Skip to main content
Glama
allenc84
by allenc84

generate_calibration

Extract calibration patterns from resolved assessments in a domain and write high-salience feedback memory. Requires at least 3 resolved assessments.

Instructions

Extract calibration patterns from resolved assessments in a domain and write a high-salience feedback memory. Run this after a batch of resolutions in a domain. Requires at least 3 resolved assessments in the domain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to generate calibration for
Behavior3/5

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

With no annotations, the description reveals it writes a 'high-salience feedback memory' (mutation) and requires 3 assessments, but lacks details on side effects, idempotency, or permissions.

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 concise sentences front-loaded with the primary action, no unnecessary words.

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?

Given the simple input schema and no output schema, the description covers the main prerequisites and timing, though it could elaborate on what the feedback memory contains.

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?

Schema coverage is 100% with the only parameter ('domain') fully enumerated and described. The description adds no extra meaning beyond the schema, so baseline 3 applies.

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 uses specific verbs ('extract', 'write') and a clear resource ('calibration patterns', 'feedback memory'), and distinguishes from siblings by focusing on post-resolution analysis in a domain.

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

Usage Guidelines4/5

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

The description explicitly states when to run ('after a batch of resolutions') and a prerequisite ('at least 3 resolved assessments'), but does not mention when not to use it or alternatives.

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/allenc84/sapience'

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