Scenario analysis: "what if OPEC cuts production?" Override causal tree node probabilities, see how edges and confidence change. Zero LLM cost, instant.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | SimpleFunctions API key. Get one at https://simplefunctions.dev/dashboard/keys | |
| thesisId | Yes | Thesis ID | |
| overrides | Yes | Node probability overrides, e.g. {"n1": 0.1, "n3": 0.2} |
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 of behavioral disclosure. It does not explicitly state whether the overrides are temporary/simulated or persist, nor does it mention any side effects or required permissions. 'Scenario analysis' and 'what if' imply a non-destructive read-only simulation, but this is not stated explicitly.
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 concise: two sentences with a useful example and key differentiators (zero cost, instant). Every word earns its place, and the structure is front-loaded with the core action.
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 tool with no output schema and only 3 parameters, the description covers the core behavior (override, see changes), the use case (scenario analysis), and a key attribute (zero cost/instant). It could be more explicit about the read-only nature and output format, but it is generally complete for a simple 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?
Schema description coverage is 100%, and the schema already provides clear descriptions for all parameters, including an example for 'overrides'. The description adds minimal extra meaning beyond the schema, essentially echoing 'causal tree node probabilities'.
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: overriding causal tree node probabilities and observing changes in edges and confidence. The 'what if' example and verb 'Override' specify the action and resource, distinguishing it from sibling tools like update_nodes and get_edges.
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 hypothetical scenario analysis and highlights 'Zero LLM cost, instant' as a benefit, but it does not explicitly name alternatives or state when not to use this tool. The context is clear but lacks direct comparison to sibling tools.
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.
