NES Coherence Signal
Server Details
Ask your AI about any brand's claim-vs-reality coherence. NES (Net Entropy Score) gives a website brand-consistency score (the claim), recent public news (the reality), and the divergence between them. Tools: get_brand_coherence and compare_brands. Directional read from public signal, not a fraud or financial check. Free.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for comparing multiple brands side by side, and one for retrieving coherence data for a single brand. There is no ambiguity.
Both tool names follow a consistent verb_noun pattern in snake_case: 'compare_brands' and 'get_brand_coherence'. This is predictable and clear.
With only two tools, the server feels thin for a domain that could benefit from additional operations like listing scanned brands or triggering a scan. The count is borderline.
The surface covers basic retrieval and comparison, but lacks obvious operations such as scanning a brand or listing available brands. There are notable gaps that could hinder agent workflows.
Available Tools
2 toolscompare_brandsAInspect
Compare the NES coherence signal for several brands side by side (claim, reality, divergence for each). Presents the data; it does NOT recommend one brand over another. The caller decides.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | Brand URLs or domains to compare (up to 8). |
Output Schema
| Name | Required | Description |
|---|---|---|
| brands | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: the tool presents data but does not recommend, which sets user expectations. However, it does not disclose whether the tool is read-only or has any side effects, leaving some ambiguity about its safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences no longer than necessary. It front-loads the primary purpose and adds a crucial behavioral qualifier (no recommendation) in the second sentence. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, clear output schema exists), the description covers the essentials: what it compares, what it outputs, and a notable non-behavior. It is sufficient for an agent to understand the tool's role and select it appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (the only parameter 'urls' has a description in the schema). The tool description adds no additional meaning beyond what the schema already provides; it merely restates the same information about URLs and the up-to-8 limit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('compare'), the resource ('NES coherence signal'), and the specific dimensions compared ('claim, reality, divergence for each'). It also distinguishes from the sibling 'get_brand_coherence' by implying a multi-brand comparison, and explicitly states what the tool does not do (recommend).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: use when you want to compare multiple brands side by side. It also clarifies that it does not recommend, so the caller must decide. However, it does not explicitly state when not to use it or mention the sibling 'get_brand_coherence' as an alternative for single-brand analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_brand_coherenceAInspect
Get the NES claim-vs-reality coherence signal for one brand: its website coherence score (the claim), recent public reality (matched news events), and the divergence between them. High divergence means the brand presents as coherent while its reality is turning negative. Directional opinion from public signal; not a fraud or financial check. Returns 'no read on file' for brands never scanned.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Brand website URL or domain, e.g. https://acme.com | |
| brand | No | Brand name, used if no URL is given. |
Output Schema
| Name | Required | Description |
|---|---|---|
| brand | No | |
| claim | No | What the brand says about itself via its website. |
| reality | No | |
| divergence | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully conveys behavioral traits: it returns coherence scores, divergence, and a 'no read on file' message for unscanned brands. It clarifies the signal is directional opinion and not a fraud check. However, it does not explicitly state side effects or confirm read-only behavior, which is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three sentences) and well-structured. The first sentence states the main purpose, followed by supporting details and a note on edge cases. Every sentence adds necessary context without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two optional parameters and an output schema, the description is complete. It covers the core functionality, usage context, limitations, and the special case of unscanned brands. No additional information is needed for an agent to correctly select and invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters with descriptions. The tool description adds value by explaining that either 'url' or 'brand' can be used individually, clarifying their relationship. This goes beyond the schema descriptions and compensates for the lack of enums or nested objects.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to get the NES claim-vs-reality coherence signal for one brand. It uses a specific verb ('Get') and resource ('coherence signal'), and distinguishes from the sibling 'compare_brands' by focusing on a single brand.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (for one brand) and what it does not do (not a fraud or financial check). It does not explicitly state alternatives but implies that comparing brands would use the sibling tool 'compare_brands'. The guidance is adequate but could be more explicit about when not to use 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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