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 readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds useful info about returned fields. With high annotation coverage, this is sufficient.
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 covers purpose and return fields, second provides usage context. No wasted 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?
Simple read-only tool with one optional parameter and strong annotations. Description covers what it does, what it returns, and when to use. No gaps.
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% and the schema already describes the only parameter 'include_inactive' well. Tool description does not add extra parameter detail beyond 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?
Clearly states verb 'List' and resource 'the caller's active subscriptions'. Lists returned fields. Distinct 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?
Explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', giving concrete when-to-use guidance. Could have added exclusions but clear enough.
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 covers many unrelated domains (elevation, finance, prediction markets, AI visibility, etc.) with multiple overlapping tools per domain (e.g., three 'ask_pipeworx' variants, several 'polymarket' tools). An agent would struggle to distinguish which tool to use for a given task.
Names follow no consistent pattern: some use snake_case with vague verbs (e.g., 'process', 'run'), others use descriptive but unrelated prefixes ('ai_', 'ask_pipeworx_', 'polymarket_'). There is no uniform verb_noun structure.
With 32 tools, the count is reasonable for a large server, but the vast majority are irrelevant to the server's stated purpose (elevation). This mismatch makes the count inappropriate.
The server name 'Open Elevation' implies a focus on elevation data, yet only 2 of 32 tools (get_elevation, get_elevations) are related. There are severe gaps: no area elevation, no geocoding, no terrain analysis. The tool surface is largely off-topic.