Lexicon
Server Details
Lexicon Oracle is a deep-knowledge engine that analyzes population data and professional behaviors unavailable in standard LLM training. Beyond raw demographics, it specializes in predictive modeling for newer generations (Gen Z/Alpha), identifying emerging cultural trends, and forecasting the success probability of new business ventures based on behavioral market fit. Key Capabilities: Predictive Success: Forecasts business viability and market adoption. Generational Intelligence: Deep-dive ana
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4/5 across 6 of 6 tools scored.
Tools are mostly distinct: compare.methodology applies frameworks to topics, compare.topic searches across lenses, and compare.vs does head-to-head comparisons. The monitor tools are clearly separate. However, an agent might briefly confuse compare.topic and compare.methodology as both involve analysis.
Names follow a pattern like lexicon.category.action, but 'lexicon.feed' lacks a subcategory, and the actions are nouns (methodology, topic, vs) rather than verbs. The mixture of 'vs' as an abbreviation and missing second-level category creates inconsistency.
Six tools is well-scoped for the intelligence and monitoring domain. Each tool serves a clear purpose without redundancy, and the count feels neither too sparse nor too heavy.
The surface covers comparative analysis, framework-based analysis, topic intelligence, monitoring of outages and refunds, and a feed. Minor gaps exist, such as lack of alert creation or additional monitoring types, but core workflows are covered.
Available Tools
6 toolslexicon.compare.methodologyAInspect
Analyse any topic through a structured analytical framework (SWOT, PESTLE, Porter's Five Forces, BCG Matrix, McKinsey 7S, Jobs-to-be-Done, or Blue Ocean Strategy). Returns framework-structured evidence and analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The subject to analyse (e.g. 'Tesla supply chain') | |
| methodology | Yes | The analytical framework to apply. |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety cues (readOnlyHint=false, destructiveHint=false, openWorldHint=true). The description adds that it returns 'framework-structured evidence and analysis' but doesn't elaborate on behavior beyond that. No contradiction with annotations.
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 redundancy, front-loaded with the core action and frameworks. Every word earns its place.
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 an output schema exists, the description covers the input parameters and return type ('framework-structured evidence and analysis') sufficiently. A bit more detail on what the output includes could make it more 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?
Schema coverage is 100% with clear descriptions for both parameters. The description adds that the topic is 'any subject' and lists the frameworks, but this adds little beyond the enum values already in the schema.
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 verb 'Analyse' and the resource 'topic through a structured analytical framework'. It lists specific frameworks, differentiating it from sibling tools like 'lexicon.compare.topic' which likely lacks a structured approach.
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 structured analysis but does not explicitly state when to use this tool versus alternatives like 'lexicon.compare.topic' or 'lexicon.compare.vs'. No exclusion criteria or context cues are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lexicon.compare.topicAInspect
Topic-specific intelligence search across five lenses: comparing decisions, planning, strategy, competitor insights, or buyer insights. Data sourced by category (government, economic, business, education, blockchain, or scholar).
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The subject of the analysis. | |
| category | Yes | Data category to pull evidence from. | |
| decision_a | No | First decision option (required for 'comparing-decisions' topic type). | |
| decision_b | No | Second decision option (required for 'comparing-decisions' topic type). | |
| topic_type | Yes | The type of analysis to perform. |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and openWorldHint=true. The description does not add behavioral context such as potential side effects, external calls, or mutation behavior beyond calling it a 'search', which understates the open-world nature.
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 concise sentences, front-loaded with the core purpose and details. No unnecessary words; every sentence contributes.
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 presence of an output schema (not shown but exists) and full schema parameter descriptions, the description adequately covers the tool's scope. It mentions the lens types and data categories. Minor gap: does not highlight conditional parameter requirements or differentiate from sibling compare tools further, 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 description coverage is 100%, so the baseline is 3. The description paraphrases the enum values but adds no new semantic detail beyond the schema descriptions. It provides a high-level context but does not explain conditional requirements like the need for decision_a and decision_b when topic_type is 'comparing-decisions'.
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's a topic-specific intelligence search across five lenses and data categories, using specific verb 'search' and enumerating the lens types and data categories. It distinguishes from siblings like 'compare.methodology' and 'compare.vs' by specifying the lens 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 context (analyzing a topic with one of the five lens types and data categories) but does not explicitly state when not to use this tool nor provide guidance on alternatives like the sibling compare tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lexicon.compare.vsAInspect
Head-to-head comparison of two entities (companies, products, policies, people, etc.) across a chosen dimension. Returns a structured comparative analysis with evidence from 14 live sources.
| Name | Required | Description | Default |
|---|---|---|---|
| party_a | Yes | First entity to compare (e.g. 'Apple Inc') | |
| party_b | Yes | Second entity to compare (e.g. 'Microsoft') | |
| dimension | No | Analytical lens for the comparison. Defaults to 'market-position' if omitted. | |
| party_a_url | No | Optional URL of a primary source document for party A. | |
| party_b_url | No | Optional URL of a primary source document for party B. |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate openWorldHint=true and readOnlyHint=false. The description adds beyond annotations by stating the output is a structured comparative analysis with evidence from 14 live sources, which is useful behavioral context. No contradictions.
