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geter-andru

Andru Revenue Intelligence

by geter-andru
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

log_revenue_insight

Capture revenue insights, decisions, and metrics to build institutional memory for revenue teams. Automatically versions previous values to track changes over time.

Instructions

Save a revenue insight, decision, metric, or pattern into Andru's memory so it compounds over time. Use after any deal decision, ICP refinement, metric update, or strategic pivot. The system automatically versions previous values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesWhat kind of memory this is.
keyYesUnique identifier for this memory (lowercase_underscored, e.g., "current_arr", "acme_deal_stage").
valueYesThe memory content — a concise, specific fact or insight.
memoryTypeNoepisodic=time-bound fact, semantic=general pattern, procedural=behavioral habit. Default: episodic.
domainNoBusiness domain this memory relates to. Default: general.
confidenceNoHow confident this memory is (0.0-1.0). Default: 1.0 for founder-stated facts.

Implementation Reference

  • src/client.js:36-38 (handler)
    The tool call is proxied to the Andru backend API endpoint '/api/mcp/tools/call'. The server implementation in src/server.js delegates execution to this client method.
    async callTool(name, args) {
  return this.post('/api/mcp/tools/call', { tool: name, arguments: args });
}
  • src/catalog.js:547-563 (schema)
    The schema and definition for 'log_revenue_insight' are stored in the static tool catalog.
    {
  name: 'log_revenue_insight',
  description: 'Save a revenue insight, decision, metric, or pattern into Andru\'s memory so it compounds over time. Use after any deal decision, ICP refinement, metric update, or strategic pivot. The system automatically versions previous values.',
  annotations: WRITE_OP,
  inputSchema: {
    type: 'object',
    properties: {
      category: {
        type: 'string',
        enum: ['metric', 'account', 'priority', 'preference', 'decision', 'deadline'],
        description: 'What kind of memory this is.',
      },
      key: {
        type: 'string',
        description: 'Unique identifier for this memory (lowercase_underscored, e.g., "current_arr", "acme_deal_stage").',
      },
      value: {

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

The description adds valuable behavioral context beyond annotations: it explains that the system 'automatically versions previous values,' which clarifies persistence and versioning behavior. Annotations indicate this is a write operation (readOnlyHint: false) with open-world data (openWorldHint: true) and non-destructive (destructiveHint: false), and the description aligns with this by describing saving/compounding without contradictions.

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?

The description is front-loaded and concise with two sentences that efficiently convey purpose and usage without wasted words. Every sentence adds value: the first defines the tool's function, and the second provides context and behavioral insight.

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 tool's complexity (6 parameters, write operation) and rich annotations, the description is mostly complete but lacks details on output or error handling. It covers purpose, usage, and key behavior (versioning), but without an output schema, it could benefit from mentioning what happens after saving (e.g., confirmation).

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?

The description does not provide additional parameter semantics beyond what the input schema already covers. With 100% schema description coverage, the schema fully documents all parameters, so the baseline score of 3 is appropriate as the description adds no extra parameter details.

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 clearly states the tool's purpose with specific verbs ('Save a revenue insight, decision, metric, or pattern into Andru's memory') and resources ('memory'), and it distinguishes from siblings by specifying this is for saving/compounding insights rather than retrieving them (unlike get_memory_history or get_revenue_memory).

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

Usage Guidelines5/5

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

The description provides explicit usage guidance with 'Use after any deal decision, ICP refinement, metric update, or strategic pivot,' giving clear context for when to invoke this tool. It implicitly distinguishes from retrieval-focused siblings by emphasizing saving/compounding rather than getting data.

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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