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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds return field details and parameter behavior (include_inactive), but does not disclose additional behavioral traits beyond what annotations already cover.
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 usage. Every sentence adds value with no redundancy.
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?
The description fully covers purpose, usage, returned fields, and parameter behavior for this simple tool. No gaps given the lack of output schema and single optional parameter.
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 baseline is 3. The description does not add new meaning beyond the schema's description for the single parameter; it only reiterates the default behavior.
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 tool lists the caller's active subscriptions and explicitly lists returned fields (id, type, params, created_at, last_fired_at, fire_count). This distinguishes it from siblings 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?
The description explicitly states when to use this tool: 'review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and guidance.
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.
Multiple tools have overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research heavily overlap with them, and entity_profile, compare_entities, recent_changes, and validate_claim all circle the same company-data space. The prediction-market cluster (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also requires careful reading to distinguish. The descriptions help, but the set relies on them to disambiguate near-duplicates.
Nearly all tools follow a consistent lowercase snake_case style, whether verb_noun (search_complexes, validate_claim, unsubscribe), noun_verb (entity_profile, recent_changes), or brand-like (ask_pipeworx, polymarket_edges). There is no camelCase mixing or chaotic verb style. Minor inconsistency exists between imperative verbs (remember, forget, subscribe) and noun-style names, but the overall pattern is readable and predictable.
33 tools is well above the 25-tool 'heavy' threshold, and many are meta-tools (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending, remember/recall/forget) that pad the surface. The server is named 'Complex Portal', yet only two tools serve that purpose—the rest belong to a broad data platform. The count feels inflated and misaligned with the server's stated identity.
For the broad Pipeworx data platform the surface is fairly complete: universal querying, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and feedback. For the Complex Portal domain named by the server, coverage is thin—just search and fetch-by-accession, with no browsing, species filtering, or cross-reference tools. The core workflow works, but the namesake domain is under-served relative to the rest of the set.