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 read-only, idempotent, non-destructive behavior. Description adds value by specifying returned fields and the optional include_inactive parameter, without contradiction.
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, front-loaded with purpose and output fields, followed by usage guidance. 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?
For a simple list operation with one optional parameter and no output schema, the description lists return fields and usage context, fully covering the tool's functionality.
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 parameter description already in schema. Description does not add extra meaning beyond what the schema provides, meeting 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?
Clearly states 'List the caller's active subscriptions' with specific verb and resource, and lists return fields, distinguishing from sibling tools 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?
Explicitly says when to use: 'review what you're monitoring before adding more or to find an id to cancel', providing clear context for usage.
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.
Many tools have distinct purposes, but there is notable overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, as are bet_research and polymarket_edges. Detailed descriptions help, but an agent could still struggle to pick the right one.
Naming conventions are mixed: snake_case (fmcsa_carrier_lookup), verb_noun (ask_pipeworx, recall), and phrases (suggest_questions, generate_llms_txt). No consistent pattern, making it harder for an agent to infer tool purpose from name alone.
35 tools is high for a single server, but the server aggregates many domains. While each tool may serve a purpose, the count feels bloated and beyond typical scope (3-15). Some tools could be merged (e.g., the ask_pipeworx variants).
For the FMCSA domain, the four tools provide decent coverage. However, the server includes many tools for other domains (e.g., prediction markets, company profiles) without full lifecycle support (e.g., only lookup, no create/update). The set feels like a random collection rather than a coherent domain.