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Glama

Tools for Agents

by io.github.max124vip-dev

robots_parse

Parse robots.txt rules

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesSite URL
timeoutNo

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 / timeout
      Added value: +{
+  "default": 15,
+  "type": "integer"
+}
    • addedInput schema / properties / url
      Added value: +{
+  "description": "Site URL",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[]New value: +[
+  "url"
+]
  2. Added

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it parses rules, with no mention of network requests, timeout behavior, return value shape, or potential side effects.

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?

The description is a single, front-loaded sentence with no filler, which is efficient. However, it is so terse that it sacrifices necessary context, making it concise but not fully appropriately sized.

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?

With no output schema or annotations, the description fails to convey what the parsed output looks like or how errors are handled. For a network-triggered tool with a timeout parameter, this leaves significant gaps.

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%: 'url' has a generic 'Site URL' description and 'timeout' has none. The description adds no additional meaning beyond what the schema already states, leaving timeout semantics entirely unexplained.

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 uses a specific verb ('parse') and resource ('robots.txt rules'), clearly identifying the tool's function. It naturally distinguishes from sibling parsers like sitemap_parse by naming robots.txt explicitly, though it omits any detail about the output format or scope.

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 such as crawl or sitemap_parse. There is no mention of use cases, prerequisites, or exclusion scenarios.

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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