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Glama

jev_config

Set or update API credentials and endpoints at runtime to prepare browser automation tasks that use TypeSafe and text models.

Instructions

Set or update API configuration at runtime.

Args: typesafe_api_key: TypeSafe API key for operation/target selection typesafe_base_url: TypeSafe API base URL (default: https://api.typesafe.ai/v1) text_model_api_key: Text model API key for TYPE_TEXT operations text_model_base_url: Text model API base URL (default: https://api.deepseek.com/v1) text_model: Text model name (default: deepseek-chat)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text_modelNo
typesafe_api_keyNo
typesafe_base_urlNo
text_model_api_keyNo
text_model_base_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose that configuration is applied at runtime and lists per-field defaults, but it omits critical behaviors for a mutation tool: whether settings persist across sessions or are process-global, whether partial updates are allowed, and what passing null (the schema default for every field) actually does.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The primary purpose is stated in a single front-loaded sentence, followed by a compact Args list that adds real information per field. No wasted prose, though the Args block is a plain list rather than tightly integrated guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no explanation, and all five parameters are documented. However, for a mutation tool with zero annotation coverage, the description should say more about persistence, permission requirements, and null/partial-update behavior before an agent could call it confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it largely does: all five parameters are explained with their purpose (key vs base URL vs model name) plus defaults for the base URLs and model. The one gap is that null semantics for each optional field are never clarified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Set or update API configuration at runtime'. This is unambiguous about what the tool does. It does not explicitly differentiate itself from siblings (jev_observe, jev_navigate, jev_act, jev_close), though those are plainly a different functional domain, so differentiation is largely self-evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'at runtime' hints at the context of use, but there is no explicit when-to-use guidance, no prerequisites, and no statement of when not to call it. With no alternative config tool named, the agent must infer the trigger conditions on its own.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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