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 provide readOnlyHint, idempotentHint, and destructiveHint. Description adds return field details and clarifies scope (caller's active subscriptions). 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: first states action and return fields, second gives usage guidance. No redundant information, 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?
Description is complete for a simple list tool with one optional parameter. Annotations cover behavioral traits, description covers return values and usage context.
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 100% of parameters with description for include_inactive. Description does not add further parameter meaning; 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?
The description specifies the verb 'List' and resource 'subscriptions', scoped to 'caller's active'. It lists return fields, clearly distinguishing from sibling tools 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?
Explicit guidance to use for reviewing monitoring before adding more or finding an id to cancel. While it doesn't name specific sibling tools, the context implies them.
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.
Most tools have distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) creates genuine confusion — the beta is explicitly identical to the stable router right now. The Polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) also overlaps in the 'find a betting edge' space, though the descriptions do a decent job of carving out niches.
Names follow a readable snake_case style with useful prefixes (ask_pipeworx, pipeworx_, polymarket_), but the set mixes verb-first (compare_entities, resolve_entity), noun-first (entity_profile, recent_alerts), and bare verbs (remember, recall, forget) with no consistent convention. The per-domain prefixes provide some predictability, but the overall pattern is not uniform.
32 tools is above the 25+ threshold for 'too many,' and the set feels bloated with hyper-niche utilities (generate_llms_txt, scan_dependency, polymarket_edge_tracker) that are unrelated to the server's apparent INSEE identity. Even as a broad data platform, several tools could be consolidated (the three ask_pipeworx variants, the AI-visibility single/comparison pair).
The Pipeworx core workflows are well-covered: routing, grounded answers, deep research, entity profiles, comparisons, claim verification, memory, subscriptions, and alerts all close loops. However, the server is named 'Insee' but only one of 32 tools touches French business registry data, leaving the implied domain almost entirely absent; peripheral one-off tools (npm, llms.txt) also have no supporting lifecycle tools.