Community-tested settings for generative video and image models: CFG, steps, denoise, fps, resolution, sampler, LoRA, ComfyUI, fp8/quantization, and training notes.
| Name | Required | Description | Default |
|---|---|---|---|
| task | No | ||
| model | Yes |
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 empty annotations, the description carries the full burden of disclosing behavior, but it does not state that the tool is read-only, what it returns, or any side effects. The name suggests a lookup, but the description never explicitly says it reads or retrieves settings, nor does it mention response structure or limitations. This is a significant gap for a tool with no annotation support.
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 core purpose ('Community-tested settings...') before listing key topics. It contains no filler words and efficiently communicates the tool's scope. The structure is acceptable, though it is a fragment rather than a full sentence, which slightly reduces clarity.
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 no annotations, no output schema, and sparse parameter documentation. The description lists many content areas but does not explain input usage, output format, or any behavioral context. This is insufficient for an agent to confidently invoke the tool; the agent must guess parameter formats and expected results. Given the tool's apparent simplicity, a more complete description is expected.
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 has two parameters (task, model) with no descriptions and 0% schema coverage. The description does not explain the role of either parameter; it only lists content areas like CFG, steps, and resolution, which are likely values returned rather than input semantics. The required 'model' parameter is implied by the tool name, but 'task' remains ambiguous. The description fails to compensate for the lack of schema documentation.
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 identifies the resource as 'community-tested settings for generative video and image models' and lists specific content areas, making the tool's purpose comprehensible. However, it lacks an explicit verb like 'looks up' or 'retrieves', relying on the tool name to convey action. It does not explicitly differentiate from siblings such as gen_video_intel, though the content focus is distinct.
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 provides no guidance on when to use this tool, when not to use it, or suitable alternatives. There is no mention of context, prerequisites, or situations where other tools might be preferred. The description is purely a content summary with no usage direction.
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.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.