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Glama

Tools for Agents

by io.github.max124vip-dev

keyword_extract

Top keywords by frequency

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesInput text
top_kNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changed
    • removedInput schema / properties / args
      Removed value: -{
-  "description": "Tool arguments",
-  "properties": {
-    "text": {
-      "description": "Primary input text",
-      "type": "string"
-    }
-  },
-  "type": "object"
-}
    • addedInput schema / properties / text
      Added value: +{
+  "description": "Input text",
+  "type": "string"
+}
    • addedInput schema / properties / top_k
      Added value: +{
+  "default": 15,
+  "type": "integer"
+}
    • changedInput schema / required
      Previous value: -[]New value: +[
+  "text"
+]
  2. Added

TDQS

C2.1/5.0
Behavior1/5

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

With no annotations, the description carries the full burden. 'Top keywords by frequency' reveals nothing about preprocessing (e.g., stop word removal), output format, case sensitivity, or language handling, leaving the agent without behavioral expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The six-word description is concise but under-specified. It omits essential details about the tool's operation, making it too sparse to be considered well-structured.

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?

Given the simple schema and absence of annotations/output schema, the description should still cover expected output and edge cases. It only indicates result type (keywords by frequency), leaving the tool's behavior largely undefined.

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 coverage is 50%: 'text' has a tautological description and 'top_k' has none. The description adds no parameter context, failing to explain that top_k controls the number of keywords returned or any format requirements.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description conveys the tool returns top keywords sorted by frequency, but lacks a clear verb or resource structure. It doesn't differentiate from sibling extraction tools like extract_emails or entity_extract, making the purpose somewhat implied.

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?

No guidance is provided on when to use this tool vs alternatives like word_stats or extract_query. The intended use case is implicit from the name and description, with no exclusions or context.

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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TDQS

C2/5.0
Disambiguation1/5

Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.

Naming Consistency2/5

Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.

Tool Count1/5

With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.

Completeness3/5

The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.

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