nexus-mcp-ci

by jaggernaut007

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.0.4
Disambiguation5/5
Each tool has a clearly distinct purpose: health checks, indexing, symbol lookup, code analysis, explanation, project mapping, and memory. No overlap or ambiguity between tools.
Naming Consistency3/5
Tool names mix verb forms (analyze, explain, map) and noun forms (health, memory). 'find_symbol' uses verb_noun while others are single words, showing inconsistency in naming conventions.
Tool Count5/5
7 tools is well-scoped for a code intelligence server, covering indexing, search, analysis, mapping, and context persistence without being overwhelming or insufficient.
Completeness4/5
The tool set covers core workflows like indexing, search, analysis, and mapping. A direct file reading tool is missing, but it is architecturally replaced by explain and map, making the gap minor.
Average 4.1/5 across 7 of 7 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- Last stable release on July 6, 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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavioral traits. While it lists what the tool analyzes, it does not mention that the tool is read-only and has no side effects, nor does it cover rate limits or performance impacts. The description is incomplete in this regard.
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 concise with two sentences, front-loading the purpose. The first sentence is somewhat long but efficient, and the second sentence adds the optional parameter. No redundant information.
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 (not shown), the description covers the main analysis aspects adequately. It mentions the key components (complexity, dependencies, code smells, quality score) and the optional scope. However, it could improve by hinting at output format or interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the single parameter `path`, and the description merely echoes the schema's meaning. No additional semantics or usage details are added beyond what the schema already provides.
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 is for code review and quality assessment, listing specific analyses (complexity, dependencies, code smells, quality score). It distinguishes from siblings by its focus on code analysis, but does not explicitly differentiate from sibling tools like 'health' or 'explain'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'health' or 'explain'. The description only mentions optional scoping via `path`, but lacks context on prerequisites, limitations, or when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as side effects, auth requirements, or rate limits. It only states the tool's purpose without addressing safety or non-obvious behaviors.
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?
The description is extremely concise with two sentences. The first sentence immediately establishes the tool's recommendation, and the second provides clear instructions on the parameter values. 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?
Given the tool has only one parameter and an output schema exists, the description covers the essential purpose and usage. It could be more complete by mentioning that the output is structural, but the output schema likely fills this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, fully describing the 'detail' parameter. The description adds usage context but does not introduce new semantic information beyond the schema, placing it at the baseline for high coverage.
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 is for 'project understanding' and contrasts with alternatives like Glob/ls/manual browsing. It specifies three modes (summary, architecture, full) with distinct purposes, making the tool's functionality unambiguous.
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 recommends when to use this tool over siblings and provides clear guidance on choosing detail levels. However, it does not explicitly mention when not to use it or compare with all sibling tools like 'health' or 'analyze'.
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?
No annotations are provided, so the description must disclose behavioral traits. It reveals that the tool returns definition and call-graph relationships, but does not mention if it has side effects, rate limits, or authorization needs. It implicitly suggests read-only behavior but doesn't state it clearly.
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?
The description is two sentences, front-loads the purpose, and includes a key usage tip. Every sentence is necessary and efficient.
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 has an output schema, the description need not detail return format. It covers the core operation and key parameter variation. Sibling context shows this is a lookup tool among analysis tools, and the description adequately differentiates.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters well-described. The description adds a note about using exact=False for fuzzy matching, which complements the schema but adds limited extra semantic value beyond what the schema already provides.
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 'look up' and the resource 'specific function/class/symbol by name'. It distinguishes from Grep by mentioning it returns definition plus call-graph relationships, making it clear what the tool does uniquely.
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 explicitly says 'preferred over Grep' and provides guidance on setting exact=False for fuzzy matching. However, it does not explicitly state when not to use this tool or how it compares to siblings like analyze or explain.
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?
Describes what the tool combines (call-graph, semantic search, quality metrics) and implies it is a read operation. With no annotations provided, the description carries the full burden and does an adequate job, though it does not discuss performance or error handling.
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, highly efficient, front-loading the purpose and usage. Every word earns its place with no redundancy.
