Extract people, places, organizations, and concepts from text to structure information for knowledge graphs and audits.
Extract named entities (people, places, organisations, concepts) from text using Semantica NER.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Input text to extract entities from |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states that it uses Semantica NER and extracts entities. With no annotations provided, the description carries the full burden, but it does not disclose whether the tool is read-only, mutates any stored data, has input size limits, returns a list/JSON, or has any rate/error behavior. 'Extract' implies pure inference, but this is not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the verb, resource, and processing engine with zero wasted words. It communicates the core operation immediately and is structurally clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter extraction tool, the description covers the main purpose, but with no output schema and no annotations, the agent is left guessing about the result format, side effects, and failure conditions. Given the richness of sibling tools (e.g., add_entity), it should at least mention that this is a readonly extraction or that the result is a list of entity mentions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the only parameter, 'text', so the description does not need to add much. The description adds marginal entity-type context but no deeper semantics about text length, language, formatting, or what 'text' should contain beyond the schema's basic explanation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Uses a specific verb and resource ('extract named entities... from text') and lists the entity categories (people, places, organisations, concepts). This clearly distinguishes it from sibling tools like extract_relations, which targets relationships, and add_entity, which likely writes to the graph rather than extracting from text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No mentions of when to use this over alternatives or exclusions. It does not reference sibling tools such as extract_relations, add_entity, or run_reasoning, leaving the agent to infer the appropriate context from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.