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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose targeting specific MongoDB operations with no overlap. The descriptions precisely differentiate between aggregation, counting, creating, deleting, finding, listing collections, and updating documents, making misselection unlikely.
Naming Consistency5/5
All tools follow a consistent 'mongo-verb-document/collection' pattern using kebab-case throughout. This predictable naming scheme (e.g., mongo-create-document, mongo-list-collections) enhances readability and agent usability.
Tool Count5/5
With 7 tools, this server is well-scoped for MongoDB operations, covering essential CRUD and utility functions. Each tool earns its place without redundancy, and the count is typical for a focused database server.
Completeness5/5
The toolset provides complete coverage for MongoDB interactions, including create, read (find/count), update, delete, aggregation, and collection listing. There are no obvious gaps, enabling agents to handle full document lifecycle and database management tasks.
Average 2.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Execute aggregation pipeline' which implies a read operation, but doesn't disclose if it's read-only, has side effects, requires specific permissions, or handles errors. For a tool with no annotations, this leaves critical behavioral traits unspecified.
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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a tool that performs complex database operations, the description is incomplete. It doesn't explain return values, error handling, or performance implications. For a tool with 3 parameters and significant behavioral complexity, this minimal description leaves too many gaps for effective use.
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 description coverage is 100%, so the schema already documents all parameters (database, collection, pipeline). The description adds no additional meaning beyond what's in the schema, such as explaining what an aggregation pipeline is or providing usage examples. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute aggregation pipeline') and resource ('on a MongoDB collection'), making the purpose understandable. However, it doesn't differentiate from sibling tools like mongo-find-documents or mongo-count-documents, which also operate on collections but with different query methods.
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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like mongo-find-documents for simple queries and mongo-count-documents for counting, there's no indication that this tool is for complex data transformations or analytics, leaving the agent to guess based on the name alone.
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?
With no annotations provided, the description carries full burden for behavioral disclosure but provides minimal information. It doesn't mention performance characteristics (e.g., whether this uses MongoDB's countDocuments method vs estimatedDocumentCount), whether it requires specific permissions, or what happens with large collections. The description states what the tool does but not how it behaves.
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 perfectly concise at just 6 words - every word earns its place. It's front-loaded with the core functionality and wastes no space on unnecessary elaboration. This is an excellent example of efficient communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query tool with 3 parameters (including a complex filter object) and no annotations or output schema, the description is insufficient. It doesn't explain what the tool returns (just a number? metadata?), how errors are handled, or provide any context about the filter parameter's capabilities beyond what's in the schema. The description should do more to compensate for the lack of structured metadata.
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 description mentions 'documents in a MongoDB collection' which implies the need for database and collection parameters, but adds no semantic value beyond what the 100% schema coverage already provides. The schema already documents all three parameters clearly, so the description doesn't enhance understanding of what each parameter means or how they interact.
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 verb ('Count') and resource ('documents in a MongoDB collection'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'mongo-find-documents' or 'mongo-aggregate' which could also provide count information through different mechanisms.
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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'mongo-find-documents' (which could count by returning array length) and 'mongo-aggregate' (which could include count operations), there's no indication of when this specialized count tool is preferred or what its performance characteristics might be.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool creates a document but doesn't mention whether this requires specific permissions, how errors are handled (e.g., duplicate keys), what the return value looks like, or any side effects. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly, earning full marks for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a write operation with 3 parameters and no output schema) and lack of annotations, the description is insufficient. It doesn't explain what happens after creation (e.g., returns an ID), error handling, or how it differs from sibling tools, leaving critical gaps for an agent to understand the full context.
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 description adds no parameter-specific information beyond what's already in the schema, which has 100% coverage with clear descriptions for 'database', 'collection', and 'document'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate with additional semantic context.
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 action ('Create a new document') and resource ('in a MongoDB collection'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its siblings like 'mongo-update-document' or 'mongo-delete-document' beyond the basic verb difference, missing explicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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 like 'mongo-update-document' for modifying existing documents or 'mongo-find-documents' for reading. There's no mention of prerequisites, error conditions, or typical use cases, leaving the agent with minimal contextual direction.
