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Glama

irrational

by io.github.urbanmorph

analyze_decision

Adversarially audit a decision for cognitive biases. Returns a directive YOUR model executes to produce the composed audit (verdict-first). Provide reasoning, not just the conclusion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoforward = a decision you are about to make; retrospective = reviewing a past decision/outcome.
judgmentYesThe decision/judgment in one line.
languageNoOptional. Natural language for the audit prose (e.g. "Tamil", "Spanish"). Bias ids stay canonical English so the result is still parseable. Defaults to English.
reasoningNoHow you arrived at it (required to audit).
structuredNoOptional. Default false → the audit comes back as readable prose. Set true to get a machine-parseable JSON object (bias ids/keys in English) for pipelines that store or compare audits.

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

No annotations exist; description reveals it returns a directive the model executes, which is a key behavioral trait. However, it does not disclose side effects, safety, or whether it modifies state.

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 with purpose, no redundancy. Every sentence adds value.

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?

Adequately describes purpose and output nature ('directive', 'verdict-first'), but lacks details on output structure and expected behavior in edge cases. No output schema to compensate.

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 covers all parameters (100%). Description adds value on 'reasoning' parameter ('Provide reasoning, not just the conclusion') but does not enrich other parameters beyond schema.

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?

Clearly states it adversarially audits a decision for cognitive biases using specific verb 'audit' and resource 'decision'. Distinguishes from sibling tools get_bias/list_biases which deal with bias definitions.

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?

Implies use when auditing a decision, but no explicit when-to-use, when-not-to-use, or alternatives provided. The instruction 'Provide reasoning, not just the conclusion' gives partial guidance.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool has a distinct purpose: analyzing decisions, retrieving a bias by ID, and listing biases. There is no overlap or ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case (analyze_decision, get_bias, list_biases), making the naming predictable and clear.

Tool Count5/5

With 3 tools, the server is well-scoped for its domain of bias analysis. The count is neither too sparse nor excessive.

Completeness4/5

The tool surface covers the core operations: listing biases, retrieving details, and analyzing decisions. Minor gaps exist (e.g., no tool to search or filter more granularly), but the set is sufficient for the intended use.

Resources