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, idempotentHint, etc. Description adds value by detailing the return fields and typical use, enhancing agent understanding of safe behavior.
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 conveys core purpose and output, second provides actionable usage guidance. No unnecessary 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 listing tool with one optional parameter and no output schema, the description covers purpose, return fields, and usage. Slightly lacking in explaining edge cases like empty results, but still complete enough.
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?
Input schema has one optional boolean parameter with description; schema coverage is 100%. Description does not need to add parameter details since the schema already suffices.
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 a specific verb and resource, and enumerates 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 advises using it to review existing subscriptions before adding more or to find an ID to cancel. Lacks explicit when-not guidance, but context is clear.
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 tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query entry points (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now); polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research form a dense prediction-market suite; ai_visibility_check and scan_competitor_ai_presence are single vs. multi variants. An agent would frequently struggle to pick the right tool without reading long descriptions.
Most tools follow a clean snake_case convention and are mostly verb_noun (generate_ulid, parse_ulid, list_subscriptions, resolve_entity, validate_claim, compare_entities), making the set predictable. Minor deviations exist (entity_profile, deep_research, bet_research, ask_pipeworx_beta) but they are still readable and don't break the overall pattern.
33 tools is well above the 25-tool threshold for a heavy surface, and the server name 'Ulid' suggests a tiny scope that wildly mismatches the actual content. While the real domain (Pipeworx data + prediction markets) is broad, the set bundles many subdomains into one server, making navigation and selection costly.
For the actual apparent domain—structured data research, entity lookups, verification, prediction-market analysis, memory, and subscriptions—coverage is strong: query, grounded query, deep research, entity profiles, comparisons, resolution, validation, discovery, alerts, and memory tools are all present. ULID functionality is minimal but sufficient (generate + parse). Minor gaps exist (e.g., no direct single-source browser beyond discover_tools) but agents can work around them.