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, so the description carries full burden. It states 'Delete documents' which implies a destructive mutation, but doesn't disclose critical behaviors: whether deletion is permanent, if it requires specific permissions, what happens on empty filter matches, or error handling. For a destructive tool with zero annotation coverage, this is a significant gap in safety and operational context.
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, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient. Every word earns its place by conveying essential purpose without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a destructive tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context (e.g., permanence, permissions), usage guidelines, and output expectations. While the schema covers parameters well, the overall context for safe and effective use is insufficient.
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 description coverage is 100%, so the schema fully documents all 4 parameters (database, collection, filter, deleteMany). The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain filter syntax, default behaviors, or provide examples. Baseline 3 is appropriate when the schema does all the heavy lifting.
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 action ('Delete') and resource ('documents from a MongoDB collection'), making the purpose immediately understandable. It distinguishes this as a deletion tool among siblings like create, find, update, and aggregate. However, it doesn't specify whether it deletes one or many documents by default, which could be more precise.
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. It doesn't mention prerequisites like database/collection existence, compare with mongo-update-document for modifications, or warn about irreversible deletion. The agent must infer usage from the tool name and context alone.
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, so the description carries the full burden of behavioral disclosure. It states the action ('Query') but doesn't cover critical aspects like read-only nature (implied but not explicit), potential performance impacts, error handling, or return format. This is inadequate for a tool with database operations.
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, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a database query tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral traits (e.g., read-only, error cases), return values, or how it differs from siblings, leaving significant gaps for an AI agent to operate effectively.
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 description coverage is 100%, so the input schema fully documents all parameters (database, collection, filter, limit). The description adds no additional meaning beyond what the schema provides, such as examples or usage tips for the filter parameter. Baseline 3 is appropriate when the schema handles parameter documentation.
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 'Query documents from a MongoDB collection' clearly states the verb ('Query') and resource ('documents from a MongoDB collection'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like mongo-aggregate or mongo-count-documents, which also involve querying operations, so it misses full sibling distinction.
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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like mongo-aggregate for complex queries and mongo-count-documents for counting, there's no mention of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action is an update but doesn't cover critical aspects like whether it's destructive, what permissions are needed, how errors are handled, or the response format. This is a significant gap for a mutation tool with complex parameters.
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, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and wastes no words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a MongoDB update operation with 5 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, error handling, return values, and how to use parameters effectively, making it incomplete for safe and accurate tool invocation.
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 description doesn't add any parameter-specific information beyond what's already in the schema, which has 100% coverage. It mentions 'documents' and 'collection' but doesn't explain the semantics of filter, update, or updateMany. The baseline score of 3 reflects adequate but minimal value added over the schema.
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 action ('Update') and resource ('documents in a MongoDB collection'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like mongo-create-document or mongo-delete-document beyond the verb 'Update', which is why it doesn't reach a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when to choose update over create/delete, or how it relates to siblings like mongo-aggregate or mongo-find-documents, leaving the agent to infer usage context.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states a read operation ('List'), implying it's non-destructive, but doesn't cover aspects like permissions needed, rate limits, error conditions, or what the output looks like (e.g., format, pagination). This leaves significant gaps for a tool that interacts with a database.
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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, and output, which could be important for database operations. This aligns with a score of 3, indicating clear gaps in completeness despite the simple structure.
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, with the 'database' parameter clearly documented. The description adds no additional meaning beyond the schema, such as examples or constraints (e.g., database must exist). This meets the baseline score of 3, as the schema adequately covers parameter semantics without extra value from the description.
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 action ('List all collections') and resource ('in a MongoDB database'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'mongo-find-documents' or 'mongo-aggregate', which operate on documents rather than collections, leaving some room for improvement in sibling distinction.
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?
The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites (e.g., database existence), exclusions, or comparisons to siblings like 'mongo-find-documents' for document-level operations, leaving the agent to infer usage based on tool names alone.
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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