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?
Annotations declare readOnlyHint=false and destructiveHint=false, so the description does not need to restate safety traits. It adds two valuable behavioral notes: 'paid per call' and 'ANY endpoint', which imply dynamic behavior and potential costs. No contradictions 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?
Two sentences, front-loaded with the core action and a clear prerequisite. Every word earns its place, and the structure is easy to scan.
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 openWorldHint, output schema, and sibling list, the description provides sufficient context: it mentions cost, prerequisite discovery, and the dynamic nature of the tool. It does not describe error cases or auth details, but those are not strongly needed with an output schema and generic endpoint semantics.
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 describes both parameters (path and query) with 100% coverage. The description adds little parameter-specific meaning beyond hinting at dynamic path discovery via list_endpoints, which is more about usage than parameter semantics. Baseline 3 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 uses a specific verb 'Call' with a clear resource 'ANY Reelfy endpoint by path with custom query parameters'. It distinguishes itself from siblings (e.g., 'list_endpoints', 'ai_image') by being a generic endpoint caller, and explicitly references a sibling tool as a prerequisite.
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?
It explicitly states 'Use list_endpoints first to discover paths and parameters', giving clear context for when this tool should be used relative to a sibling. However, it does not explicitly mention when not to use it (e.g., prefer a specialized tool when available).
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.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.