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 and destructiveHint=false, so description's job is lighter. It adds value by noting default active-only behavior and the effect of include_inactive parameter. Describes returned fields, enhancing transparency 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: first states purpose and output fields, second gives usage guidance. No fluff, every sentence is justified and front-loaded. Excellent conciseness.
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 low complexity (1 optional param, no output schema), the description covers purpose, usage, and behavior adequately. It lists return fields and default behavior. No missing critical information; could mention pagination or limits but not needed 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 covers the only parameter (include_inactive) with 100% description coverage, so description doesn't need to add much. It mentions the parameter implicitly but adds no new semantics beyond the schema. 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?
Description clearly states 'List the caller's active subscriptions', providing specific verb and resource. It also enumerates return fields (id, type, params, etc.), making the output clear. Differentiates from siblings like subscribe/unsubscribe by focusing on listing.
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?
Explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' Tells when to use and provides practical context, distinguishing it from other actions.
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.
Several tools form near-overlapping clusters: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route questions, and bet_research/polymarket_edges/polymarket_arbitrage/polymarket_fill_risk/polymarket_kalshi_spread all target prediction-market edges. ask_pipeworx_beta is explicitly identical to ask_pipeworx today, so an agent must read long descriptions to pick correctly. Most other tools are distinct, but the ambiguous clusters pull the score down.
All names use snake_case and are readable, but conventions mix: many are verb_noun (ask_pipeworx, compare_entities, discover_tools, subscribe), several are noun phrases (macro_snapshot, indicator, entity_profile, polymarket_arbitrage), and a few are adjective_noun (recent_alerts, deep_research). The near-duplicate ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded suffixes form the only consistent family, but overall the naming pattern is not uniform.
33 tools is well beyond the 15-tool well-scoped range and even past the 25-tool 'too many' threshold. The server tries to be a data router, prediction-market desk, AI visibility checker, memory store, and subscription manager all at once, and includes an intentional duplicate (ask_pipeworx_beta). Several tools (remember/recall/forget, subscribe/unsubscribe/list_subscriptions/recent_alerts) could be their own server.
The surface covers question answering, deep research, entity resolution/profile/comparison, macro indicators, prediction-market analytics, subscriptions, memory, and feedback—no obvious dead ends for the main workflows. Minor gaps exist (e.g., no direct generic web search, no update for saved memory other than overwrite, and some niche additions like generate_llms_txt feel out of place), but the core data and research lifecycle is well covered.