Live comparison intelligence for autonomous agents — evidence from 20 independent sources, PESTLE Triangulation, Head-to-Head VS, and Deep Research frameworks.
Live hosted endpoint: https://dbssearch.today/mcp/v1
Tool | Description |
| Head-to-Head VS comparison between two vendors/products |
| PESTLE Triangulation deep research on a single vendor |
| Topic-specific competitive intelligence |
| Live outage and reliability monitoring |
| Refund rate and customer satisfaction signals |
| Competitive intelligence feed for a market/industry |
Related MCP server: lorg-mcp-server
{
"mcpServers": {
"lexicon": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://dbssearch.today/mcp/v1"],
"env": {}
}
}
}Or run locally with Docker:
docker run -e LEXICON_API_KEY=your_key ghcr.io/nadine302324-commits/lexicon-mcp-servergit clone https://github.com/nadine302324-commits/lexicon-mcp-server
cd lexicon-mcp-server
npm install
LEXICON_API_KEY=your_key node index.js Get a free API key at dbssearch.today. Set LEXICON_API_KEY env var, or pass apiKey in Smithery config.
The hosted endpoint at https://dbssearch.today/mcp/v1 is freely testable without a key (rate-limited).
Open agent marketplace with free test endpoint:
POST https://dbssearch.today/marketplace/v1/test
Agent card: https://dbssearch.today/.well-known/agent.json
MIT
Deep PESTLE Triangulation research on a single vendor. Returns structured Political, Economic, Social, Technical, Legal, Environmental analysis with live source citations.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor or company name | |
| industry | No | Industry context | |
| focus | No | Optional focus area within PESTLE |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It states returns analysis with citations, implying read-only operation, but does not explicitly confirm safety, authentication needs, or other 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no wasted words. Efficient and clear.
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?
For a research tool with 3 parameters and no output schema, the description adequately explains the output format (structured PESTLE with citations) and purpose. Lacks mention of read-only nature but overall sufficient.
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 coverage is 100% with each parameter described. The description adds minimal extra meaning beyond the schema, so baseline 3 is appropriate.
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?
Description clearly states it performs 'Deep PESTLE Triangulation research on a single vendor' and returns structured analysis, distinguishing it from sibling tools like lexicon_compare_topic and lexicon_compare_vs which likely compare multiple vendors or topics.
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 use for single-vendor deep dives but provides no explicit guidance on when to use versus alternatives, no exclusions, and no mention of prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Topic-specific competitive intelligence. Ask any comparison question across vendors, markets, or features.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The comparison topic or question | |
| industry | No | Industry context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as output format, data sources, or any constraints. The vague phrase 'Ask any comparison question' lacks specificity about what the tool does or returns.
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 with only two sentences, no redundancy, and front-loaded with the core purpose. Every word adds value.
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?
The tool has no output schema, and the description fails to explain what the tool returns (e.g., text, data, or analysis). Given its complexity as a 'competitive intelligence' tool, the description is insufficient for an agent to understand the output.
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 coverage is 100% with parameter descriptions. The tool description adds context ('competitive intelligence', 'comparison question') but does not significantly enhance understanding beyond the schema for the 'topic' and 'industry' parameters. Baseline score of 3 is appropriate.
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 provides 'topic-specific competitive intelligence' and allows asking 'any comparison question across vendors, markets, or features.' This distinguishes it from sibling tools like lexicon_compare_methodology and lexicon_compare_vs, which imply more focused comparison types.
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 for broad topic comparisons but does not explicitly state when to use this tool versus alternatives like lexicon_compare_vs or lexicon_compare_methodology. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Head-to-Head VS comparison between two vendors or products. Retrieves live evidence from 20 independent web sources and applies PESTLE Triangulation to produce a structured comparison report.
| Name | Required | Description | Default |
|---|---|---|---|
| vendorA | Yes | First vendor or product name | |
| vendorB | Yes | Second vendor or product name | |
| industry | No | Industry context (e.g. fintech, healthcare, SaaS) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals that the tool retrieves live evidence from 20 sources and applies PESTLE Triangulation, providing some transparency. However, without annotations, it lacks details on potential side effects, required permissions, or output format.
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, consisting of two sentences that effectively front-load the purpose and key behavioral aspects. No superfluous 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 absence of an output schema and the presence of sibling tools, the description is somewhat sparse. It explains the basic behavior but could better prepare the agent for interpreting results or handling errors.
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?
Input schema has 100% description coverage, so the baseline is 3. The description reinforces the schema by referring to 'vendors or products' and 'industry context', but adds no extra semantic nuance beyond what is already in the parameter descriptions.
