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LexiconLocal

LexiconLocal

by alexeymohr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

lexicon_read

Retrieve the complete indexed document or a specific chunk with context, converting a search result into prior knowledge for new tasks.

Instructions

Read an indexed document, or one chunk of it with surrounding context. Use after lexicon_search to expand a promising result: pass that result's path and chunk_ord.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "lexicon_read"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesDocument path exactly as returned by lexicon_search
chunk_ordNoChunk ordinal to centre on. Omit for the whole document.
context_chunksNoChunks of context each side

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses the core behavior: reading a whole document or a centred chunk with surrounding context, and it clarifies that path must come from lexicon_search. It does not explicitly state read-only/no side effects, but the verb 'Read' strongly implies it, and the chunk/context semantics are well described.

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 with no filler. The action and scope appear first, and the follow-up sentence provides the exact usage pattern with the sibling tool. Every clause earns its place.

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?

For a three-parameter tool with an output schema and a single sibling, the description is complete. It explains what the tool does, when to use it, how to connect it to lexicon_search, and how the chunking parameters behave. No critical information is missing for correct invocation.

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 input schema covers 100% of parameters and already explains path provenance, chunk_ord as the centre, the whole-document option, and context_chunks as each-side context. The description adds orchestration context (use after search) but does not significantly add parameter-level meaning beyond what the schema provides, so the baseline of 3 applies.

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 states a specific verb ('Read') and resource ('an indexed document, or one chunk of it with surrounding context'), and clearly distinguishes it from the sibling tool by describing the read operation that follows search. It is immediately obvious that this tool is for expanding an existing search result, not for searching.

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 explicitly states when to use the tool: 'Use after lexicon_search to expand a promising result.' It also tells the agent exactly how to invoke it, by passing the path and chunk_ord from that result, which effectively routes the agent between lexicon_read and lexicon_search.

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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