compare_texts
Compare two texts or URLs.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url_a | No | ||
| url_b | No | ||
| text_a | No | ||
| text_b | No |
Tools for Agents
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Changed1 schema field changed
- added
Input schema / requiredAdded value: +[]
- First observed
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It only says 'Compare two texts or URLs' without disclosing whether URLs are fetched, whether it returns a similarity score, a diff, or a boolean, or any other behavioral traits.
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, direct sentence with no filler words. It front-loads the verb and resource, making it highly concise and structurally efficient.
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 there is no output schema and no annotations, the description is too incomplete. It does not mention the return format, clarify parameter pairing, or differentiate from similar sibling tools, leaving the agent with insufficient information to invoke the tool confidently.
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 description coverage is 0%, so the description must compensate for the undocumented parameters. The phrase 'texts or URLs' hints at two groups (text_a/text_b and url_a/url_b) but does not clarify that users must provide both of the same type or explain each parameter's role.
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 clearly states the tool compares two texts or URLs, which identifies the action and resource. It does not explicitly differentiate from siblings like text_diff or text_similarity, but the mention of 'texts or URLs' broadens its scope and provides a distinct identity.
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 offers no guidance on when to use this tool instead of specialized siblings such as text_diff, text_similarity, or diff_url. It does not state scenarios where a generic comparison is preferred or provide any exclusions.
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.