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 declare read-only, idempotent, non-destructive. Description adds that it returns specific fields and defaults to active subscriptions, plus the include_inactive parameter. No contradictions.
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, front-loaded with core action, then 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?
Sufficient for a simple list tool with rich annotations; describes return fields and usage context. Could mention default value for include_inactive, but schema covers it.
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 single boolean parameter with 100% coverage. Description does not add extra meaning beyond what the schema's description already provides.
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 the tool lists the caller's active subscriptions, specifies return fields, and is distinct from sibling tools like subscribe/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 tells when to use: to review monitoring before adding more or to find an id to cancel. Implicitly excludes subscribe/unsubscribe 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.
Multiple tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) have overlapping research purposes. Additionally, many Polymarket and Pipeworx-specific tools are unrelated to the Connecticut Open Data server name, causing confusion.
Naming is highly inconsistent: some use snake_case (ask_pipeworx, resolve_entity), others use longer descriptive phrases (polymarket_fill_risk, scan_competitor_ai_presence), with no clear pattern.
The server claims to be about Connecticut Open Data but includes 34 tools, only 3 of which (datasets, metadata, query) are relevant. The vast majority are unrelated Pipeworx/Prediction Market tools, making the size inappropriate.
For Connecticut Open Data coverage, only basic dataset search, metadata, and query tools exist. Missing common operations like data upload, schema modification, or API key management for the open data portal.