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 disclose readOnlyHint, idempotentHint, and destructiveHint. The description adds value by listing return fields and mentioning the include_inactive parameter, without contradicting 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 sentences deliver all necessary information without redundancy or extraneous content, efficiently front-loading the core 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?
For a simple listing tool with one optional parameter and no output schema, the description adequately covers purpose, usage context, and return fields.
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%, so the description does not need to elaborate on parameters. It adds no extra meaning beyond what the schema provides, meeting the 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?
The description clearly states the action ('List') and resource ('the caller's active subscriptions'), distinguishing it 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 advises when to use this tool: 'review what you're monitoring before adding more or to find an id to cancel', providing clear context for invocation.
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 clearly distinct purposes, but research entry points overlap heavily (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the beta tool currently behaves identically to the stable version. Polymarket tools and AI visibility tools also create multiple near-overlapping options that require careful reading to disambiguate.
All names follow snake_case and are readable, but there is no consistent pattern: some are verb-first (ask_, resolve_, validate_, scan_), some noun-first (entity_profile, pipeworx_trending, polymarket_edges), and some are bare nouns (distance, destination). This mixed convention makes the tool set feel less predictable than it should.
35 tools is excessive for a server named Geodistance, and the actual purpose is a sprawling data-research and prediction-market toolkit with a few geospatial helpers. The set would be better split into focused servers; as-is it feels bloated and poorly scoped.
The data-research surface is quite complete (discovery, lookup, grounded answers, comparisons, subscriptions, memory, feedback), and geodistance has core operations like distance, destination, and coordinate conversion. However, the geographic functionality is thin and the overall grab-bag composition makes it hard to assess completeness against any single coherent domain; obvious geospatial features like geocoding, routing, or area calculations are absent.