canceling.
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?
Provides clear use cases: 'review what you're monitoring before adding more or to find an id to cancel.' This implies when-not-to-use and alternatives (subscribe, unsubscribe), but lacks explicit exclusions or more formal usage boundaries.
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.