nexusfeed-mcp

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation5/5
Each tool targets a distinct domain and action: ABC tools handle license verification (list states, search by business, lookup by license number), while LTL tools cover carrier information (list carriers, fuel surcharge, accessorials). Clear descriptions prevent confusion.
Naming Consistency5/5
All tool names follow a consistent 'domain_verb_noun' pattern in snake_case (abc_list_states, ltl_get_fuel_surcharge). The naming is predictable and uniform across both domains.
Tool Count4/5
Six tools is a reasonable count for the two domains. However, one tool (ltl_get_accessorials) is non-functional (returns 'coming soon'), slightly reducing its value. Still, the count is appropriate for the scope.
Completeness3/5
The ABC domain is well-covered with list, search, and lookup operations. The LTL domain has a gap: accessorial fees are not yet available. Additionally, abc_search_licenses notes that some filters are not applied server-side, limiting its completeness.
Average 4.2/5 across 6 of 6 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility. It discloses that the tool is not yet available and will return an unavailability message, which is important behavioral context. No other traits like auth or side effects are mentioned, but the core constraint is clearly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with two major sentences plus the coming-soon tag. It front-loads the purpose. While concise, the '[COMING SOON]' marker could be placed after the description for better flow. Overall, no wasted words.
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?
For a not-yet-available tool with an output schema, the description adequately explains its intended function, current unavailability, and directs to a sibling alternative. It lacks parameter details and output content hints, but given the 'coming soon' status, the context is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description provides no explanation of the two parameters (carriers and fee_types). It does not clarify allowed values, formats, or how they influence the output, leaving the agent without meaningful guidance beyond the schema's type information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the current accessorial fee schedule for LTL carriers, listing examples like liftgate and residential delivery. It distinguishes from sibling tool ltl_get_fuel_surcharge. However, the '[COMING SOON]' prefix may temporarily confuse about availability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises 'Use ltl_get_fuel_surcharge for current carrier data', providing a specific alternative. It implies this tool is for accessorials but does not explicitly state when to use it over other tools or that it is currently unavailable for actual queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It thoroughly discloses caching (<500ms), weekly extraction, data source (tariff pages), carrier coverage, and verifiability block, with no contradictions to annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is information-dense and front-loaded with core purpose, but a bit lengthy and includes future sprint plans which could be separated. Nonetheless, every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite thorough behavior description, the complete absence of parameter explanations creates a significant gap in completeness, especially with 0% schema coverage and no annotations to fall back on.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no parameter descriptions in schema). Description fails to explain any of the three parameters (carriers, weeks, include_doe_price), leaving the agent without necessary usage details.
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?
Description clearly states the tool returns LTL fuel surcharge percentages with DOE diesel price, and explicitly distinguishes from browsing carrier websites, which is a specific verb+resource with sibling differentiation.
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?
Explicitly states when to use (instead of browsing unreliable carrier websites) and mentions caching and carrier coverage scope, though lacks explicit when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It describes the return content adequately but does not disclose any side effects, authentication needs, or rate limits. For a stateless, read-only list tool, this is acceptable but not exemplary.
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 concise sentences: first states action and content, second provides usage guidance. No extraneous words, front-loaded with 'Returns metadata.' Highly efficient.
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?
For a simple, parameterless tool with an output schema, the description covers the main purpose and usage context. It lists example output fields and advises initial use. However, it could mention if the output contains all fields from the schema or if authentication is required, but overall 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?
The tool has zero parameters, so no parameter information is needed. The schema coverage is 100% trivially. The description adds value by detailing the output fields, which is beyond the schema's scope, earning a high score.
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 it returns metadata for all supported US states, listing specific fields (agency name, SLA, extraction method, CAPTCHA). The phrase 'Use this first' distinguishes it from siblings like abc_lookup_license, establishing its role as a preliminary overview tool.
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 advises using this tool first when building a multi-state workflow to understand coverage. It gives a clear context of use but does not elaborate on when not to use it or provide alternatives beyond the implied sibling relationship.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It accurately describes the tool as returning metadata with no side effects, which is sufficient for a read-only list operation. No contradictions or omissions noted.
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 concise sentences, front-loaded with the primary action and resource, no redundant information.
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?
The description adequately covers the tool's purpose, output content, and usage context given its simplicity (no parameters, has output schema). It mentions specific fields and provides a use case, making it complete for an agent.
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?
There are no parameters (0 params = baseline 4). The description adds value by detailing what the output contains, though it doesn't need to explain parameters since none exist.
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 it returns metadata for all LTL carriers, listing specific fields (SCAC code, data products, etc.), which directly answers what the tool does and distinguishes it from siblings like ltl_get_accessorials.
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 advises to use this tool 'to discover coverage before building a carrier comparison workflow,' providing clear context for when to use it, though it does not specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the transparency burden. It discloses that city/county/zip/license_status filters are accepted but not applied server-side, state-specific API variations, and the need to check the verifiability block for compliance. It also notes the output includes license type, status, expiration, and suspension history.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that efficiently front-loads the main purpose and then provides use cases, state notes, and caveats. Every sentence adds value, though it could be slightly more structured (e.g., bullet points for parameters) to improve readability.
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?
Given the tool's complexity (multiple states, varying APIs, filter limitations, compliance requirements), the description covers all necessary aspects: operation, usage context, state-specific behaviors, parameter limitations, and output fields. The presence of an output schema reduces the need to document return values, but the description still mentions key output fields.
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?
The schema has 0% description coverage, so the description must add parameter meaning. It explains that city/county/zip/license_status are not applied server-side, and specifies that trade_name, owner_name, address are search criteria. While not describing each parameter exhaustively, it adds valuable context beyond the bare schema titles.
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 searches US state ABC board databases for liquor licenses by business name, owner name, or address. It specifies the resource (licenses), action (searches), and scope (specific states and use cases), which distinguishes it from siblings like abc_lookup_license (likely a direct lookup) and abc_list_states (likely listing supported states).
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 provides concrete use cases ('before approving a distributor order, binding an insurance policy, or onboarding a merchant') and state-specific requirements (e.g., TX requires TWOCAPTCHA_API_KEY, NY only active licenses). However, it does not explicitly tell when to use this tool over siblings like abc_lookup_license, though the context implies it for name/address searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully discloses behavioral details: returns full current record with specific fields, mentions speed advantage, and reveals the _verifiability block for independent verification.
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 efficient sentences, no filler, front-loaded with action and resource, each sentence adds value.
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?
Covers purpose, alternative, use case, and verifiability. Missing error handling or prerequisites, but for a lookup tool with output schema, this is near-complete.
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 has 0% description coverage, but description adds context by referring to 'state-issued license number' and implies state parameter. Could add format examples, but overall adds meaning beyond 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 verb 'looks up' and the resource 'specific liquor license by its state-issued license number', and explicitly contrasts with sibling abc_search_licenses, making it distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use (when license number is known) and not to use (use abc_search_licenses otherwise), plus a concrete use case for point-in-time verification.
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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