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 two sentences, front-loading the main purpose and succinctly adding output characteristics. Every sentence is meaningful with 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 schema coverage, annotations, and output schema existence, the description provides adequate context. It mentions live sources and return structure. Minor gap: could clarify how openWorldHint affects result consistency, but not critical.
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%, so each parameter has a description. The tool description adds minimal extra semantics beyond reiterating the comparison dimension. It does not elaborate on party_a/b usage beyond schema. 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?
The description clearly states the tool performs head-to-head comparisons of two entities across a chosen dimension, with examples of entity types and dimension from the schema. However, it does not explicitly differentiate 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 description implies usage for comparing two specific entities but provides no explicit guidance on when to use this tool versus alternatives (e.g., other compare tools). No when-not-to-use 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.
lexicon.feedARead-onlyIdempotentInspect
Returns all Lexicon-generated intelligence as structured JSON — every VS comparison and methodology analysis ever run, with full evidence citations. Paginate with page and limit. Filter with query.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number (default: 1). | |
| limit | No | Results per page, max 200 (default: 50). | |
| query | No | Optional search filter (e.g. 'Salesforce' filters to comparisons involving Salesforce). |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds that the output includes 'full evidence citations' and is structured JSON, providing useful context beyond the annotations without contradiction.
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 are concise and front-loaded: first states purpose and output, second explains usage. 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 has only 3 optional parameters and an output schema exists, the description covers purpose, output format, and parameter usage adequately. No gaps for a read-only list 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?
Schema coverage is 100% with descriptions, so baseline is 3. The description adds value by mentioning pagination and filtering, reinforcing the parameter usage beyond the schema.
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 'returns all Lexicon-generated intelligence as structured JSON' and specifies it includes 'every VS comparison and methodology analysis ever run', which distinguishes it from sibling tools that focus on specific comparisons or monitors.
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 explains pagination with 'page and limit' and filtering with 'query', but does not explicitly state when to use this tool versus alternatives like lexicon.compare.*. However, the sibling tool names imply this is the comprehensive feed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lexicon.monitor.outageARead-onlyIdempotentInspect
Detects live infrastructure outages for a vendor or query. Returns outage status, financial impact, SLA breach risk, monetary loss estimate, refund eligibility, and hidden dependency maps.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Vendor name or outage query (e.g. 'AWS us-east-1', 'Stripe', 'Salesforce CRM'). |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate safe, idempotent, non-destructive behavior. The description adds specifics about return data (outage status, financial impact, etc.), but no further behavioral details beyond annotations.
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: first states purpose, second lists outputs. Efficient, front-loaded, and 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?
With a single parameter, rich annotations, and an output schema, the description sufficiently covers what the tool does and returns. No gaps remain.
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 a well-described parameter. The description does not add extra meaning beyond the schema; it restates 'vendor or query'.
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 detects live infrastructure outages, using a specific verb and resource. The sibling tools are distinct (compare, feed, refunds), so the purpose is well-differentiated.
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 outage detection but does not explicitly state when to use it versus alternatives or when not to use it. Context from sibling names clarifies, but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lexicon.monitor.refundsARead-onlyIdempotentInspect
Searches SEC EDGAR filings (8-K, 10-K, 10-Q) and live web sources to surface refund rates, refund amounts, SLA violation records, service credit terms, and financial disclosures for any vendor.
| Name | Required | Description | Default |
|---|---|---|---|
| vendor | No | Vendor name to look up (e.g. 'Salesforce', 'AWS', 'Microsoft Azure'). | |
| vendors | No | Optional array of {vendor, outage_summary} objects for batch lookup (up to 10). | |
| outage_summary | No | Optional description of the outage or incident context. |
Output Schema
| Name | Required | Description |
|---|---|---|
| content | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructive hint. The description adds behavioral context by specifying the data sources (SEC EDGAR and live web) and the types of information surfaced (refund rates, etc.), enhancing agent understanding beyond annotations.
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 a single concise sentence that front-loads the action (searches) and includes key details. It is not overly verbose, though slightly longer than necessary.
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 presence of an output schema (not shown but indicated), the description adequately covers the input purpose and output scope. It lists specific filing types and data categories, making it complete for a search 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?
Schema has 100% description coverage for all three parameters (vendor, vendors, outage_summary) with clear explanations. The description does not add significant parameter-level meaning beyond the schema, but it provides overall context for how the parameters are used together.
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 explicitly states the tool searches SEC EDGAR filings and live web sources for refund-related data (refund rates, amounts, SLA violations, service credits, financial disclosures). This is specific and clearly distinguishes it from sibling tools like lexicon.monitor.outage (outage monitoring) and lexicon.compare.* (comparison).
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 looking up vendor refund data, but it does not provide explicit guidance on when to use this tool versus alternatives. No exclusions or when-not-to-use cases are mentioned.
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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