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 readOnly and non-destructive. Description adds return field details and the effect of the include_inactive parameter.
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, front-loaded with purpose and return 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 tool with one optional param and no output schema, the description adequately covers return fields and usage. Could mention pagination or limits if applicable, but not required.
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 parameter description in schema. Description mentions the parameter's effect but adds no new semantics beyond the 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 'List the caller's active subscriptions' with specific return fields. Distinguishes 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 to use for review before adding more subscriptions or to find an id to cancel. Provides clear usage context.
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 clearly distinct purposes, but there are some close groups: the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) require careful reading to distinguish subtle differences.
Naming is inconsistent: some tools start with verbs (ask, compare, generate), others with nouns (inegi_indicator, entity_profile), and there is no uniform verb_noun pattern. However, within families, naming is consistent (e.g., ask_pipeworx*).
With 33 tools, the server is on the higher side but not excessive. Each tool serves a distinct purpose, though some could be merged (e.g., ask_pipeworx variants). The count is justified by the breadth of domains covered (INEGI, Pipeworx, Polymarket, utilities).
The toolset covers a wide range of functionalities: Mexican demographic/economic data, general structured data queries, prediction market analysis, and utility tools. Minor gaps exist (e.g., limited direct API for some Mexican indicators), but overall the surface is comprehensive for the stated purpose.