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 readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by specifying the exact return fields (id, type, params, etc.), which is helpful beyond the safety profile.
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, no wasted words. The first sentence states the action and output, the second provides usage guidance. Ideal conciseness.
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, the description covers purpose, return structure, and usage context. No output schema exists, but the description explicitly lists the returned fields, so completeness is high.
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% (one parameter with a description). The description does not add meaning beyond the schema: it mentions 'active' as default, which aligns with the parameter description, but no extra parameter context is provided.
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 it lists the caller's active subscriptions and enumerates the returned fields (id, type, params, etc.), distinguishing it from sibling tools like subscribe or 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?
Explicitly tells the agent when to use the tool: 'to review what you're monitoring before adding more or to find an id to cancel', providing clear use cases and avoiding 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.
Several tools occupy nearly interchangeable roles: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, and validate_claim all route similar factual queries. discover_tools/suggest_questions and bet_research/polymarket_edges similarly overlap, so an agent needs to read long descriptions to avoid misselection.
All names are readable lowercase snake_case, but the conventions are mixed: imperative verb_noun names (list_subscriptions, validate_claim) sit alongside noun phrases (polymarket_edges, recent_alerts), bare verbs (forget, subscribe), and variant suffixes (ask_pipeworx_beta/grounded). It is not chaotic, but there is no single predictable naming pattern.
Thirty-two tools is far more than the apparent Texas DMV scope supports: only tx_dmv_vehicle_registrations is DMV-related, and the rest are Pipeworx platform, prediction-market, memory, and unrelated utility tools. The count is excessive for the server's stated name and purpose.
The Texas DMV surface is severely incomplete: a single statewide registration-count tool covering fiscal years 2001-2021, with no title/registration transactions, VIN lookup, driver services, county/ZIP breakdowns, or current data. The tool's own description references a California DMV companion that is not present, leaving obvious gaps for any realistic DMV workflow.