tridentchain-mcp on PyPI
Server Quality Checklist
Latest release: v0.1.6
- Disambiguation5/5
Each tool has a clearly distinct purpose: scan_project for project-only dependencies, scan_full for comprehensive coverage including system and IDE extensions, and validate_after_patch for post-patch verification. Descriptions explicitly differentiate use cases.
Naming Consistency5/5All tool names follow a consistent verb_noun snake_case pattern: scan_project, scan_full, validate_after_patch. The naming is predictable and descriptive.
Tool Count4/5With 3 tools, the server is minimal but well-scoped for its vulnerability scanning and validation domain. Each tool earns its place, though the count is on the lower end.
Completeness4/5The server covers the core workflow: scanning project or full system and validating fixes. Minor gaps exist, such as lacking a tool to configure output paths or list past scans, but the primary use cases are handled.
Average 4.3/5 across 3 of 3 tools scored.
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
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, so the description correctly implies a non-destructive scanning operation. The description adds context about output format (JSON + HTML), ranking criteria (EPSS, CISA KEV), and surfaced areas, but does not detail side effects or required permissions beyond what annotations imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with numbered points and a comparison. It is front-loaded with the core purpose. However, it could be slightly more concise by removing redundant phrasing like 'these are a growing attack vector' which adds color but not essential instruction.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (4 parameters, multiple surfaces), the description covers the scanning scope and output format adequately. It lacks parameter details and does not leverage the output schema (though not shown). The comparison with scan_project adds completeness. However, the missing parameter explanations reduce usability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for missing parameter documentation. However, it only mentions project_path implicitly via the surfaces and output_dir in reports. It does not explain the purpose of output_dir, run_profile, or max_findings, leaving the agent unsure how to set them correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a comprehensive scan covering project dependencies, OS packages, and IDE extensions. It explicitly distinguishes from scan_project by noting that scan_full covers three surfaces that project-only scanners cannot reach.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool (e.g., 'complete coverage', 'full audit') and when to use scan_project instead ('fast project-only checks'). Includes specific user request patterns and a warning about slower speed.
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?
Annotations already indicate openWorldHint (external access) and non-destructive. Description adds that it writes HTML reports to output_dir, which is a behavioral side effect. 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, front-loaded with purpose and usage. Efficient, but could be slightly tighter. Length is justified by the added context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage, results (JSON+HTML), and alternatives. Output schema exists, so return values are documented elsewhere. Missing parameter descriptions are a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 4 parameters with 0% description coverage. Description only explains output_dir (reports written there). Other parameters like run_profile and max_findings are not described, so partial compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans project dependencies for CVEs and ranks by EPSS/KEV risk. It distinguishes from sibling scan_full by noting scope difference (npm/PyPI vs OS packages). Specific verb 'scan' and resource 'project dependencies' are present.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit best-for: wanting to fix CVEs first, supply-chain risk, pairing with validate_after_patch. Also recommends scan_full for broader coverage. Provides clear when-to-use and alternatives.
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?
Annotations already declare readOnlyHint, destructiveHint, and openWorldHint. The description adds behavioral details: it compares two inputs and outputs resolved_count, remaining_count, new_count, and validation_passed, with condition for validation_passed. This adds value beyond annotations, but could include more on error behavior or input validation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two sentences, front-loaded with the core purpose, followed by usage guidance and return value explanation. No extraneous information; every sentence is necessary and effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's specific role, the description covers when to use, how to chain with siblings, and what outputs to expect. It is fairly complete but could mention input format expectations (e.g., must be output from scan_project) and handle cases of invalid input.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions are absent (0% coverage). The description compensates by explaining that baseline_json and after_patch_json are scan results from before and after patch, and that they should be JSON strings. This provides essential meaning beyond the schema's bare field definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: confirming that dependency upgrades resolved CVEs. It specifies the action (validate), resource (dependency upgrades/CVEs), and distinguishes from siblings by noting uniqueness to TridentChain and referencing chainable usage with scan_project.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description gives clear when-to-use scenarios (after npm update, pip install -U, or patch application) and how to use it (chain with two scan_project calls or pass saved JSON results). It mentions uniqueness but lacks explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/DevInder1/supply-chain-scanner-public'
If you have feedback or need assistance with the MCP directory API, please join our Discord server