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 indicate readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the return fields and the behavior of the include_inactive parameter, complementing the annotations well.
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 concise sentences: first sentence states purpose and return fields, second provides usage guidance. No redundancy or unnecessary words.
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 operation with one optional parameter and no output schema, the description covers enough: it lists returned fields and the parameter's effect. Annotations further clarify safety and idempotency.
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 baseline 3 applies. The description does not add new meaning beyond the schema's parameter description. It mentions 'active subscriptions' but the parameter is fully documented in 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 uses a specific verb 'List' and resource 'subscriptions,' clearly indicating it returns the caller's active subscriptions. It lists returned fields (id, type, params, etc.) and distinguishes from sibling tools like subscribe/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?
The description explicitly says to use it 'to review what you're monitoring before adding more or to find an id to cancel,' providing clear context. It does not explicitly state when not to use it, but siblings like subscribe/unsubscribe imply alternatives.
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.
ask_pipeworx_beta is explicitly stated to be currently identical to ask_pipeworx, which is a direct duplication. The polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes around finding and validating betting edges, and the ask_pipeworx / ask_pipeworx_grounded / deep_research / validate_claim tools all handle natural-language 'look up X' queries, making misselection likely without reading lengthy descriptions.
snake_case is uniform and helpful prefixes (mbta_, polymarket_, pipeworx_, ask_pipeworx) create recognizable families. However, verb style is inconsistent — imperative verbs like ask/compare/discover/validate mix with noun-first names like bet_research, entity_profile, and search_within, and the memory trio (remember/recall/forget) doesn't share a common prefix.
At 35 tools this exceeds the comfortable range, and the count is inflated by near-duplicates (ask_pipeworx_beta) and a dense 6-tool polymarket family. The server also mixes unrelated domains — only 4 of 35 tools are MBTA transit tools while the rest are Pipeworx data research, prediction markets, memory, and subscriptions — making it a kitchen sink rather than a well-scoped set.
The Pipeworx research surface is thorough: query, grounded verification, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscriptions are all covered with few dead ends. Minor gaps exist — the MBTA portion lacks schedule/line-detail tools beyond departures and alerts, and the AI-visibility feature feels bolted on without deeper integration.