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Glama

create_task

create task

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
inputsNo
licenseNounspecified
room_idNo
categoryNoresearch
objectiveNo
parent_idNo
difficultyNomedium
expires_atNo
risk_levelNoreview_required
attributionNo
descriptionYes
next_actionNo
allowed_toolsNo
output_formatNotext
expected_outputNoOriginal proposed result as plain text; include limitations and evidence where relevant.
next_action_kindNo
estimated_minutesNo
relay_leg_minutesNo
validation_methodNoindependent_review
next_action_outputNo
acceptance_criteriaNo
next_action_sourcesNo
source_expectationsNo
next_action_progressNo
required_output_keysNo
next_action_result_idNo
required_capabilitiesNo
external_side_effects_allowedNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

D1.5/5.0
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it offers none. It does not state whether the operation is a write, has side effects, requires permissions, is reversible, or what happens on success/failure. The description is entirely silent on behavior.

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

Conciseness2/5

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

The description is extremely brief ('create task'), which could be seen as concise, but it is under-specification rather than effective brevity. It does not front-load any useful information and fails to justify its presence given the tool's complexity. Every sentence (or word) should add value, and here none does.

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

Completeness1/5

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

For a tool with 29 parameters, no output schema, and no annotations, the description is grossly incomplete. It does not explain what constitutes a task, what the common use case is, how to structure the required 'title' and 'description', or what the system expects in return. An agent cannot reliably call this tool without additional external knowledge.

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

Parameters1/5

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

The input schema has 29 parameters with 0% description coverage, and the description 'create task' adds no meaning to any parameter. The agent cannot know the purpose of fields like 'source_expectations', 'relay_leg_minutes', or 'next_action_kind' from the description alone, and the schema lacks descriptions too, leaving the agent completely without guidance.

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

Purpose2/5

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

The description 'create task' is a direct restatement of the tool name and provides no additional specificity about what a task is, how it differs from other tools, or what creating one entails. It does not distinguish from siblings like create_room or task_action, and conveys the same information as the name alone.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention scenarios, prerequisites, or constraints, and the sibling tool list (e.g., create_room, post_message) suggests alternative actions that are not compared. The user is left to infer the usage context entirely.

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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