MCP Tools
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: insert_text for line-based file editing, os_notification for system alerts, and read_symbol for symbol extraction. There is no overlap in functionality, making tool selection straightforward for an agent.
Naming Consistency5/5All tool names follow a consistent snake_case pattern with a verb_noun structure (insert_text, os_notification, read_symbol). This uniformity enhances readability and predictability across the tool set.
Tool Count3/5With only 3 tools, the set feels thin for a general-purpose 'MCP Tools' server, potentially lacking coverage for common operations like file reading or deletion. However, the tools are well-defined, so it's borderline but not severely mismatched.
Completeness2/5The tool set has significant gaps for a file and system operations domain: there is no tool for reading file contents (only symbol extraction), deleting files, or basic file management. This incompleteness will likely cause agent failures in broader workflows.
Average 3.9/5 across 3 of 3 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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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?
Annotations indicate readOnlyHint=true and openWorldHint=false, suggesting a safe, non-destructive operation with limited scope. The description adds context by specifying 'native notification systems,' implying platform-specific behavior, but doesn't detail aspects like permission requirements, notification duration, or user interaction effects beyond what annotations cover.
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 is a single, efficient sentence with no wasted words, clearly stating the tool's function. It's appropriately sized and front-loaded, making it easy to understand at a glance without unnecessary elaboration.
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 tool's low complexity (2 parameters, no output schema) and annotations covering safety, the description is minimally adequate. However, it lacks details on behavioral outcomes (e.g., how notifications appear or are dismissed) and doesn't compensate for the absence of an output schema, leaving gaps in understanding the tool's full impact.
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 description coverage is 100%, with clear documentation for both parameters ('message' and 'title'). The description doesn't add meaning beyond the schema, such as examples or edge cases, but the schema adequately defines parameters, meeting the baseline for high coverage.
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?
The description clearly states the action ('Send') and target ('OS notifications using native notification systems'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'insert_text' or 'read_symbol', which are unrelated to notifications, so it lacks explicit 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives or in what context it's appropriate. There's no mention of prerequisites, limitations, or scenarios where this tool is preferred over other notification methods, leaving usage entirely implicit.
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 provide readOnlyHint=true and openWorldHint=false, indicating a safe, bounded operation. The description adds valuable context beyond annotations by mentioning streaming with concurrency control for performance, which helps the agent understand execution behavior, though it lacks details on error handling or output format.
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 is front-loaded with the core purpose in the first sentence, followed by supporting details in a second sentence. Both sentences earn their place by clarifying functionality and performance, with no wasted words or redundancy.
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 moderate complexity, annotations cover safety, and schema fully describes inputs, the description is mostly complete. However, the lack of an output schema means the description could better explain return values or error cases, leaving a minor gap in contextual understanding.
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 description coverage is 100%, so the schema fully documents parameters like 'file_paths', 'limit', and 'symbol'. The description adds minimal semantics by noting support for many file formats and performance features, but it does not significantly enhance parameter understanding beyond the schema.
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's purpose with a specific verb ('Find and extract') and resource ('symbol block by name from files'), and distinguishes it from siblings like 'insert_text' and 'os_notification' by focusing on read-only symbol extraction rather than insertion or system notifications. It also specifies the supported file formats, adding precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for extracting symbols from files in various formats, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. No sibling-specific guidance is given, leaving the agent to infer context from tool names alone.
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 indicate readOnlyHint=false and openWorldHint=false, which the description aligns with by describing a write operation ('insert or replace text'). The description adds valuable behavioral context beyond annotations, explaining the tool is designed for line-number operations and large files, and suggesting a complementary tool (read_symbol) for symbol-based editing. No contradictions with annotations exist.
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 is highly concise and well-structured with three sentences that each serve a distinct purpose: stating the core function, providing usage context, and offering a practical tip. There is no wasted text, and information is front-loaded effectively.
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 complexity (file editing with line ranges), the description provides good contextual completeness despite no output schema. It explains the tool's purpose, ideal use cases, and how to combine it with another tool. However, it doesn't detail error conditions or the exact behavior when to_line is omitted, leaving minor gaps.
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?
With 100% schema description coverage, the input schema already fully documents all parameters. The description adds minimal parameter semantics beyond the schema, mentioning 'line ranges' and 'line-number operations' which are implied by the parameter names. It doesn't provide additional syntax or format details, so the baseline score of 3 is appropriate.
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's purpose with specific verbs ('insert or replace text') and resources ('at precise line ranges in files'), distinguishing it from sibling tools like os_notification and read_symbol. It explicitly mentions the target resource (files) and the precise nature of the operation (line-number based editing).
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 provides explicit guidance on when to use this tool ('ideal for direct line-number operations... and large files where context-heavy editing is inefficient') and when to use alternatives ('Combine with read_symbol to edit any symbol anywhere without knowing its file or line range'). It clearly differentiates from sibling tools and offers practical usage scenarios.
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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