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Glama

Tools for Agents

by io.github.max124vip-dev

pdf_split

Extract PDF page range text

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPDF URL
end_pageNoLast page (inclusive)
start_pageNo

Schema Changelog

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

  1. Changed5 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 / end_page
      Added value: +{
+  "description": "Last page (inclusive)",
+  "type": "integer"
+}
    • addedInput schema / properties / start_page
      Added value: +{
+  "default": 1,
+  "type": "integer"
+}
    • addedInput schema / properties / url
      Added value: +{
+  "description": "PDF URL",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[]New value: +[
+  "url"
+]
  2. Added

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description bears the full burden of behavioral disclosure. It only states the core action (extracting text) but does not mention side effects, requirements (e.g., internet access for URL), limitations (e.g., page bounds), or return behavior. This is insufficient for a tool that likely performs file downloads and processing.

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?

The description is a single concise sentence that front-loads the core purpose. There is no redundancy or wasted words, making it easy for an agent to quickly understand the tool's basic function.

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 tool has 3 parameters, no output schema, and no annotations, the description is far too sparse. It does not explain return format, error conditions, or how page ranges are validated. This is a minimal viable description at best; for a tool of this complexity, more context is needed.

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?

Schema coverage is 67%, with descriptions for url and end_page but not start_page. The description's phrase 'page range' adds context connecting start_page and end_page, but it doesn't explain the default behavior of start_page (covered by schema default) or the meaning of 'inclusive' beyond the schema. It partially compensates but adds limited value.

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 'Extract PDF page range text' clearly specifies the action (extract text) and the resource (PDF page range), distinguishing it from generic PDF extraction tools like extract_pdf. However, it doesn't explicitly differentiate from siblings such as pdf_metadata or ocr_url, leaving some ambiguity about the exact output format.

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?

The description provides no guidance on when to use this tool versus alternatives like extract_pdf or pdf_metadata. It does not mention prerequisites, use cases, or exclusions, leaving the agent without context for choosing this tool.

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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