call_endpointCall ANY Reelfy endpoint by path with custom query parameters (paid per call). Use list_endpoints first to discover paths and parameters.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Endpoint path, e.g. /api/cve-exploit-score | |
| query | No | Query parameters as an object |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
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?
The description adds "paid per call," which is a critical behavioral trait beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false). This cost warning helps the agent weigh usage. The phrase "call ANY endpoint" aligns with openWorldHint and implicitly warns of arbitrary side effects, though it doesn't detail specifics. No contradiction with annotations.
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: the first states the core function and cost; the second provides a crucial prerequisite. Every clause earns its place, with no filler. It is front-loaded with the verb and resource, making it immediately scannable.
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 open-world nature and generic purpose, the description provides key context: it points to list_endpoints for discovery, mentions cost, and signals that any path is accepted. The presence of an output schema covers return values, so the description doesn't need to. It could add more about authentication or error handling, but the current guidance is sufficient for safe operation.
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 covers 100% of parameters with descriptions: path and query. The description echoes this by saying "by path with custom query parameters," but adds no additional semantic nuance beyond what the schema provides. The baseline of 3 is appropriate since the schema carries the load.
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 action ("Call ANY Reelfy endpoint by path") and adds an important qualifier ("ANY") that distinguishes it from sibling tools like list_endpoints, which merely discover endpoints. It also mentions the ability to pass custom query parameters, making the 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 provides explicit usage guidance: "Use list_endpoints first to discover paths and parameters." This not only tells the agent when to use this tool but also establishes a prerequisite, reducing misuse. It implies call_endpoint is for executing endpoints after discovery, which distinguishes it from dedicated endpoint tools in the sibling list.
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.
Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.