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 and idempotent, but the description adds value by listing return fields and mentioning the optional include_inactive parameter, clarifying behavior beyond annotations.
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, front-loaded with purpose, zero waste.
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?
Given the simple tool (1 optional boolean, no output schema), the description fully covers functionality, return data, and use cases.
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 a description for the boolean parameter. The tool description does not add further meaning, meeting the baseline.
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 the action ('List the caller's active subscriptions') and specifies the return fields, distinguishing it 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?
Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use and implies 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.
The tool set has severe overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve similar query/discovery purposes, and multiple polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) overlap heavily in finding betting opportunities. An agent would struggle to select among these without deep familiarity, especially when ask_pipeworx and ask_pipeworx_beta are currently identical.
Individual families are internally consistent (ca_procurement_*, polymarket_*, pipeworx_*), but the server as a whole mixes domain-prefixed snake_case, bare verb phrases (ask_pipeworx, bet_research), and descriptive noun phrases (entity_profile, recent_changes). More importantly, the vast majority of tool names have nothing to do with the server's stated 'Ca Procurement' identity, so the naming fails to signal a coherent tool set.
36 tools is well past the 25+ threshold for 'too many,' and over 85% of them (31 tools) are unrelated to California procurement—they cover general data lookup, prediction markets, npm packages, and memory storage. A scoped CA procurement server would reasonably have 5–8 tools; this is a general-purpose data platform wearing a procurement label.
The five relevant ca_procurement_* tools cover the main read-side query patterns well: award search, commodity rankings, department profiles, supplier aggregation, and top suppliers. However, the surface lacks contract/award detail retrieval by ID, solicitation or RFP search, and any vendor registration or contract lifecycle data, leaving notable gaps for a procurement-focused tool set.