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Glama

narrative-engine

by io.github.jdhart81

translate_narrative

Translate raw ecological/agent data into a decision-maker-ready narrative.

audience_type: board_member | general_public | grant_funder |
    institutional_investor | journalist | policymaker | regulator |
    retail_investor | scientist
format_type: academic_paper | executive_summary | grant_proposal |
    investor_deck | newsletter | policy_brief | press_release

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
request_idNo
format_typeYes
key_messageNo
payment_refNo
agent_outputYes
audience_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / payment_ref
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Payment Ref"
+}
    • addedInput schema / properties / request_id
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Request Id"
+}
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

No annotations (readOnlyHint, destructiveHint, etc.) are provided, so the description carries full burden. It does not disclose behavioral traits like side effects (e.g., database writes), authentication needs, rate limits, or error behaviors. The tool could be a pure transformation, but this is not explicitly stated, leaving ambiguity.

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 short (two lines plus a list) and gets to the point. However, the list is inline with newlines, which could be better structured (e.g., using bullet points). There is no unnecessary content, so it earns a high score for conciseness.

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

Completeness2/5

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

The tool has 6 parameters, a nested object, and an output schema (not shown). The description explains the purpose and gives parameter hints for two parameters, but fails to mention that 'agent_output' is required or what it should contain. It also doesn't describe return values, though output schema exists. Given the complexity, the description is incomplete.

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

Parameters2/5

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

Input schema has 0% description coverage, so the description must compensate. It adds meaning for 'audience_type' and 'format_type' by listing possible values, but does not explain 'agent_output' (an object with no defined structure), 'key_message', 'request_id', or 'payment_ref'. The description covers only two out of six parameters, which is insufficient for a tool with many parameters.

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

Purpose4/5

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

The description clearly states the tool's verb ('Translate') and resource ('raw ecological/agent data') and specifies the output ('decision-maker-ready narrative'). It also lists audience and format types, making the purpose unambiguous. However, it does not differentiate from the sibling tool 'describe_agent', so it loses some clarity.

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?

The description provides context on when to use the tool by listing audience types and formats, implying use cases like generating a press release or policy brief. However, it does not explicitly state when not to use this tool or mention alternatives, such as when to use 'describe_agent' instead. Guidance is implicit but not thorough.

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

All narrative-engine toolsInspect
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TDQS

B3.3/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one is for self-description of the agent, the other translates ecological data into narratives for different audiences and formats. No overlap.

Naming Consistency5/5

Both tools use a consistent verb_noun pattern (describe_agent, translate_narrative), which is clear and predictable.

Tool Count3/5

With only 2 tools, the server feels thin for a 'narrative engine' domain. While not critically underpopulated, it borders on insufficient for typical multi-step workflows.

Completeness2/5

The tool surface is severely limited: only self-description and a single translation operation. Missing are tools for creating narratives from scratch, editing, comparing, or managing templates, making the set incomplete for the stated domain.

Resources