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reading-companion

reading-companion

by aby0
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

extract_context

Extracts latent features from your reading profile to generate a context builder prompt for reflection.

Instructions

Analyze the profile to extract latent features.

Returns the context builder prompt along with the current profile. After analysis, call update_latent_features with the results.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "extract_context"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It does not confirm whether the tool is read-only or has side effects, nor does it explain what 'analysis' entails beyond returning results. This lack of transparency is a significant gap.

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 two sentences, no wasted words. It conveys purpose, outputs, and follow-up action efficiently.

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 absence of an output schema, the description adequately states what is returned (context builder prompt and current profile). However, it could provide more detail about the format of these returns or the nature of 'latent features'. Still, it is sufficient for use.

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

Parameters4/5

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

The input schema has zero parameters with 100% coverage. No parameter descriptions are needed, so the tool meets the baseline expectation for a parameterless tool.

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 action: 'Analyze the profile to extract latent features' and specifies the return values (context builder prompt and current profile). It distinguishes from sibling tools by mentioning a specific follow-up action (update_latent_features).

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 instructs to call update_latent_features after analysis, providing clear workflow guidance. However, it does not state when to use this tool vs alternatives, though the implied sequence is clear given the sibling set.

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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