Advanced: Current resting orders on Kalshi.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | SimpleFunctions API key. Get one at https://simplefunctions.dev/dashboard/keys | |
| status | No | Filter by status: resting, canceled, executed. Default: resting | resting |
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?
With no annotations, the description carries the full burden but only states that the tool returns current resting orders. It omits behavioral details like auth requirements, pagination, data freshness, or error handling, offering minimal transparency.
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 extremely brief and front-loaded, conveying the core idea in a few words. The 'Advanced:' prefix is somewhat unnecessary, but the overall structure is efficient.
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 a simple 2-parameter tool with no output schema or annotations, the description is minimally adequate. However, it leaves uncertainty about response structure and does not leverage context to clarify usage, so it falls short of full completeness.
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 provides 100% coverage, explaining both apiKey and status with defaults and allowed values. The description adds no additional parameter semantics, so a baseline score 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 specifies the tool retrieves current resting orders on Kalshi. The phrase 'resting orders' distinguishes it from executed or canceled order queries, aligning well with sibling tools like get_fills.
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?
No guidance is provided about when to use this tool versus alternatives such as get_fills or get_positions. It does not mention exclusions, prerequisites, or appropriate contexts beyond the terse 'Advanced' label.
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.
Many tools have overlapping purposes, such as multiple market query tools (scan_markets, screen_markets, get_market_detail, get_market_diff, get_market_history, inspect_ticker) and legislative tools (legislation, get_legislation, list_legislation, query_gov). Aliases like get_heartbeat_config/get_heartbeat_status and explore_public/explore_theses add further confusion. An agent would struggle to select the correct tool without deeply reading each description.
Most tools follow a verb_noun pattern (get_, list_, create_, update_), but there are notable deviations: 'legislation' lacks the 'get_' prefix, 'stt' and 'tts' are acronyms, 'monitor_the_situation' is a full phrase, and 'x_account/x_news/x_volume' use a non-standard prefix. The overall style is readable, but the mixed conventions reduce predictability.
108 tools is extreme for any server, even one covering prediction markets, trading, portfolio management, forum, skills, and speech. The massive surface area overwhelms agents and makes the server feel more like a platform than a coherent toolkit. This many tools inevitably leads to redundancy and maintenance burden.
The server covers an impressively broad domain: market data, thesis management, intents, strategies, positions, portfolio, forum, skills, legislative and economic queries, and audio/visual processing. Minor gaps exist (e.g., no delete for skills/theses, no update for some portfolio items) but core workflows are well-supported. Overall lifecycle coverage for most entities is strong.
