Skip to main content
Glama

Make a decision

decide

Make a deterministic, auditable decision in a domain.

The verdict comes from the domain's rule set (Rete engine, never the LLM), so it is reproducible and compliant. The explanation is generated from the domain's ingested policy documents.

Args: domain: Rule-set domain, e.g. "loan", "fraud", "clinical". query: Natural-language question or decision request. facts: Structured facts for working memory, e.g. {"credit_score": 710, "annual_income": 85000}. Use list_rules to see which fields a domain's rules test. unstructured_text: Optional free text (an application, a case note); facts are extracted from it automatically and merged. response_mode: "verdict_only" (fastest), "verdict_with_explanation", or "full_audit" (every rule evaluation + retrieved chunks, available on every plan including the free tier). rule_firings come back in causal order: a rule that matched a fact asserted by an earlier firing appears after it, with the derived facts listed under asserted_facts. filter_retrieval_with_rules: Pattern 01 — run the rules first and let a fired rule's retrieval_scope action narrow which documents the retrieval searches before it runs. extract_from_retrieval: Pattern 02 — parse the retrieved documents into facts and assert them into working memory, so rules fire on what was actually read (not just the facts you passed).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
factsNo
queryYes
domainYes
response_modeNoverdict_with_explanation
unstructured_textNo
extract_from_retrievalNo
filter_retrieval_with_rulesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the sparse annotations, the description discloses key behavioral traits: determinism, auditable rule-set execution (never LLM), explanation generation from policy documents, causal ordering of rule firings, and two documented retrieval patterns. It also mentions 'available on every plan including the free tier,' providing useful context not in annotations.

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

Conciseness4/5

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

The description is long but well-structured with Args and Pattern sections. For a tool with 7 parameters and multiple behavioral nuances, each section earns its place. It could be tightened slightly (e.g., condensing the response_mode explanation), but it is not wasteful.

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

Completeness5/5

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

Given the tool's complexity and the presence of an output schema, the description covers all necessary areas: purpose, rule-based execution, response modes, rule firing order, retrieval patterns, and cross-references to sibling tools for rule inspection. No critical gaps remain.

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?

Schema description coverage is 0%, so the description carries the full burden. It thoroughly explains all seven parameters, including examples for facts (e.g., {'credit_score': 710}, the meaning of response_mode enum values, and the two boolean patterns. This adds far more meaning than the bare 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?

The description begins with a specific verb+resource: 'Make a deterministic, auditable decision in a domain.' It clearly distinguishes the tool from sibling rule-management tools (e.g., list_rules, put_rules) by focusing on the decision execution itself, and adds the unique context of the Rete engine.

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 provides clear context for when to use the tool ('Make a decision') and even directs users to list_rules to inspect which fields a domain tests. It does not explicitly state when-not-to-use or name alternative tools for other tasks, missing the 'exclusions' element, but the intended usage is unambiguous.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.3/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: decide executes decisions, rule management tools (get_rule_source, list_rules, put_rules) each address different aspects of rule viewing/editing, import_policy_rules handles policy-to-rule drafting, and document tools handle knowledge base content. No two tools are easily confused.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, e.g., list_rules, get_usage, put_rules, import_policy_rules. The single-verb 'decide' fits naturally as the core action. There is no mixing of styles or unpredictable naming.

Tool Count5/5

With 8 tools, the server is well-scoped for its purpose of rule-based decisioning with RAG support. Each tool represents a distinct capability in the workflow—policy ingestion, rule authoring, knowledge base management, decision execution, and usage monitoring—without excess.

Completeness4/5

The tool set covers the core lifecycle: ingest policy text, import draft rules, publish rules, list rules/source, and execute decisions. Minor gaps exist such as no explicit delete for documents or rules (though put_rules with empty YAML can effectively clear rules), and domain listing is indirect via list_rules. These are workable omissions.