NCP - Natural Context Provider
Server Quality Checklist
Latest release: v2.3.0
- Disambiguation5/5
The two tools have completely distinct purposes: 'code' executes TypeScript code with automatic tool discovery, while 'find' searches or lists MCP tools. There is no overlap in functionality, and their descriptions clearly differentiate their roles.
Naming Consistency3/5The tool names are simple and readable ('code' and 'find'), but they do not follow a consistent naming pattern like verb_noun. While both are single words, this is a mixed convention that lacks the predictability of a structured pattern.
Tool Count2/5With only 2 tools, the server feels thin for its purpose of providing natural context and tool discovery. The 'code' tool is broad and complex, but the overall set lacks depth, suggesting an incomplete surface for the intended scope of automating tool usage.
Completeness2/5The server has significant gaps: it focuses on execution and discovery but lacks tools for managing or configuring the context, handling errors beyond retries, or integrating with specific domains like email or messaging directly. This will likely cause agent failures when more comprehensive operations are needed.
Average 4.3/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 3 of 4 community issues answered or closed in the last 6 months
- 19 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 failing
This repository is licensed under Elastic License 2.0.
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.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses vector search behavior, listing mode, and default parameter values (limit, confidence_threshold). Missing error handling or output format details.
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?
Description is a single sentence that front-loads purpose and covers key details. Efficient, but could benefit from bullet points for readability.
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 5 parameters, output schema present, and no required params, description covers main usage modes and parameter effects. Minor gaps: no examples or error scenarios, but sufficient for typical use.
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 coverage is 100%, providing baseline of 3. Description adds value by explaining confidence_threshold meaning (lower=more results) and depth levels (0=names, 1=+descriptions, 2=+parameters), beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Search or list MCP tools' with two distinct modes (with description param vs without). Uses specific verb+resource, but does not explicitly differentiate from sibling tool 'code'.
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?
Explicitly states when to use description param vs not, and mentions pipe separator for multi-query. Does not provide when-not-to-use or alternatives, but context 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?
No annotations are provided, so the description must disclose behavior. It explains code execution, automatic param mapping, and timeouts. However, it omits warning about potential destructive actions from arbitrary code execution, which is a slight gap.
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 headings, bullet points, and examples. While it is relatively long, the complexity of the tool justifies the detail. Some redundancy could be trimmed, but it effectively communicates necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, the description covers execution method, parameter usage, return values (success/failure), timeout, and mentions the sibling tool. With an output schema present, it doesn't need to detail return format, but it still provides useful return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 2 parameters at 100% description coverage. The description adds significant value by explaining how to use the 'code' parameter with ncp.do examples, such as using 'intent' and 'params' objects, and clarifies the timeout defaults and limits.
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 it executes TypeScript code for automatic tool discovery and parameter mapping. It distinguishes itself from the sibling tool 'find' by explaining the primary method (ncp.do) and fallback direct calls, making the purpose unambiguous.
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?
The description explicitly tells when to use ncp.do (primary, embedding-based param matching) versus direct namespace calls (when exact tools known). It provides success/failure behavior and retry guidance, offering comprehensive usage instructions.
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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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.
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