lexicon
Server Details
Comparison intelligence: live evidence from 20 sources, PESTLE/VS/Deep Research frameworks.
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- Healthy
- Last Tested
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- Streamable HTTP
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- nadine302324-commits/lexicon-mcp-server
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- Server Listing
- Lexicon Intelligence
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Tool Definition Quality
Average 4.1/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: methodology frameworks, topic intelligence, head-to-head comparisons, historical feed, outage monitoring, and refund searching. No overlap.
All tool names follow a consistent dot-separated pattern with category prefixes like 'compare.' and 'monitor.', making them predictable and easy to navigate.
6 tools is well-scoped for an intelligence server covering comparisons, monitoring, and historical data, with no unnecessary additions.
Covers the core domain of comparative analysis and monitoring well. Minor gap: lacks a tool for single-entity deep dive, but the set is functional for its stated purpose.
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 indicate non-read-only, non-idempotent, open-world behavior. The description adds no further behavioral context beyond returning structured evidence. With annotations present, this is adequate but adds minimal value.
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 fluff, essential information front-loaded. Example of conciseness.
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 low parameter count, full schema coverage, and presence of output schema, the description provides sufficient context. No obvious gaps.
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. The description repeats the parameter purposes but does not add meaning beyond the schema. Baseline 3 applies.
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 function: 'Analyse any topic through a structured analytical framework' and lists specific frameworks (SWOT, PESTLE, etc.), differentiating it from sibling tools like `lexicon.compare.topic` and `lexicon.compare.vs`.
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 when framework-structured analysis is needed but does not explicitly state when to use or avoid this tool, nor does it mention alternatives. No exclusion criteria 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 provide behavioral hints (readOnlyHint, etc.). The description adds value by explaining the multi-lens nature and data categories, indicating it is a search operation that may involve external data. 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?
The description is two sentences, very concise, and front-loaded with key information. Every sentence adds value 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 an output schema, the description need not detail return values. It covers input parameters and data categories sufficiently. Minor missing details (e.g., error handling) are acceptable.
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 baseline is 3. The description provides an overview of parameters (topic types and categories) and hints about decision_a/decision_b requirement, but does not significantly enhance understanding beyond schema 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 specifies the tool's purpose: topic-specific intelligence search across five lenses (comparing decisions, planning, strategy, competitor insights, buyer insights) and six data categories. It uses a specific verb and resource, distinguishing it from sibling tools.
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 when to use this tool (topic-specific search across specified lenses) but does not explicitly state when not to use it or list alternatives among siblings. It lacks exclusion criteria or comparative guidance.
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 (readOnlyHint: false, destructiveHint: false) indicate mutation is possible but description adds context: returns a structured analysis with evidence from 14 live sources. No contradiction, but lacks detail on side effects or authorization requirements.
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 conveying purpose, input, and output without redundancy. Highly efficient.
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?
Output schema exists, parameters are fully documented, and description covers purpose and key behavioral details (live sources, structured analysis), making it complete for this tool's complexity.
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 all parameters described. The description adds minimal extra meaning beyond confirming the comparison dimension and entity scope, but does not enhance parameter semantics significantly.
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 'Head-to-head comparison of two entities' using a specific verb and resource, and distinguishes from siblings like 'lexicon.compare.methodology' and 'lexicon.compare.topic' by focusing on entities (companies, products, etc.).
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 when to use (comparing two entities) and lists entity types, but does not explicitly state when not to use or name alternative tools. However, the entity focus provides clear context relative to siblings.
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 declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds behavioral context about returning 'structured JSON with full evidence citations', which is not in annotations. No contradictions are present.
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 main purpose and content, followed by parameter usage. No redundant 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?
Given the existence of an output schema and thorough annotations, the description provides sufficient context: it explains the content (comparisons and analyses with citations) and how to paginate/filter. 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?
The input schema has 100% coverage, describing all three parameters (page, limit, query). The description repeats this usage info without adding new meaning, so it meets the baseline but does not exceed it.
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 'returns all Lexicon-generated intelligence as structured JSON', specifying the resource and that it includes 'every VS comparison and methodology analysis ever run'. This effectively distinguishes it from sibling tools like lexicon.compare.vs or lexicon.monitor.outage, which are more specific.
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. It implies this is the general feed tool, but does not explicitly state when to use it versus the more targeted sibling tools. However, the context from sibling names provides enough implicit guidance.
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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context by listing returned data (outage status, financial impact, etc.) and mentions 'hidden dependency maps', which goes beyond the 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 sentence that front-loads the main action 'detects live infrastructure outages' and lists key outputs. It is efficient but could be slightly more structured to improve readability.
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, the description does not need to detail return values, but it still lists them. It is complete for the tool's complexity, though no prerequisites or limitations are mentioned.
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 parameter 'query', which already explains its purpose. The description does not add new semantic 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 'detects live infrastructure outages' for a vendor or query, which is a specific verb+resource combination. It distinguishes itself from sibling tools like lexicon.compare.* and lexicon.monitor.refunds by focusing on outage detection.
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 checking outages but does not provide explicit when-to-use or when-not-to-use guidance. Alternatives among siblings are not mentioned, leaving the agent to infer context.
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 safe read-only and idempotent behavior. The description adds that it searches multiple sources (EDGAR + live web), giving agents understanding of data breadth. 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?
Single sentence covers purpose and scope efficiently with no redundancy. Slightly more structure (e.g., bullet points) could improve readability, but it is appropriately sized.
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 and 100% schema coverage, the description provides sufficient context about the tool's function and data sources. It is comprehensive for a search tool with optional parameters.
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 all parameters described. The description does not add extra meaning beyond what the schema provides, meeting the baseline for self-documenting schemas.
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 uses specific verb 'Searches' and clearly identifies resources (SEC EDGAR filings and live web sources) and target information (refund rates, amounts, etc.), effectively distinguishing it from siblings like lexicon.monitor.outage.
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 states the tool searches refund info for any vendor, implying it should be used for refund-related queries. While it doesn't explicitly list when not to use it or name alternatives, the sibling tools context (e.g., outage monitor) provides contrast.
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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