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Glama

jev_analyze

Analyze any text by running a planning pipeline to return generated questions, answers, and a summary.

Instructions

Run the full pipeline (LLM planner -> local Jev) on any text; returns questions, answers and a summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

There are no annotations, so the description carries the full burden. It does disclose the internal progression ('LLM planner -> local Jev') and the high-level return content, which adds behavior context. However, it does not mention potential side effects, latency, cost, authentication needs, or whether the operation is effectively stateless and read-only.

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

Conciseness5/5

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

The description is a single, well-structured sentence. It front-loads the core verb and resource, then gives the pipeline detail and output items with no filler words.

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?

For a one-parameter tool with an output schema, the description is adequate but not complete. It explains what the tool does and what it returns, but with no annotations and no usage-versus-sibling guidance, the agent is left to infer when this tool should be invoked instead of ask/plan/health and whether there are any operational caveats.

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

Parameters3/5

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

The schema has only one parameter, 'prompt', with no description coverage (0%). The description adds modest value by saying 'any text' tells the agent the prompt is free-form text, but it does not explain length limits, formatting expectations, or how the prompt should be phrased to get optimal questions/answers/summary results.

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?

The description clearly states a specific action ('Run the full pipeline'), the input scope ('any text'), and the outputs ('questions, answers and a summary'). It does not explicitly distinguish itself from siblings like jev_ask or jev_plan, but the 'full pipeline' phrasing implies it is the comprehensive analysis entry point.

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 description lacks explicit guidance on when to use this tool versus its siblings, such as jev_ask, jev_plan, or jev_health. 'On any text' is a broad context, not a precise usage rule, and no exclusions or alternative conditions are mentioned.

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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