MCP Files
Server Quality Checklist
Latest release: v1.9.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: insert_text handles line-based file editing, os_notification sends OS notifications, and read_symbol extracts symbol blocks from files. There is no overlap in functionality, making tool selection straightforward for an agent.
Naming Consistency4/5The naming follows a consistent verb_noun pattern (insert_text, read_symbol, os_notification), which is predictable and readable. The slight deviation is that os_notification uses an underscore but starts with 'os' as a prefix, which is still coherent with the overall style.
Tool Count3/5With only 3 tools, the server feels thin for a general-purpose 'MCP Files' domain, as it lacks basic file operations like reading, writing, or deleting entire files. However, the tools are specialized and focused, so it's borderline but not severely mismatched.
Completeness2/5The tool set is significantly incomplete for a files-oriented server, missing core operations such as reading file contents, writing files, deleting files, or listing directories. While the provided tools are useful for specific tasks, agents will face gaps in handling common file workflows.
Average 3.8/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 already declare readOnlyHint=true and openWorldHint=false, indicating a safe, non-destructive operation with limited scope. The description adds minimal behavioral context beyond this, mentioning 'native notification systems' but not detailing platform-specific behaviors, permissions needed, or notification persistence. It doesn't contradict annotations, but adds little value.
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 zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately.
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 with full schema coverage, and annotations covering safety, the description is minimally adequate. However, it lacks output information (no output schema) and doesn't explain what happens after sending (e.g., notification display behavior), leaving some gaps for a notification tool.
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 descriptions for both parameters in the schema itself. The description adds no additional meaning about parameters beyond what's in the schema, such as format examples or usage tips for 'title' defaults. Baseline 3 is appropriate when schema does the heavy lifting.
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 OS notifications') and resource ('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 notification functions, so it doesn't fully distinguish 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 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 contexts it's appropriate. There's no mention of prerequisites, limitations, or scenarios where this tool is preferred over other notification methods.
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?
Annotations indicate readOnlyHint=true and openWorldHint=false, confirming this is a safe read operation with limited scope. The description adds value by mentioning streaming with concurrency control for performance and the ability to handle multiple file formats, but it does not disclose details like rate limits, authentication needs, or error handling beyond what annotations provide.
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 front-loaded with the core purpose in the first sentence, followed by supporting details in a second sentence. It avoids unnecessary elaboration, though the second sentence could be slightly more concise by integrating performance notes more tightly.
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 complexity (4 parameters, no output schema) and rich annotations, the description is adequate but lacks details on return values, error cases, or examples of symbol extraction. It covers the what and how but not the full behavioral context needed for optimal agent use.
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 all four parameters. The description does not add specific meaning or usage details beyond the schema, such as explaining wildcard patterns in 'symbols' or performance implications of 'optimize'. Baseline 3 is appropriate as the schema handles the heavy lifting.
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 specific action ('Find and extract symbol block by name from files') and resource ('files'), with explicit mention of supported file formats (TS, JS, GraphQL, CSS, etc.). It distinguishes itself from sibling tools like 'insert_text' and 'os_notification' by focusing on symbol extraction rather than text insertion or OS notifications.
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?
The description provides clear context for when to use this tool—for extracting symbol blocks from various file formats using streaming with concurrency control. However, it does not explicitly state when not to use it or name alternatives, such as using 'insert_text' for adding content instead of reading it.
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 this is a write operation (readOnlyHint: false) and not open-world (openWorldHint: false), which the description aligns with by describing text insertion/replacement. The description adds valuable context about efficiency for large files and the need to combine with 'read_symbol' for symbol editing, going beyond what annotations provide.
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 specific usage scenarios and a tip. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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?
For a mutation tool with no output schema, the description provides good contextual completeness by explaining use cases, efficiency considerations, and integration with sibling tools. It could be slightly improved by mentioning error handling or confirmation of changes, but it covers the essential context given the annotations and schema richness.
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 all parameters. The description does not add any parameter-specific details beyond what's in the schema, such as explaining the relationship between 'from_line' and 'to_line' or providing examples. Baseline 3 is appropriate when schema coverage is complete.
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 specific action ('Insert or replace text') on a specific resource ('at precise line ranges in files'), distinguishing it from sibling tools like 'os_notification' and 'read_symbol' which serve different purposes. It explicitly mentions the target use case for line-number operations.
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 combine it with alternatives ('Combine with read_symbol... to edit any symbol anywhere without knowing its file or line range'), including a specific tip about using 'optimize: false'.
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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