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 core function: comparing two vendors or products head-to-head. It mentions retrieving live evidence and applying PESTLE Triangulation, making the purpose distinct from sibling tools like lexicon_compare_methodology or lexicon_compare_topic.
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 text implies when to use the tool (for comparing two vendors/products) but does not explicitly state when not to use it or suggest alternatives. No guidance on prerequisites or context limits is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Competitive intelligence feed for a market or industry. Returns recent developments, entrant signals, and trend analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | Market or industry name | |
| limit | No | Maximum items to return (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose whether the tool is read-only, has rate limits, or any side effects. Only states it 'returns' data, implying a safe operation but lacking explicit behavioral details.
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?
Two sentences, succinct and to the point. Efficiently conveys purpose and return type without extraneous information.
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?
Description covers overall purpose and return value type but lacks details on pagination, ordering, or item structure. Given simplicity and no output schema, it's minimally adequate but not fully complete.
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?
Input schema covers both parameters with descriptions (100% coverage). The tool description adds context on the return value (recent developments, etc.) but does not enhance parameter meaning beyond what the schema provides.
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 it returns a competitive intelligence feed with recent developments, entrant signals, and trend analysis. It distinguishes from sibling tools like compare and monitor.
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?
No guidance on when to use this tool instead of alternatives like lexicon_compare or lexicon_monitor. Does not mention context or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live outage and reliability monitoring for a vendor. Returns current status, recent incidents, and reliability signals from independent sources.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor name to check for outages |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions that data comes from independent sources and includes status, incidents, and reliability signals, but does not disclose whether the tool is read-only, any rate limits, or the freshness of data. Adequate but not detailed.
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?
Two sentences, no wasted words. Front-loads 'Live outage and reliability monitoring' and then enumerates returns. Efficient and clear.
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 simplicity (one parameter, no output schema), the description adequately explains its purpose and what it returns. No further detail needed.
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% for the single 'vendor' parameter, so baseline is 3. The description adds no additional meaning beyond the schema's 'Vendor name to check for outages', e.g., no examples or format guidance.
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 monitors live outage and reliability for a vendor, and specifies it returns current status, incidents, and reliability signals. This distinguishes it from sibling tools like lexicon_compare_methodology which focus on comparisons.
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 does not provide any guidance on when to use this tool versus alternatives, or when not to use it. It only states what it does, without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Refund rate and customer satisfaction signal monitoring for a vendor.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | Yes | Vendor name | |
| industry | No | Industry context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral details such as side effects, permissions, or data scoping. For a monitoring tool, it is unclear if it is read-only or requires special access. The description minimally implies passive observation but fails to offer transparency.
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?
Single sentence that is front-loaded with the core action and resource. No wasted words; the sentence is appropriately sized for the tool's simplicity.
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?
No output schema is provided, and the description does not explain what the monitoring outputs look like (e.g., refund rate values, satisfaction signals format). For a monitoring tool, this is a significant omission that hinders an agent from understanding the return value.
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 coverage is 100%, with descriptions for both parameters (vendor and industry). The description restates 'for a vendor' but adds no extra meaning beyond the schema. Baseline 3 is appropriate as the schema already documents the parameters well.
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 monitors refund rate and customer satisfaction signals for a vendor, distinguishing it from sibling tools like lexicon_monitor_outage which monitors outages. The verb 'monitoring' is specific enough, and the resource is well-defined.
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?
No guidance on when to use this tool versus alternatives. It does not specify conditions for use, prerequisites, or cases where another tool like lexicon_feed might be more appropriate. The description lacks explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0lexicon_compare_methodologylexicon_compare_topiclexicon_compare_vslexicon_feedlexicon_monitor_outagelexicon_monitor_refundsScored across 6 tools
Each tool has a clearly distinct purpose: comparisons are split by methodology, topic, and head-to-head; feed is for market intelligence; monitors cover outage and refunds. No overlap or ambiguity.
All tools use the prefix 'lexicon_' followed by a clear verb_noun pattern (compare_methodology, compare_topic, compare_vs, feed, monitor_outage, monitor_refunds). Naming is perfectly consistent.
With 6 tools covering competitive intelligence comparisons, market feed, and monitoring, the count is well-scoped for the domain. Each tool serves a distinct and necessary function.
The tool set covers core competitive intelligence needs: comparisons, feed, and monitoring. Minor gaps exist (e.g., no general search or custom report tool), but the surface is largely complete for typical use cases.
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