langextract-mcp

langextract_extract

Read-only

Extract structured entities from literal text and return grounded source spans, using example-guided prompts and optional HTML visualization.

Instructions

Extract structured entities from literal text and return grounded spans.

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": {
      "text": "<text>",
      "prompt_description": "<prompt_description>",
      "examples": "<examples>"
    },
    "name": "langextract_extract"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes
`examples`	Yes
`max_workers`	No
`grounded_only`	No
`visualization`	No
`max_char_buffer`	No
`extraction_passes`	No
`prompt_description`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

C2.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=false, destructiveHint=false, covering the safety profile. The description adds the concept of grounding ('grounded spans'), which maps to the grounded_only flag, but does not disclose that extraction is LLM-driven, explain latency/cost, or define what grounding means.

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?

A single front-loaded sentence with no filler. It is well-structured, though arguably terse for a tool of this complexity; conciseness itself is not the issue so much as the missing content.

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?

For a tool with 8 parameters, 3 required, nested Example/ExampleExtraction structures, and LLM-based extraction semantics, one sentence is inadequate. The role of prompt_description and examples (the mechanism that makes extraction work) is never described, leaving the agent unable to call it correctly without inspecting the raw schema.

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?

Schema description coverage is 0% across 8 parameters, so the description carries the full burden. It only loosely hints at the 'text' input ('literal text') and the 'grounded_only' flag ('grounded spans'); prompt_description, examples, max_workers, max_char_buffer, extraction_passes, and visualization receive no explanation anywhere.

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 states a specific verb (Extract), resource (structured entities from literal text), and output (grounded spans). It clearly distinguishes this core extraction tool from siblings langextract_status and langextract_visualize by function, though it does not name those siblings explicitly.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no mention of prerequisites (e.g. that a prompt_description and examples are required), and no routing to langextract_visualize for rendering results. The agent must infer usage entirely from the schema.

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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