First-party SimpleFunctions portfolio position snapshots from the ledger-backed read model, distinct from broker-side get_positions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows | |
| since | No | ISO lower bound | |
| until | No | ISO upper bound | |
| venue | No | ||
| apiKey | Yes | SimpleFunctions API key | |
| cursor | No | Pagination cursor | |
| source | No | ||
| marketId | No | ||
| thesisId | No | ||
| confidence | No | Attribution confidence such as exact, low, or unknown |
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?
No annotations are provided, so the description carries the burden. It discloses that the data is 'ledger-backed' and a 'read model', implying read-only and internally consistent snapshots. However, it does not mention required authentication (apiKey), pagination behavior, or data freshness, leaving notable gaps in behavioral disclosure.
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 a single, compact sentence that front-loads the key information (what the tool does) and adds a crucial distinction. Every word earns its place, with no fluff or repetition.
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 tool has 10 parameters, no output schema, and no annotations. The description provides only a high-level distinction from get_positions but does not explain the snapshot contents, intended use cases, or how the ledger-backed model differs in terms of data latency or scope. This leaves the agent with insufficient context to fully leverage the tool's filtering and return capabilities.
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 description coverage is 60%, so the baseline is 3. The description provides no additional parameter context beyond what the schema already offers. It does not clarify the meaning of venue, source, marketId, thesisId, or other undocumented parameters, but given moderate coverage, the baseline 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 returns portfolio position snapshots from a ledger-backed read model, with an explicit distinction from broker-side get_positions. This uses a specific verb ('snapshots') and resource ('portfolio positions'), and differentiates from a directly relevant sibling tool.
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 mentions 'distinct from broker-side get_positions', which acts as a when-not guideline and names an alternative tool. While it does not say 'use this for X', the context of first-party portfolio positions is clear, and the exclusion of broker-side positions is valuable 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.
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.
