LLM Pulse MCP Serverofficial
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licenseA
qualityB
maintenanceMonitors brand mentions, citations, sentiment, competitor share of voice, and GEO performance across AI search engines.
Last updated
1
MIT
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.
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- Healthy
- Last Tested
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- Streamable HTTP
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Tool Definition Quality
A4.1/5.0
Tool DescriptionsA
Average 4.4/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
Each tool has a clearly distinct purpose: one compares multiple brands side by side, while the other retrieves detailed coherence data for a single brand. No overlap or ambiguity.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern (compare_brands, get_brand_coherence), using snake_case throughout. The naming is predictable and clear.
Tool Count3/5
With only 2 tools, the server feels minimal for the domain of brand coherence signals. Additional tools for scanning new brands or listing available brands would enhance coverage, but the current count is borderline acceptable.
Completeness2/5
The server lacks essential operations: there is no way to scan or add a new brand to the database, nor list which brands are already scanned. Users are limited to querying existing data, leaving clear gaps that hinder full workflow completion.
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | Brand URLs or domains to compare (up to 8). |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| brands | No |
Tool Definition Quality
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must bear the full burden. It accurately describes the non-recommending behavior and the data presentation, but does not mention read-only nature or any other side effects, which is acceptable for a data presentation tool.
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?
Two sentences, concise and front-loaded with the main purpose. No redundant information. Every word contributes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (unseen but present) and the simplicity of input, the description sufficiently covers the tool's purpose and constraints. It could mention the output format briefly, but completeness is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. The description adds value by specifying the limit of up to 8 URLs and clarifying that they are brand URLs or domains, which goes beyond the schema's 'Brand URLs or domains to compare'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares brands side-by-side for NES coherence signal, listing specific outputs (claim, reality, divergence). This distinguishes it from sibling get_brand_coherence, which presumably handles 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.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states it presents data and does not recommend, guiding the agent to use it when only data is needed. It does not explicitly mention when to use the sibling tool, but the contrast is implied.
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.
ParametersJSON Schema
| 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
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| brand | No | |
| claim | No | What the brand says about itself via its website. |
| reality | No | |
| divergence | No |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses return fields, the 'no read on file' case, and its nature as public directional signal. Lacks explicit read-only guarantee but is otherwise transparent.
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?
Three sentences, no redundancy. Front-loaded with main action, each sentence adds value. Very concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for its complexity. With output schema present, description explains key return values and edge case. Parameter coverage 100%, sibling listed. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters. Description adds meaning by stating 'used if no URL is given' for brand parameter, clarifying mutual exclusivity. This goes beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets 'NES claim-vs-reality coherence signal for one brand' with specific components (coherence score, reality, divergence). It distinguishes from 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.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It specifies when to use (for one brand's coherence), notes the 'no read on file' edge case, and clarifies it's directional opinion, not fraud/financial check. No explicit when-not-to-use or alternative beyond sibling name.
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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