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 existence of an output schema, the description adequately covers behavior, usage, and parameter guidance. It does not explicitly differentiate from siblings like 'analyze' or 'map', but the purpose is clear enough that an agent can decide appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining verbosity usage ('summary' and 'full'), but slightly mismatches the schema's default 'detailed' and misses 'detailed' in the example, causing minor confusion.
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?
Clearly states it explains an unfamiliar symbol by combining call-graph, semantic search, and quality metrics. However, it does not explicitly differentiate from listed sibling tools like 'analyze' or 'map'.
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?
Explicitly states when to use ('onboarding to an unfamiliar symbol') and mentions an alternative action ('so Read is often unnecessary'). Also provides guidance on verbosity levels with concrete examples.
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?
No annotations are provided, so the description carries the full burden. It discloses persistence across sessions, semantic search, and deletion by various criteria. However, it lacks details on side effects (e.g., permanent deletion, rate limits, auth requirements) or behavior in edge cases, which would be helpful for a tool with multiple actions.
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?
The description is extremely concise: two sentences, no filler. The first sentence gives the overall purpose, and the second efficiently covers all three actions with their key parameters. Every word earns its place.
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 tool has 9 parameters and 3 actions, the description effectively covers the primary use cases. An output schema exists (though not shown) so missing details about return values are acceptable. It could mention pagination or all-memories retrieval, but overall it is reasonably complete for a memory tool.
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 description coverage is 100%, providing baseline 3. The description adds value by grouping parameters by action context (e.g., specifying which params apply to store, search, delete), making it easier for an agent to understand which fields are relevant for each action. This goes beyond the raw schema definitions.
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's purpose: 'Persist and retrieve project context across sessions.' It then enumerates three distinct actions (store, search, delete) with specific verbs and resources, making the scope unambiguous and distinguishable from sibling tools which appear unrelated.
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 explicit usage guidance for each action: 'Use action='store' to save a decision/note, action='search' to find memories by semantic similarity, action='delete' to clean up by ID, tags, or type.' While it does not mention when not to use the tool or alternative tools, the sibling tools are sufficiently different so no confusion arises.
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 full burden. It discloses incremental default behavior, live progress reporting, sequential processing for low RAM, and automatic file watching. It could mention error handling or idempotency, but the provided details are good.
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?
Four sentences with no wasted words. The first sentence front-loads the primary purpose. Each sentence provides essential information about usage, behavior, and automation.
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 presence of an output schema (not shown), the description covers key aspects: order of use, multi-path support, progress, incremental nature, and auto-watch. It could mention error scenarios or that the tool is non-destructive, but overall it is sufficient for a setup tool.
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%, so baseline 3. The description adds behavioral context: paths are processed sequentially to keep RAM low. It also reinforces the comma-separated usage. This adds meaningful value beyond the 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 that the tool indexes a codebase and is the first step before using other tools, explicitly distinguishing from status/health. It also mentions supporting comma-separated paths, incremental indexing, and auto-watch.
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 explicitly states 'Use first on any new or changed codebase, before any other tool' and notes that re-running is rarely needed. However, it does not provide exclusions or when not to use, but the context with sibling tools is clear.
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 provided, the description carries full burden. It transparently indicates the tool is read-only and limited to uptime/engine status. However, it could mention behavior like response format or potential latency, but for a simple health probe it is sufficient. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a dash for contrast, conveying all necessary information without waste. Every word earns its place.
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 simplicity (no parameters, output schema exists), the description fully covers its purpose, scope, and usage context. It even references an alternative tool, making it complete for an AI 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?
The tool has zero parameters, and the schema coverage is 100%. According to guidelines, baseline is 4 for 0 params. The description adds no parameter information because none is needed.
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 is for liveness/readiness probes (uptime, which engines are up) and explicitly differentiates from checking index freshness/completeness, which is handled by a sibling tool named `status`. This provides a specific verb+resource+scope and distinguishes from alternatives.
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?
Explicitly states when to use (liveness/readiness probes) and when not to use (checking index freshness or completeness), and directly suggests an alternative tool (`status`). This is exemplary guidance for an AI agent.
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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