get_exhibitionsBrowse current and past exhibitions at the Art Institute. Returns exhibition titles, descriptions, and status (active or closed).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of exhibitions to return (1-100, default 10) |
| Name | Required | Description | Default |
|---|---|---|---|
| total | Yes | Total number of exhibitions | |
| exhibitions | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond that: it scopes to 'current and past exhibitions' and describes the return payload (titles, descriptions, status), which helps the agent understand the tool's scope and output.
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?
The description is two sentences, front-loaded with the main action and resource, followed by return details. Every sentence contributes useful information without redundancy or waste.
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 list tool with one optional parameter, strong read-only annotations, and an output schema, the description provides sufficient context for an agent to select and invoke it correctly. The scope and return content are covered, and no additional behavioral details are necessary.
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?
The input schema already fully describes the only parameter (limit) with range and default, so the description adds no additional parameter meaning. With 100% schema coverage, a baseline of 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?
The description clearly states the tool's function with a specific verb ('Browse') and resource ('exhibitions at the Art Institute'), and further specifies what it returns (titles, descriptions, status). This distinguishes it from sibling tools like get_artist or get_artwork, which target different entity types.
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 implies usage for browsing exhibitions but does not explicitly state when to use it versus alternatives, nor does it mention exclusions or when to prefer other tools. The purpose is clear enough that an agent could infer when to use it, but explicit guidance is absent.
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 clusters of tools heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions over the same underlying sources, and six polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) have blurry boundaries. The four art museum tools are entirely unrelated to the data-research tools, adding confusion to the set.
Names are consistently snake_case and generally readable, but the verb-object pattern is not consistent: bare verbs (remember, forget, recall, subscribe) sit alongside verb-first names (get_artwork, validate_claim, resolve_entity) and noun-first compounds (pipeworx_trending, polymarket_edges, ai_visibility_check). The repeated prefixes (ask_pipeworx, polymarket_) do provide some structure.
At 35 tools, the server is overstuffed. The core Pipeworx data and Polymarket analytics surface alone would justify roughly 20 tools, but memory management, subscription lifecycle, llms.txt generation, npm dependency scanning, claim validation, and Art Institute of Chicago lookups are unrelated additions that push the count well beyond a focused scope.
Each functional cluster is fairly complete on its own: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, data lookup has casual, grounded, deep, and validation modes, and prediction markets cover research, edges, arbitrage, fill risk, tracking, and cross-venue spreads. The issue is not missing capabilities but the lack of a single coherent domain.