Skip to main content
Glama

validate_response

Validates parsed JSON against a shape spec to enforce structured output. Returns validation result with human-readable error for retry feedback.

Instructions

Validate a parsed JSON value against an agentcast shape spec. Spec maps field name to type: 'string', 'number', 'boolean', 'array', 'object'. Suffix with '?' for optional. Returns valid=true on success, or valid=false with a human-readable error string suitable for retry feedback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shapeYesShape spec: { fieldName: 'string' | 'number' | 'boolean' | 'array' | 'object', ... }. Append '?' for optional fields, e.g. { name: 'string', age: 'number?' }.
valueYesThe parsed JSON value to validate (any shape).
Behavior4/5

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

Discloses return format, spec details, optional fields, and error string suitability for retry. No annotations provided, so description carries full burden.

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?

Two sentences, front-loaded purpose, no wasted words. Every sentence adds value.

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

Completeness4/5

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

Covers validation logic, shape format, return values, and ties to retry feedback. Could mention what happens if input is not parsed JSON, but overall sufficient.

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

Parameters5/5

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

Adds meaning beyond schema: explains shape spec format, optional field syntax, and return behavior. Schema coverage is 100% but description enriches understanding.

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

Purpose5/5

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

The description explicitly states it validates a parsed JSON value against a shape spec, with clear return behavior. It distinguishes from sibling tools (extract_json, build_retry_prompt) by focusing on validation.

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

Usage Guidelines3/5

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

Implicitly indicates use for validation after parsing, but no explicit when-to-use, when-not, or comparison to siblings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MukundaKatta/agentcast-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server