Get traditional market prices via Databento. Default: SPY, VIX, TLT, GLD, USO. Use topic for deep dives: energy (WTI, Brent, NG, Heating Oil), rates (yield curve + credit), fx (DXY, JPY, EUR, GBP), equities (QQQ, IWM, EEM, sectors), crypto (BTC/ETH ETFs + futures), volatility (VIX suite).
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Deep dive topic: energy, rates, fx, equities, crypto, volatility. Omit for core 5. |
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 must carry the behavioral burden. It states the data source (Databento), the default symbols, and the topic categories, which provides useful context. However, it does not disclose output format, update frequency, or any limitations, leaving the agent to infer the exact behavior of 'market prices'.
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 core action, and every word adds value. The first sentence states the function and default universe; the second efficiently enumerates topic options and their coverage. No fluff or repetition exists.
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 the tool's low complexity (one optional parameter, no output schema), the description is largely sufficient for an agent to invoke it correctly. It covers defaults, topic choices, and data source. It could be slightly more complete by describing the shape of the returned prices, but this is not critical for a simple fetch tool.
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 schema already documents the single parameter 'topic' at 100% coverage, so the baseline is 3. The description adds significant value by enumerating accepted topic values and mapping each to concrete instruments (e.g., energy → WTI, Brent, NG, Heating Oil), which goes beyond the schema's generic 'Deep dive topic' phrasing.
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 uses a specific verb ('Get') with a clear resource ('traditional market prices via Databento') and defines the default scope (SPY, VIX, TLT, GLD, USO). It distinguishes itself from sibling tools by emphasizing a broad default snapshot plus deep-dive topics, making the tool's purpose unmistakable.
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 clearly states when to use the tool (for traditional market prices) and how to use the optional topic parameter for deep dives in specific asset classes. It does not explicitly name alternatives or exclusions, but the detailed topic mapping gives practical usage context.
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.
