verify_fact
Verify a factual claim.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | fast | |
| claim | Yes |
Tools for Agents
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- 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 of behavioral disclosure, but it offers none. It does not describe how verification is performed, whether external sources are consulted, what output is produced, or any limitations. This is purely a purpose statement.
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 description is extremely brief with only a single short phrase, but this reflects under-specification rather than effective conciseness. It lacks the structured guidance needed to aid tool selection and invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Fact-checking is inherently complex, but the description provides no details about sources, evidence, output format, or failure modes. Combined with the absence of annotations and output schema, the description is wholly inadequate for an agent to predict behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes two parameters (claim and mode) with 0% description coverage, yet the description does not mention either. The agent is left without any explanation of what 'mode' (default 'fast') means or what format 'claim' should take.
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 ('verify') and resource ('factual claim'), clearly indicating the tool's core action. However, it does not differentiate from sibling tools like source_credibility or research_topic, leaving potential ambiguity about the exact 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?
No guidance is given on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description only states the action without providing usage context, leaving the agent to infer when it would be appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
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.