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, covering the safety profile. Description adds return field details and default active-only behavior implicitly via the existence of include_inactive. No contradictions, but no extra behavioral traits like pagination or error handling beyond the schema.
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 purpose and return fields, second gives usage guidance. Front-loaded with the core action, zero filler. Every sentence earns its place.
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 and no output schema, the description is complete: it explains what is listed, the return fields, and when to use it. Minor omissions like pagination or error behavior are not critical for this complexity.
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 the single parameter include_inactive fully described in the schema. The description does not directly mention this parameter but implies it by stating 'active subscriptions'. Baseline 3 is appropriate as the schema carries the parameter meaning.
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?
Description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools like subscribe/unsubscribe by focusing on listing, and further clarifies the scope (caller's active) and return fields.
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?
Provides explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when to use the tool, though it doesn't mention alternatives or when not to use it. Clear context without exclusions.
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 there is overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and several Polymarket-specific tools. Descriptions help differentiate, but some confusion is possible.
All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, compare_entities, validate_claim). No mixing of conventions, and naming is predictable.
33 tools is a high count for a finance server, including many meta-tools (memory, subscriptions, feedback) that are not finance-specific. The core finance set is reasonable, but the overall surface feels heavy.
The server covers many data sources (SEC, FRED, FDA, etc.) but lacks direct stock quotes or fundamental CRUD operations. Some areas (e.g., Polymarket, AI visibility) are over-represented, leaving gaps in core finance tasks.