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 readOnly, idempotent, non-destructive. Description adds specific return fields and implies default behavior (active only). No contradictions.
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, front-loaded with the core action, no redundancy.
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 list tool with one optional parameter and no output schema, description sufficiently covers behavior and return 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?
The single parameter (include_inactive) is fully described in the input schema; the description does not add further detail beyond schema coverage.
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?
Clearly states the tool lists the caller's active subscriptions and enumerates the returned fields (id, type, etc.). Distinguishes from siblings like subscribe and unsubscribe.
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 explicit use cases: reviewing monitored subscriptions before adding more, or finding an ID to cancel. Gives practical context.
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 overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog, and composite tools like entity_profile, recent_changes, compare_entities, and validate_claim draw on similar company-data sources. However, the descriptions are extremely detailed with explicit usage guidance, so an agent can usually pick correctly despite the overlap.
Most tool names follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, list_subscriptions, validate_claim), and domain prefixes like nola_, pipeworx_, and polymarket_ help group tools. Minor deviations such as entity_profile, recent_changes, and polymarket_edges are still readable and do not undermine the overall pattern.
At 34 tools, the set spans far beyond the 'Data Nola' name: New Orleans open data, general Pipeworx data research, Polymarket arbitrage, npm dependency scanning, AI visibility, memory, and subscriptions. This breadth makes the surface heavy and forces agents to triage a large and heterogeneous toolset.
The broader research platform is well covered: question answering, entity resolution, profiles, comparisons, claim validation, subscriptions, memory, and NOLA dataset querying all have solid lifecycle support. Minor gaps exist—no NOLA dataset metadata or write tools, no Polymarket order execution—but these are acceptable for a read-only data server.