Extract ranked keywords and named entities (people, orgs, places, products) from text as clean JSON arrays — for tagging, indexing, and enrichment pipelines. $0.004/call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze (truncated to 8,000 characters) | |
| max_keywords | No | Maximum keywords to return |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the output format ('clean JSON arrays') and pricing, but omits important behavioral details like the 8,000-character truncation limit, rate limits, or auth requirements. This leaves gaps in transparency.
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?
The description is a single sentence that front-loads the main action, includes pricing, and uses no filler. Every word contributes to conveying purpose and value.
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?
The description covers purpose, output format, use cases, and pricing. The schema covers the parameters. While it doesn't mention the 8,000-character truncation limit, that is available in the schema. The lack of an output schema makes the 'clean JSON arrays' statement somewhat vague, but overall it is adequately complete for a simple extraction tool.
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?
Schema coverage is 100%, so the baseline is 3. The description adds context about ranked output and entity types (people, orgs, places, products), but it does not specifically explain the parameters beyond what the schema already provides. It enhances understanding of the output, but not the parameters themselves.
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?
The description clearly states the tool extracts ranked keywords and named entities from text, using a specific verb and resource. It distinguishes itself from sibling tools like classify_text or summarize_text by focusing on extraction and entity recognition.
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?
The description provides explicit use cases ('for tagging, indexing, and enrichment pipelines'), giving clear context for when to use it. However, it does not mention alternatives or exclusions, so it stops short of a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.