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, openWorldHint, idempotentHint, destructiveHint. Description adds context by listing the return fields, which is useful 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, no wasted words, front-loaded with purpose.
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 no output schema and low parameter count, the description adequately lists return fields and provides usage context, making it complete for this simple tool.
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 clear description for the only parameter (include_inactive). Description does not add meaning beyond what's in the schema, so baseline 3 is appropriate.
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 specifies the returned fields (id, type, params, etc.). It distinguishes 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?
Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells when and why to use the tool.
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.
Each tool targets a distinct purpose: data lookup (ask_pipeworx vs deep_research), entity profiles, comparisons, memory, monitoring, and prediction market analysis. Overlaps are minimal and mitigated by explicit usage guidance (e.g., ask_pipeworx vs. ask_pipeworx_grounded vs. deep_research).
All tools use descriptive snake_case names following a verb_noun or verb_preposition pattern (e.g., entity_profile, resolve_entity, scan_dependency). The naming is predictable and internally consistent, making it easy for an agent to infer tool purposes.
33 tools is on the high end for typical MCP servers. However, the server covers an exceptionally broad domain (structured data across SEC, FDA, FRED, weather, news, crypto, etc.) and includes meta-tools, monitoring, and memory. The count is justified but may feel excessive for many use cases.
The tool surface covers the full lifecycle of data access and analysis: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, entity_profile), comparison, claim validation, monitoring, memory, and feedback. There are no obvious gaps for the declared domain, and a feedback tool is provided for missing functionality.